From e41c1bdc4300d5ed6603a11185b326e8d3ae5e5a Mon Sep 17 00:00:00 2001 From: divious1 Date: Tue, 23 Feb 2021 22:41:48 -0500 Subject: [PATCH 01/62] skeleton --- bin/{doc-gen.py => doc_gen.py} | 43 ++++++++++++++-------------------- 1 file changed, 18 insertions(+), 25 deletions(-) rename bin/{doc-gen.py => doc_gen.py} (94%) diff --git a/bin/doc-gen.py b/bin/doc_gen.py similarity index 94% rename from bin/doc-gen.py rename to bin/doc_gen.py index df97861c27..f3e619b0de 100644 --- a/bin/doc-gen.py +++ b/bin/doc_gen.py @@ -661,42 +661,35 @@ def parse_data_models_from_search(search): if __name__ == "__main__": # grab arguments - parser = argparse.ArgumentParser(description="generates documentation from our content", epilog=""" - This tool converts manifests information to documents in variious format like markdown and wiki markup used by Splunk docs.""") + parser = argparse.ArgumentParser(description="Generates documentation from Splunk Security Content", epilog=""" + This tool converts all Splunk Security Content detections, stories, workbooks and spec files into documentation. It builds both wiki markup (Splunk Docs) an markdown documentation.""") parser.add_argument("-p", "--path", required=True, help="path to security_content repo") parser.add_argument("-o", "--output", required=True, help="path to the output directory for the docs") parser.add_argument("-v", "--verbose", required=False, default=False, action='store_true', help="prints verbose output") - parser.add_argument("-gsd", "--gen_splunk_docs", required=False, default=True, action='store_true', - help="generates wiki markup splunk documentation, default to true") - parser.add_argument("-gmd", "--gen_markdown_docs", required=False, default=True, action='store_true', - help="generates markdown docs, default to true") + parser.add_argument("-t", "--type", required=False, default="all", help="type of content to generate documentation for, supports `detections`, `stories`, `spec`, and `all`, defaults to `all`" ) # parse them args = parser.parse_args() REPO_PATH = args.path OUTPUT_DIR = args.output verbose = args.verbose - gsd = args.gen_splunk_docs - gmd = args.gen_markdown_docs + type = args.type - stories = load_objects("stories/*.yml") - detections = [] - detections = load_objects("detections/*/*.yml") - detections.extend(load_objects("detections/*/*/*.yml")) + allowed_types = ['stories', 'detections', 'spec', 'all'] + if type not in allowed_types: + print("ERROR: the type {0} is not support, the current support types are: {1}".format(type,allowed_types)) + parser.print_help() + sys.exit(1) - # complete_stories = generate_stories(REPO_PATH, verbose) - # complete_detections = generate_detections(REPO_PATH, complete_stories) + #if type == 'all': - if gsd: - story_count, path = write_splunk_docs(stories, detections, OUTPUT_DIR) - print("{0} story documents have been successfully written to {1}".format(story_count, path)) - else: - print("--gen_splunk_docs was set to false, not generating splunk documentation") + print("finished successfully!") - if gmd: - story_count, path = write_markdown_docs(stories, detections, OUTPUT_DIR) - print("{0} story documents have been successfully written to {1}".format(story_count, path)) - else: - print("--gen_splunk_docs was set to false, not generating splunk documentation") +# stories = load_objects("stories/*.yml") +# detections = [] +# detections = load_objects("detections/*/*.yml") +# detections.extend(load_objects("detections/*/*/*.yml")) - print("documentation generation for security content completed..") + + #story_count, path = write_splunk_docs(stories, detections, OUTPUT_DIR) + #print("{0} story documents have been successfully written to {1}".format(story_count, path)) From d8d90111a0166440330b147ed492c06da145d72d Mon Sep 17 00:00:00 2001 From: divious1 Date: Thu, 25 Feb 2021 01:10:17 -0500 Subject: [PATCH 02/62] markdown_detections --- bin/doc_gen.py | 673 +- .../doc_detections_markdown.j2 | 121 + docs/detections.md | 16282 ++++++++++++++++ 3 files changed, 16438 insertions(+), 638 deletions(-) create mode 100644 bin/jinja2_templates/doc_detections_markdown.j2 create mode 100644 docs/detections.md diff --git a/bin/doc_gen.py b/bin/doc_gen.py index f3e619b0de..d979f6d03e 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -1,661 +1,54 @@ import glob import yaml import argparse -from os import path +from os import path, walk import sys import re from jinja2 import Environment, FileSystemLoader -def load_objects(file_path): - files = [] - manifest_files = path.join(path.expanduser(REPO_PATH), file_path) - - for file in sorted(glob.glob(manifest_files)): - files.append(load_file(file)) - - return files -def load_file(file_path): - with open(file_path, 'r') as stream: - try: - file = list(yaml.safe_load_all(stream))[0] - except yaml.YAMLError as exc: - print(exc) - sys.exit("ERROR: reading {0}".format(file_path)) - return file +def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): - -def prepare_content(stories, detections): - - # enrich stories with information from detections: data_models, mitre_ids, kill_chain_phases, nists - sto_to_data_models = {} - sto_to_mitre_attack_ids = {} - sto_to_kill_chain_phases = {} - sto_to_ciss = {} - sto_to_nists = {} - sto_to_det = {} - for detection in detections: - if 'analytic_story' in detection['tags']: - for story in detection['tags']['analytic_story']: - if story in sto_to_det.keys(): - sto_to_det[story].add(detection['name']) - else: - sto_to_det[story] = {detection['name']} - - data_model = parse_data_models_from_search(detection['search']) - if data_model: - if story in sto_to_data_models.keys(): - sto_to_data_models[story].add(data_model) - else: - sto_to_data_models[story] = {data_model} - - if 'mitre_attack_id' in detection['tags']: - if story in sto_to_mitre_attack_ids.keys(): - for mitre_attack_id in detection['tags']['mitre_attack_id']: - sto_to_mitre_attack_ids[story].add(mitre_attack_id) - else: - sto_to_mitre_attack_ids[story] = set(detection['tags']['mitre_attack_id']) - - if 'kill_chain_phases' in detection['tags']: - if story in sto_to_kill_chain_phases.keys(): - for kill_chain in detection['tags']['kill_chain_phases']: - sto_to_kill_chain_phases[story].add(kill_chain) - else: - sto_to_kill_chain_phases[story] = set(detection['tags']['kill_chain_phases']) - - if 'cis20' in detection['tags']: - if story in sto_to_ciss.keys(): - for cis in detection['tags']['cis20']: - sto_to_ciss[story].add(cis) - else: - sto_to_ciss[story] = set(detection['tags']['cis20']) - - if 'nist' in detection['tags']: - if story in sto_to_nists.keys(): - for nist in detection['tags']['nist']: - sto_to_nists[story].add(nist) - else: - sto_to_nists[story] = set(detection['tags']['nist']) - - for story in stories: - story['detections'] = sorted(sto_to_det[story['name']]) - if story['name'] in sto_to_data_models: - story['data_models'] = sorted(sto_to_data_models[story['name']]) - if story['name'] in sto_to_mitre_attack_ids: - story['mitre_attack_ids'] = sorted(sto_to_mitre_attack_ids[story['name']]) - if story['name'] in sto_to_kill_chain_phases: - story['kill_chain_phases'] = sorted(sto_to_kill_chain_phases[story['name']]) - if story['name'] in sto_to_ciss: - story['ciss'] = sorted(sto_to_ciss[story['name']]) - if story['name'] in sto_to_nists: - story['nists'] = sorted(sto_to_nists[story['name']]) - - #sort stories into categories - categories = [] - category_names = set() - for story in stories: - if 'category' in story['tags']: - category_names.add(story['tags']['category'][0]) - - for category_name in sorted(category_names): - new_category = {} - new_category['name'] = category_name - new_category['stories'] = [] - categories.append(new_category) - - for story in stories: - for category in categories: - if category['name'] == story['tags']['category'][0]: - category['stories'].append(story) - - return categories - - -def write_splunk_docs(stories, detections, OUTPUT_DIR): - - categories = prepare_content(stories, detections) - - j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), - trim_blocks=True) - template = j2_env.get_template('splunk_docs_categories.j2') - output_path = OUTPUT_DIR + "/splunk_docs_categories.wiki" - output = template.render(categories=categories) - with open(output_path, 'w') as f: - f.write(output) - - return len(stories), output_path - - -def write_markdown_docs(stories, detections, OUTPUT_DIR): - - categories = prepare_content(stories, detections) - - j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), - trim_blocks=True) - template = j2_env.get_template('stories_categories.j2') - output_path = OUTPUT_DIR + "/stories_categories.md" - output = template.render(categories=categories) - with open(output_path, 'w') as f: - f.write(output) - - return len(stories), output_path - - - - - - -# function to get unique values -def unique(list1): - # init a null list - unique_list = [] - # traverse for all elements - for x in list1: - # check if exists in unique_list or not - if x not in unique_list: - unique_list.append(x) - return unique_list - - -def process_data_metadata(obj, complete_obj, name): - - # collect tagging - metadata = obj['data_metadata'] - if 'data_models' in metadata: - complete_obj[name]['data_models'] = metadata['data_models'] - if 'providing_technologies' in metadata: - complete_obj[name]['providing_technologies'] = metadata['providing_technologies'] - if 'data_source' in metadata: - complete_obj[name]['data_source'] = metadata['data_source'] - - if 'mappings' in obj: - complete_obj[name]['mappings'] = obj['mappings'] - if 'fields_required' in obj: - complete_obj[name]['entities'] = obj['fields_required'] - if 'entities' in obj: - complete_obj[name]['entities'] = obj['entities'] - - return complete_obj - - -def process_metadata(detections, story_name): - # grab mappings - mappings = dict() - - # grab provising technologies - providing_technologies = [] - - # grab datamodels - data_models = [] - - # process the above for detections - for detection_name, detection in sorted(detections.items()): - for s in detection['stories']: - - # check if the detection is part of this story - if s == story_name: - # grab providing technologies - if 'providing_technologies' in detection: - for pt in detection['providing_technologies']: - providing_technologies.append(pt) - - # grab data models - if 'data_models' in detection: - for dm in detection['data_models']: - data_models.append(dm) - - for key in detection['mappings'].keys(): - mappings[key] = list(detection['mappings'][key]) - - return mappings, providing_technologies, data_models - - -def generate_detections(REPO_PATH, stories): - # first we process detections + types = ["endpoint", "application", "cloud", "deprecated", "experimental", "network", "web"] + manifest_files = [] + for t in types: + for root, dirs, files in walk(REPO_PATH + '/detections/' + t): + for file in files: + if file.endswith(".yml"): + manifest_files.append((path.join(root, file))) detections = [] - detections_manifest_files = path.join(path.expanduser(REPO_PATH), "detections/*.yml") - for detections_manifest_file in glob.glob(detections_manifest_files): + for manifest_file in manifest_files: + detection_yaml = dict() + if VERBOSE: + print("processing manifest {0}".format(manifest_file)) - # read in each detection - with open(detections_manifest_file, 'r') as stream: + with open(manifest_file, 'r') as stream: try: - detection = list(yaml.safe_load_all(stream))[0] + object = list(yaml.safe_load_all(stream))[0] except yaml.YAMLError as exc: print(exc) - sys.exit("ERROR: reading {0}".format(detections_manifest_file)) + print("Error reading {0}".format(manifest_file)) + error = True + continue + detection_yaml = object + detection_yaml['kind'] = manifest_file.split('/')[-2] + detections.append(detection_yaml) - detections.append(detection) + j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), + trim_blocks=True) + template = j2_env.get_template('doc_detections_markdown.j2') + output_path = path.join(OUTPUT_DIR + '/detections.md') + output = template.render(detections=detections) + with open(output_path, 'w', encoding="utf-8") as f: + f.write(output) + print("doc_gen.py wrote {0} detection documentation to: {1}".format(len(detections),output_path)) - complete_detections = dict() - for detection in detections: - # lets process v1 detections - if detection['spec_version'] == 1: - if verbose: - print("processing v1 detection: {0}".format(detection['search_name'])) - name = detection['search_name'] - type = 'splunk' - description = detection['search_description'] - id = detection['search_id'] - # grab search information - correlation_rule = detection['correlation_rule'] - search = detection['search'] - schedule = detection['scheduling'] - earliest_time = schedule['earliest_time'] - latest_time = schedule['latest_time'] - cron = schedule['cron_schedule'] - # grabbing entities - entities = [] - investigations = [] - baselines = [] - responses = [] - for story_name, story in sorted(stories.items()): - for d in story['detections']: - if d['name'] == name: - if 'investigations' in story: - investigations = story['investigations'] - if 'baselines' in story: - baselines = story['baselines'] - - # lets process v2 detections - if detection['spec_version'] == 2: - if verbose: - print("processing v2 detection: {0}".format(detection['name'])) - name = detection['name'] - id = detection['id'] - entities = detection['entities'] - description = detection['description'] - - # splunk - if 'splunk' in detection['detect']: - type = 'splunk' - correlation_rule = detection['detect']['splunk']['correlation_rule'] - search = correlation_rule['search'] - earliest_time = correlation_rule['schedule']['earliest_time'] - latest_time = correlation_rule['schedule']['latest_time'] - cron = correlation_rule['schedule']['cron_schedule'] - - # uba - if 'uba' in detection['detect']: - uba = detection['detect']['uba'] - type = 'uba' - search = uba['search'] = 'CONSTRUCT DETECTION SEARCH HERE' - # earliest_time = uba['earliest_time'] - # latest_time = uba['latest_time'] - # cron = uba['cron_schedule'] - - # phantom - if 'phantom' in detection['detect']: - phantom = detection['detect']['phantom'] - type = 'phantom' - search = phantom['search'] = 'CONSTRUCT DETECTION SEARCH HERE' - # earliest_time = phantom['earliest_time'] - # latest_time = phantom['latest_time'] - # cron = phantom['cron_schedule'] - - baselines = [] - investigations = [] - responses = [] - if 'baselines' in detection: - for b in detection['baselines']: - baselines.append({"type": b['type'], "name": b['name']}) - if 'investigations' in detection: - for i in detection['investigations']: - investigations.append({"type": i['type'], "name": i['name']}) - if 'responses' in detection: - for r in detection['responses']: - responses.append({"type": r['type'], "name": r['name']}) - - complete_detections[name] = {} - complete_detections[name]['detection_name'] = name - complete_detections[name]['id'] = id - complete_detections[name]['search'] = search - complete_detections[name]['latest_time'] = latest_time - complete_detections[name]['earliest_time'] = earliest_time - complete_detections[name]['cron'] = cron - complete_detections[name]['investigations'] = investigations - complete_detections[name]['baselines'] = baselines - complete_detections[name]['responses'] = responses - complete_detections[name]['entities'] = entities - complete_detections[name]['description'] = description - complete_detections[name]['correlation_rule'] = correlation_rule - complete_detections[name]['type'] = type - complete_detections[name]['maintainers'] = detection['maintainers'] - if 'references' not in detection: - detection['references'] = [] - complete_detections[name]['references'] = detection['references'] - if 'channel' not in detection: - detection['channel'] = "" - complete_detections[name]['channel'] = detection['channel'] - if 'confidence' not in detection: - detection['confidence'] = "" - complete_detections[name]['confidence'] = detection['confidence'] - if 'eli5' not in detection: - detection['eli5'] = "" - complete_detections[name]['eli5'] = detection['eli5'] - if 'how_to_implement' not in detection: - detection['how_to_implement'] = "" - complete_detections[name]['how_to_implement'] = detection['how_to_implement'] - if 'asset_type' not in detection: - detection['asset_type'] = "" - complete_detections[name]['asset_type'] = detection['asset_type'] - if 'known_false_positives' not in detection: - detection['known_false_positives'] = "" - complete_detections[name]['known_false_positives'] = detection['known_false_positives'] - complete_detections[name]['security_domain'] = detection['security_domain'] - complete_detections[name]['version'] = detection['version'] - complete_detections[name]['spec_version'] = detection['spec_version'] - complete_detections[name]['creation_date'] = detection['creation_date'] - # set modification date to creation of there is not one - if 'modification_date' in detection: - complete_detections[name]['modification_date'] = detection['modification_date'] - else: - complete_detections[name]['modification_date'] = detection['creation_date'] - - # process its metadata - complete_detections = process_data_metadata(detection, complete_detections, name) - - # stories associated with the detection - complete_detections[name]['stories'] = [] - for story_name, story in sorted(stories.items()): - for d in story['detections']: - if d['name'] == name: - complete_detections[name]['stories'].append(story['story_name']) - - # sort uniq the results - complete_detections[name]['stories'] = sorted(set(complete_detections[name]['stories'])) - - return complete_detections - - -def generate_stories(REPO_PATH, verbose): - story_files = [] - story_manifest_files = path.join(path.expanduser(REPO_PATH), "stories/*.yml") - - for story_manifest_file in glob.glob(story_manifest_files): - - # read in each story - with open(story_manifest_file, 'r') as stream: - try: - story = list(yaml.safe_load_all(stream))[0] - except yaml.YAMLError as exc: - print(exc) - sys.exit("ERROR: reading {0}".format(story_manifest_file)) - - story_files.append(story) - - # store an object with all stories and their data - - complete_stories = dict() - for story in story_files: - if verbose: - print("processing story: {0}".format(story['name'])) - # Start building the story for the use case - name = story['name'] - complete_stories[name] = {} - complete_stories[name]['story_name'] = name - complete_stories[name]['id'] = story['id'] - - # grab modification date if it has one, otherwise write as creation date - complete_stories[name]['creation_date'] = story['creation_date'] - if 'modification_date' in story: - complete_stories[name]['modification_date'] = story['modification_date'] - - else: - complete_stories[name]['modification_date'] = story['creation_date'] - complete_stories[name]['description'] = story['description'] - if 'references' not in story: - story['references'] = [] - complete_stories[name]['references'] = story['references'] - complete_stories[name]['version'] = story['version'] - complete_stories[name]['narrative'] = story['narrative'] - complete_stories[name]['spec_version'] = story['spec_version'] - complete_stories[name]['maintainers'] = story['maintainers'] - - # grab searches - if story['spec_version'] == 1: - detections = [] - baselines = [] - investigations = [] - category = [] - - category.append(story['category']) - - if 'detection_searches' in story['searches']: - for d in story['searches']['detection_searches']: - detections.append({"type": "splunk", "name": d}) - complete_stories[name]['detections'] = detections - - # in spec v1 these are part of the story which is why we are grabbing them here - if 'support_searches' in story['searches']: - for b in story['searches']['support_searches']: - baselines.append({"type": "splunk", "name": b}) - complete_stories[name]['baselines'] = baselines - - if 'contextual_searches' in story['searches']: - for i in story['searches']['contextual_searches']: - investigations.append({"type": "splunk", "name": i}) - if 'investigative_searches' in story['searches']: - for i in story['searches']['investigative_searches']: - investigations.append({"type": "splunk", "name": i}) - complete_stories[name]['investigations'] = investigations - - if story['spec_version'] == 2: - detections = [] - if 'detections' in story: - for d in story['detections']: - detections.append({"type": d['type'], "name": d['name']}) - complete_stories[name]['detections'] = detections - category = story['category'] - complete_stories[name]['category'] = category - return complete_stories - - -def write_splunk_docs_bak(stories, detections, OUTPUT_DIR): - - paths = [] - # Create conf files from analytics stories files - splunk_docs_output_path = OUTPUT_DIR + "/splunk_docs_categories.wiki" - paths.append(splunk_docs_output_path) - output_file = open(splunk_docs_output_path, 'w') - output_file.write("= Use Case Categories=\n") - output_file.write("The collapse...\n") - - # calculate categories - categories = [] - for story_name, story in sorted(stories.items()): - c = story['category'] - categories.append(c) - - # get a unique set of them - categories = unique(categories) - for c in categories: - output_file.write("\n\n=={0}==\n".format(c[0])) - - # iterate through every story and print it out - for story_name, story in sorted(stories.items()): - # if the category matches - if story['category'] == c: - output_file.write("\n==={0}===\n".format(story_name)) - output_file.write("\n{0}\n".format(story['description'])) - output_file.write( - """\n
\n
\n""") - # header information - output_file.write("\n====Narrative====\n{0}\n".format(story['narrative'])) - - mappings, providing_technologies, data_models = process_metadata(detections, story_name) - - # providing tech - output_file.write("\n====Providing Technologies====\n") - providing_technologies = unique(providing_technologies) - for pt in providing_technologies: - output_file.write("* {0}\n".format(pt)) - - # providing tech - output_file.write("\n====Data Models====\n") - data_models = unique(data_models) - for dm in data_models: - output_file.write("* {0}\n".format(dm)) - - # mappings - output_file.write("\n====Mappings====\n") - - output_file.write("\n=====ATT&CK=====\n") - if mappings['mitre_attack']: - for m in mappings['mitre_attack']: - output_file.write("* {0}\n".format(m)) - - output_file.write("\n=====Kill Chain Phases=====\n") - if mappings['kill_chain_phases']: - for m in mappings['kill_chain_phases']: - output_file.write("* {0}\n".format(m)) - - if mappings['cis20']: - output_file.write("\n=====CIS=====\n") - for m in mappings['cis20']: - output_file.write("* {0}\n".format(m)) - - if mappings['nist']: - output_file.write("\n=====NIST=====\n") - for m in mappings['nist']: - output_file.write("* {0}\n".format(m)) - - # references - output_file.write("\n====References====\n") - for r in story['references']: - output_file.write("* {0}\n".format(r)) - - # story details - output_file.write("\ncreation_date = {0}\n\n".format(story['creation_date'])) - output_file.write("modification_date = {0}\n\n".format(story['modification_date'])) - output_file.write("version = {0}\n".format(story['version'])) - - # footer information - output_file.write("""\n
\n
\n""") - output_file.write("""\n[[Category:V:Lab:drafts]]""") - - output_file.close() - story_count = len(stories.keys()) - return story_count, paths - - -def write_markdown_docs_bak(stories, detections, OUTPUT_DIR): - paths = [] - # Create conf files from analytics stories files - splunk_docs_output_path = OUTPUT_DIR + "/stories_categories.md" - paths.append(splunk_docs_output_path) - output_file = open(splunk_docs_output_path, 'w') - output_file.write("# Categories\n") - output_file.write("Analytics stories organized by categories\n") - - # calculate categories - categories = [] - for story_name, story in sorted(stories.items()): - c = story['category'] - categories.append(c) - - # get a unique set of them - categories = unique(categories) - - # build category TOC - for c in categories: - output_file.write("\n* [{0}](#{1})\n".format(c[0], c[0].replace(' ', '-').lower())) - - for c in categories: - output_file.write("\n\n## {0}\n".format(c[0])) - - # build story TOC - for story_name, story in sorted(stories.items()): - # if the category matches - if story['category'] == c: - output_file.write("\n* [{0}](#{1})\n".format(story_name, story_name.replace(' ', '-').lower())) - - # iterate through every story and print it out - for story_name, story in sorted(stories.items()): - # if the category matches - if story['category'] == c: - output_file.write("\n### {0}\n".format(story_name)) - # basic story info - output_file.write("* id = `{0}`\n".format(story['id'])) - output_file.write("* creation_date = {0}\n".format(story['creation_date'])) - output_file.write("* modification_date = {0}\n".format(story['modification_date'])) - output_file.write("* version = {0}\n".format(story['version'])) - output_file.write("* spec_version = {0}\n".format(story['spec_version'])) - - # description and narrative - output_file.write("\n##### Description\n{0}\n".format(story['description'])) - output_file.write("\n##### Narrative\n{0}\n".format(story['narrative'])) - - # process detections - output_file.write("\n##### Detections\n") - # write all detections - if 'detections' in story: - for d in story['detections']: - output_file.write("* {0}\n".format(d['name'])) - - mappings, providing_technologies, data_models = process_metadata(detections, story_name) - - # providing tech - output_file.write("\n##### Providing Technologies\n") - providing_technologies = unique(providing_technologies) - for pt in providing_technologies: - output_file.write("* {0}\n".format(pt)) - - # data models - output_file.write("\n##### Data Models\n") - data_models = unique(data_models) - for dm in data_models: - output_file.write("{0}\n".format(dm)) - - # mappings - output_file.write("\n##### Mappings\n") - - output_file.write("\n###### ATT&CK\n") - if mappings['mitre_attack']: - for m in mappings['mitre_attack']: - output_file.write("* {0}\n".format(m)) - - output_file.write("\n###### Kill Chain Phases\n") - if mappings['kill_chain_phases']: - for m in mappings['kill_chain_phases']: - output_file.write("* {0}\n".format(m)) - - if mappings['cis20']: - output_file.write("\n###### CIS\n") - for m in mappings['cis20']: - output_file.write("* {0}\n".format(m)) - - if mappings['nist']: - output_file.write("\n###### NIST\n") - for m in mappings['nist']: - output_file.write("* {0}\n".format(m)) - - # maintainers - output_file.write("\n##### Maintainers\n") - for m in story['maintainers']: - output_file.write("* name = {0}\n".format(m['name'])) - output_file.write("* email = {0}\n".format(m['email'])) - output_file.write("* company = {0}\n".format(m['company'])) - - # references - output_file.write("\n##### References\n") - for r in story['references']: - output_file.write("* {0}\n".format(r)) - - output_file.close() - story_count = len(stories.keys()) - return story_count, paths - - -def parse_data_models_from_search(search): - match = re.search('from\sdatamodel\s?=\s?([^\s.]*)',search) - if match is not None: - return match.group(1) return False if __name__ == "__main__": @@ -672,7 +65,7 @@ if __name__ == "__main__": args = parser.parse_args() REPO_PATH = args.path OUTPUT_DIR = args.output - verbose = args.verbose + VERBOSE = args.verbose type = args.type allowed_types = ['stories', 'detections', 'spec', 'all'] @@ -681,7 +74,11 @@ if __name__ == "__main__": parser.print_help() sys.exit(1) - #if type == 'all': + TEMPLATE_PATH = path.join(REPO_PATH, 'bin/jinja2_templates') + + if type == 'all': + generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE) + print("finished successfully!") diff --git a/bin/jinja2_templates/doc_detections_markdown.j2 b/bin/jinja2_templates/doc_detections_markdown.j2 new file mode 100644 index 0000000000..de1853d709 --- /dev/null +++ b/bin/jinja2_templates/doc_detections_markdown.j2 @@ -0,0 +1,121 @@ +# Splunk Security Content Detections +![security_content](static/logo.png) +===== +All the detections shipped to different Splunk products. Below is a breakdown by kind. + +## Cloud +
+ View + +{% for detection in detections %} +{% if detection.kind == 'cloud' %} +- [{{ detection.name }}](#{{ detection.name|lower|replace(" ", "-") }}) +{% endif %} +{% endfor %} +
+ +## Endpoint +
+ View + +{% for detection in detections %} +{% if detection.kind == 'endpoint' %} +- [{{ detection.name }}](#{{ detection.name|lower|replace(" ", "-") }}) +{% endif %} +{% endfor %} +
+ +## Network +
+ View + +{% for detection in detections %} +{% if detection.kind == 'network' %} +- [{{ detection.name }}](#{{ detection.name|lower|replace(" ", "-") }}) +{% endif %} +{% endfor %} +
+ +## Application +
+ View + +{% for detection in detections %} +{% if detection.kind == 'application' %} +- [{{ detection.name }}](#{{ detection.name|lower|replace(" ", "-") }}) +{% endif %} +{% endfor %} +
+ +## Web +
+ View + +{% for detection in detections %} +{% if detection.kind == 'web' %} +- [{{ detection.name }}](#{{ detection.name|lower|replace(" ", "-") }}) +{% endif %} +{% endfor %} +
+ + + +{% for detection in detections %} +### {{ detection.name }} +{{ detection.description }} + +- **Product**: {{ detection.tags.product|join(', ') }} +- **Data Models**: {{ detection.datamodels|join(', ') }} +- **ATT&CK**: {{ detection.tags.mitre_attack_id|join(', ') }} +- **Last Updated**: {{ detection.date }} + +
+ View + +#### Search +``` +{{ detection.search }} +``` +#### Associated Analytic Story +{% for story in detection.tags.analytics_story %} +* {{ story }} +{% endfor %} + +#### How To Implement +{{ detection.how_to_implement}} + +#### Required fields +{% for field in detection.tags.required_fields %} +* {{ field }} +{% endfor %} + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +{% for id in detection.tags.mitre_attack_id %} +| {{ id }} | x | x | +{% endfor %} + +#### Kill Chain Phases +{% for phase in detection.tags.kill_chain_phases %} +* {{ phase }} +{% endfor %} + +#### Known False Positives +{{ detection.known_false_positives}} + +#### References +{% for reference in detection.references %} +* {{ reference }} +{% endfor %} + +#### Test Dataset +{% for dataset in detection.tags.dataset %} +* {{ dataset }} +{% endfor %} + +_version_: {{detection.version}} +
+--- +{% endfor %} diff --git a/docs/detections.md b/docs/detections.md new file mode 100644 index 0000000000..ae69d2cef7 --- /dev/null +++ b/docs/detections.md @@ -0,0 +1,16282 @@ +# Splunk Security Content Detections +![security_content](static/logo.png) +===== +All the detections shipped to different Splunk products. Below is a breakdown by kind. + +## Cloud +
+ View + +- [O365 Add App Role Assignment Grant User](#o365-add-app-role-assignment-grant-user) +- [AWS SAML Access by Provider User and Principal](#aws-saml-access-by-provider-user-and-principal) +- [Cloud Compute Instance Created In Previously Unused Region](#cloud-compute-instance-created-in-previously-unused-region) +- [O365 Excessive SSO logon errors](#o365-excessive-sso-logon-errors) +- [O365 Suspicious Admin Email Forwarding](#o365-suspicious-admin-email-forwarding) +- [Cloud Provisioning Activity From Previously Unseen City](#cloud-provisioning-activity-from-previously-unseen-city) +- [Detect New Open GCP Storage Buckets](#detect-new-open-gcp-storage-buckets) +- [Abnormally High Number Of Cloud Instances Destroyed](#abnormally-high-number-of-cloud-instances-destroyed) +- [High Number of Login Failures from a single source](#high-number-of-login-failures-from-a-single-source) +- [Detect AWS Console Login by User from New City](#detect-aws-console-login-by-user-from-new-city) +- [Detect AWS Console Login by User from New Country](#detect-aws-console-login-by-user-from-new-country) +- [Cloud Instance Modified By Previously Unseen User](#cloud-instance-modified-by-previously-unseen-user) +- [Detect Spike in S3 Bucket deletion](#detect-spike-in-s3-bucket-deletion) +- [AWS Network Access Control List Deleted](#aws-network-access-control-list-deleted) +- [Cloud Compute Instance Created By Previously Unseen User](#cloud-compute-instance-created-by-previously-unseen-user) +- [AWS Detect Users with KMS keys performing encryption S3](#aws-detect-users-with-kms-keys-performing-encryption-s3) +- [O365 Suspicious User Email Forwarding](#o365-suspicious-user-email-forwarding) +- [Cloud API Calls From Previously Unseen User Roles](#cloud-api-calls-from-previously-unseen-user-roles) +- [O365 PST export alert](#o365-pst-export-alert) +- [AWS Network Access Control List Created with All Open Ports](#aws-network-access-control-list-created-with-all-open-ports) +- [Abnormally High Number Of Cloud Instances Launched](#abnormally-high-number-of-cloud-instances-launched) +- [Cloud Provisioning Activity From Previously Unseen Country](#cloud-provisioning-activity-from-previously-unseen-country) +- [Cloud Compute Instance Created With Previously Unseen Instance Type](#cloud-compute-instance-created-with-previously-unseen-instance-type) +- [AWS Detect Users creating keys with encrypt policy without MFA](#aws-detect-users-creating-keys-with-encrypt-policy-without-mfa) +- [O365 Suspicious Rights Delegation](#o365-suspicious-rights-delegation) +- [New container uploaded to AWS ECR](#new-container-uploaded-to-aws-ecr) +- [Detect Spike in blocked Outbound Traffic from your AWS](#detect-spike-in-blocked-outbound-traffic-from-your-aws) +- [Detect New Open S3 buckets](#detect-new-open-s3-buckets) +- [Detect AWS Console Login by User from New Region](#detect-aws-console-login-by-user-from-new-region) +- [Detect AWS Console Login by New User](#detect-aws-console-login-by-new-user) +- [Detect Spike in AWS Security Hub Alerts for User](#detect-spike-in-aws-security-hub-alerts-for-user) +- [Cloud Provisioning Activity From Previously Unseen IP Address](#cloud-provisioning-activity-from-previously-unseen-ip-address) +- [O365 Disable MFA](#o365-disable-mfa) +- [Detect New Open S3 Buckets over AWS CLI](#detect-new-open-s3-buckets-over-aws-cli) +- [O365 Excessive Authentication Failures Alert](#o365-excessive-authentication-failures-alert) +- [O365 Added Service Principal](#o365-added-service-principal) +- [Detect Spike in AWS Security Hub Alerts for EC2 Instance](#detect-spike-in-aws-security-hub-alerts-for-ec2-instance) +- [Cloud Compute Instance Created With Previously Unseen Image](#cloud-compute-instance-created-with-previously-unseen-image) +- [O365 Bypass MFA via Trusted IP](#o365-bypass-mfa-via-trusted-ip) +- [Abnormally High Number Of Cloud Infrastructure API Calls](#abnormally-high-number-of-cloud-infrastructure-api-calls) +- [Detect GCP Storage access from a new IP](#detect-gcp-storage-access-from-a-new-ip) +- [AWS Cross Account Activity From Previously Unseen Account](#aws-cross-account-activity-from-previously-unseen-account) +- [O365 New Federated Domain Added](#o365-new-federated-domain-added) +- [Detect S3 access from a new IP](#detect-s3-access-from-a-new-ip) +- [Cloud Provisioning Activity From Previously Unseen Region](#cloud-provisioning-activity-from-previously-unseen-region) +- [Abnormally High Number Of Cloud Security Group API Calls](#abnormally-high-number-of-cloud-security-group-api-calls) +- [AWS SAML Update identity provider](#aws-saml-update-identity-provider) +- [Kubernetes Azure detect sensitive role access](#kubernetes-azure-detect-sensitive-role-access) +- [Kubernetes AWS detect sensitive role access](#kubernetes-aws-detect-sensitive-role-access) +- [Kubernetes GCP detect sensitive object access](#kubernetes-gcp-detect-sensitive-object-access) +- [Kubernetes Azure scan fingerprint](#kubernetes-azure-scan-fingerprint) +- [Kubernetes AWS detect service accounts forbidden failure access](#kubernetes-aws-detect-service-accounts-forbidden-failure-access) +- [Kubernetes GCP detect most active service accounts by pod](#kubernetes-gcp-detect-most-active-service-accounts-by-pod) +- [aws detect permanent key creation](#aws-detect-permanent-key-creation) +- [GCP Detect high risk permissions by resource and account](#gcp-detect-high-risk-permissions-by-resource-and-account) +- [Kubernetes AWS detect suspicious kubectl calls](#kubernetes-aws-detect-suspicious-kubectl-calls) +- [Kubernetes AWS detect RBAC authorization by account](#kubernetes-aws-detect-rbac-authorization-by-account) +- [Kubernetes Azure detect most active service accounts by pod namespace](#kubernetes-azure-detect-most-active-service-accounts-by-pod-namespace) +- [GCP Detect gcploit framework](#gcp-detect-gcploit-framework) +- [Kubernetes Azure detect service accounts forbidden failure access](#kubernetes-azure-detect-service-accounts-forbidden-failure-access) +- [Kubernetes AWS detect most active service accounts by pod](#kubernetes-aws-detect-most-active-service-accounts-by-pod) +- [Amazon EKS Kubernetes cluster scan detection](#amazon-eks-kubernetes-cluster-scan-detection) +- [Kubernetes Azure detect RBAC authorization by account](#kubernetes-azure-detect-rbac-authorization-by-account) +- [aws detect attach to role policy](#aws-detect-attach-to-role-policy) +- [AWS EKS Kubernetes cluster sensitive object access](#aws-eks-kubernetes-cluster-sensitive-object-access) +- [GCP Kubernetes cluster pod scan detection](#gcp-kubernetes-cluster-pod-scan-detection) +- [GCP Kubernetes cluster scan detection](#gcp-kubernetes-cluster-scan-detection) +- [Kubernetes GCP detect suspicious kubectl calls](#kubernetes-gcp-detect-suspicious-kubectl-calls) +- [gcp detect oauth token abuse](#gcp-detect-oauth-token-abuse) +- [Kubernetes Azure detect suspicious kubectl calls](#kubernetes-azure-detect-suspicious-kubectl-calls) +- [Kubernetes GCP detect sensitive role access](#kubernetes-gcp-detect-sensitive-role-access) +- [aws detect sts get session token abuse](#aws-detect-sts-get-session-token-abuse) +- [Amazon EKS Kubernetes Pod scan detection](#amazon-eks-kubernetes-pod-scan-detection) +- [aws detect role creation](#aws-detect-role-creation) +- [Kubernetes GCP detect RBAC authorizations by account](#kubernetes-gcp-detect-rbac-authorizations-by-account) +- [Kubernetes Azure pod scan fingerprint](#kubernetes-azure-pod-scan-fingerprint) +- [aws detect sts assume role abuse](#aws-detect-sts-assume-role-abuse) +- [Kubernetes Azure detect sensitive object access](#kubernetes-azure-detect-sensitive-object-access) +- [GCP Detect accounts with high risk roles by project](#gcp-detect-accounts-with-high-risk-roles-by-project) +- [Kubernetes GCP detect service accounts forbidden failure access](#kubernetes-gcp-detect-service-accounts-forbidden-failure-access) +
+ +## Endpoint +
+ View + +- [Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules](#reconnaissance-of-process-or-service-hijacking-opportunities-via-mimikatz-modules) +- [Schtasks used for forcing a reboot](#schtasks-used-for-forcing-a-reboot) +- [Single Letter Process On Endpoint](#single-letter-process-on-endpoint) +- [Suspicious Rundll32 no CommandLine Arguments](#suspicious-rundll32-no-commandline-arguments) +- [Detect Rare Executables](#detect-rare-executables) +- [Dump LSASS via comsvcs DLL](#dump-lsass-via-comsvcs-dll) +- [Malicious PowerShell Process - Connect To Internet With Hidden Window](#malicious-powershell-process---connect-to-internet-with-hidden-window) +- [Suspicious Rundll32 dllregisterserver](#suspicious-rundll32-dllregisterserver) +- [Illegal Access To User Content via PowerSploit modules](#illegal-access-to-user-content-via-powersploit-modules) +- [Reconnaissance of Access and Persistence Opportunities via PowerSploit modules](#reconnaissance-of-access-and-persistence-opportunities-via-powersploit-modules) +- [Shim Database File Creation](#shim-database-file-creation) +- [Processes launching netsh](#processes-launching-netsh) +- [Credential Extraction indicative of use of DSInternals credential conversion modules](#credential-extraction-indicative-of-use-of-dsinternals-credential-conversion-modules) +- [Schtasks scheduling job on remote system](#schtasks-scheduling-job-on-remote-system) +- [Certutil exe certificate extraction](#certutil-exe-certificate-extraction) +- [Unusually Long Command Line](#unusually-long-command-line) +- [Reconnaissance of Connectivity via PowerSploit modules](#reconnaissance-of-connectivity-via-powersploit-modules) +- [Create Remote Thread into LSASS](#create-remote-thread-into-lsass) +- [Reconnaissance and Access to Shared Resources via PowerSploit modules](#reconnaissance-and-access-to-shared-resources-via-powersploit-modules) +- [Attempted Credential Dump From Registry via Reg exe](#attempted-credential-dump-from-registry-via-reg-exe) +- [Detect processes used for System Network Configuration Discovery](#detect-processes-used-for-system-network-configuration-discovery) +- [Setting Credentials via DSInternals modules](#setting-credentials-via-dsinternals-modules) +- [Common Ransomware Extensions](#common-ransomware-extensions) +- [Assessment of Credential Strength via DSInternals modules](#assessment-of-credential-strength-via-dsinternals-modules) +- [Detect Rundll32 Application Control Bypass - setupapi](#detect-rundll32-application-control-bypass---setupapi) +- [Suspicious microsoft workflow compiler rename](#suspicious-microsoft-workflow-compiler-rename) +- [Execution of File with Multiple Extensions](#execution-of-file-with-multiple-extensions) +- [Illegal Management of Active Directory Elements and Policies via DSInternals modules](#illegal-management-of-active-directory-elements-and-policies-via-dsinternals-modules) +- [Detect Prohibited Applications Spawning cmd exe](#detect-prohibited-applications-spawning-cmd-exe) +- [Illegal Service and Process Control via PowerSploit modules](#illegal-service-and-process-control-via-powersploit-modules) +- [Detect Computer Changed with Anonymous Account](#detect-computer-changed-with-anonymous-account) +- [Suspicious writes to windows Recycle Bin](#suspicious-writes-to-windows-recycle-bin) +- [BCDEdit Failure Recovery Modification](#bcdedit-failure-recovery-modification) +- [Access LSASS Memory for Dump Creation](#access-lsass-memory-for-dump-creation) +- [Ntdsutil export ntds](#ntdsutil-export-ntds) +- [Detect Regsvcs with No Command Line Arguments](#detect-regsvcs-with-no-command-line-arguments) +- [Credential Extraction indicative of use of DSInternals modules](#credential-extraction-indicative-of-use-of-dsinternals-modules) +- [Attempted Credential Dump From Registry via Reg exe](#attempted-credential-dump-from-registry-via-reg-exe) +- [Reg exe Manipulating Windows Services Registry Keys](#reg-exe-manipulating-windows-services-registry-keys) +- [Suspicious Rundll32 Rename](#suspicious-rundll32-rename) +- [Credential Dumping via Symlink to Shadow Copy](#credential-dumping-via-symlink-to-shadow-copy) +- [Malicious PowerShell Process - Encoded Command](#malicious-powershell-process---encoded-command) +- [Reconnaissance of Defensive Tools via PowerSploit modules](#reconnaissance-of-defensive-tools-via-powersploit-modules) +- [File with Samsam Extension](#file-with-samsam-extension) +- [Script Execution via WMI](#script-execution-via-wmi) +- [Process Execution via WMI](#process-execution-via-wmi) +- [Illegal Privilege Elevation via Mimikatz modules](#illegal-privilege-elevation-via-mimikatz-modules) +- [Detect Regsvcs with Network Connection](#detect-regsvcs-with-network-connection) +- [Credential Extraction indicative of FGDump and CacheDump with s option](#credential-extraction-indicative-of-fgdump-and-cachedump-with-s-option) +- [Monitor Registry Keys for Print Monitors](#monitor-registry-keys-for-print-monitors) +- [Shim Database Installation With Suspicious Parameters](#shim-database-installation-with-suspicious-parameters) +- [Suspicious Regsvr32 Register Suspicious Path](#suspicious-regsvr32-register-suspicious-path) +- [Detect Regsvr32 Application Control Bypass](#detect-regsvr32-application-control-bypass) +- [Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules](#reconnaissance-and-access-to-active-directoty-infrastructure-via-powersploit-modules) +- [Probing Access with Stolen Credentials via PowerSploit modules](#probing-access-with-stolen-credentials-via-powersploit-modules) +- [Unusually Long Command Line - MLTK](#unusually-long-command-line---mltk) +- [Detect Credential Dumping through LSASS access](#detect-credential-dumping-through-lsass-access) +- [Detect Rundll32 Inline HTA Execution](#detect-rundll32-inline-hta-execution) +- [Illegal Service and Process Control via Mimikatz modules](#illegal-service-and-process-control-via-mimikatz-modules) +- [Registry Keys for Creating SHIM Databases](#registry-keys-for-creating-shim-databases) +- [Short Lived Windows Accounts](#short-lived-windows-accounts) +- [Credential Extraction indicative of use of PowerSploit modules](#credential-extraction-indicative-of-use-of-powersploit-modules) +- [Windows Event Log Cleared](#windows-event-log-cleared) +- [Detect Prohibited Applications Spawning cmd exe](#detect-prohibited-applications-spawning-cmd-exe) +- [Unload Sysmon Filter Driver](#unload-sysmon-filter-driver) +- [Unusually Long Command Line](#unusually-long-command-line) +- [Detect Rundll32 Application Control Bypass - advpack](#detect-rundll32-application-control-bypass---advpack) +- [Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules](#reconnaissance-and-access-to-accounts-groups-and-policies-via-powersploit-modules) +- [Create local admin accounts using net exe](#create-local-admin-accounts-using-net-exe) +- [Reconnaissance and Access to Computers via Mimikatz modules](#reconnaissance-and-access-to-computers-via-mimikatz-modules) +- [Detect Regasm with Network Connection](#detect-regasm-with-network-connection) +- [System Process Running from Unexpected Location](#system-process-running-from-unexpected-location) +- [Rare Parent-Child Process Relationship](#rare-parent-child-process-relationship) +- [Setting Credentials via PowerSploit modules](#setting-credentials-via-powersploit-modules) +- [Dump LSASS via procdump](#dump-lsass-via-procdump) +- [Disabling Remote User Account Control](#disabling-remote-user-account-control) +- [Reconnaissance of Credential Stores and Services via Mimikatz modules](#reconnaissance-of-credential-stores-and-services-via-mimikatz-modules) +- [Creation of Shadow Copy with wmic and powershell](#creation-of-shadow-copy-with-wmic-and-powershell) +- [Detect HTML Help Renamed](#detect-html-help-renamed) +- [Windows Security Account Manager Stopped](#windows-security-account-manager-stopped) +- [Reconnaissance and Access to Accounts and Groups via Mimikatz modules](#reconnaissance-and-access-to-accounts-and-groups-via-mimikatz-modules) +- [Detect Rundll32 Application Control Bypass - syssetup](#detect-rundll32-application-control-bypass---syssetup) +- [System Processes Run From Unexpected Locations](#system-processes-run-from-unexpected-locations) +- [USN Journal Deletion](#usn-journal-deletion) +- [Detect Regasm Spawning a Process](#detect-regasm-spawning-a-process) +- [Credential Extraction indicative of Lazagne command line options](#credential-extraction-indicative-of-lazagne-command-line-options) +- [Credential Extraction indicative of FGDump and CacheDump with v option](#credential-extraction-indicative-of-fgdump-and-cachedump-with-v-option) +- [Detect HTML Help Using InfoTech Storage Handlers](#detect-html-help-using-infotech-storage-handlers) +- [Common Ransomware Notes](#common-ransomware-notes) +- [Reconnaissance and Access to Processes and Services via Mimikatz modules](#reconnaissance-and-access-to-processes-and-services-via-mimikatz-modules) +- [Sc exe Manipulating Windows Services](#sc-exe-manipulating-windows-services) +- [Overwriting Accessibility Binaries](#overwriting-accessibility-binaries) +- [Detect Regsvcs Spawning a Process](#detect-regsvcs-spawning-a-process) +- [Detect MSHTA Url in Command Line](#detect-mshta-url-in-command-line) +- [WBAdmin Delete System Backups](#wbadmin-delete-system-backups) +- [Illegal Management of Computers and Active Directory Elements via PowerSploit modules](#illegal-management-of-computers-and-active-directory-elements-via-powersploit-modules) +- [Suspicious Reg exe Process](#suspicious-reg-exe-process) +- [Detect New Local Admin account](#detect-new-local-admin-account) +- [Reconnaissance and Access to Computers and Domains via PowerSploit modules](#reconnaissance-and-access-to-computers-and-domains-via-powersploit-modules) +- [Suspicious msbuild path](#suspicious-msbuild-path) +- [Credential Extraction native Microsoft debuggers peek into the kernel](#credential-extraction-native-microsoft-debuggers-peek-into-the-kernel) +- [Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass](#attempt-to-set-default-powershell-execution-policy-to-unrestricted-or-bypass) +- [Remote Process Instantiation via WMI](#remote-process-instantiation-via-wmi) +- [Detect mshta inline hta execution](#detect-mshta-inline-hta-execution) +- [Samsam Test File Write](#samsam-test-file-write) +- [Suspicious microsoft workflow compiler usage](#suspicious-microsoft-workflow-compiler-usage) +- [Illegal Enabling or Disabling of Accounts via DSInternals modules](#illegal-enabling-or-disabling-of-accounts-via-dsinternals-modules) +- [Deleting Shadow Copies](#deleting-shadow-copies) +- [Reconnaissance and Access to Shared Resources via Mimikatz modules](#reconnaissance-and-access-to-shared-resources-via-mimikatz-modules) +- [Attempt To Stop Security Service](#attempt-to-stop-security-service) +- [RunDLL Loading DLL By Ordinal](#rundll-loading-dll-by-ordinal) +- [Ryuk Test Files Detected](#ryuk-test-files-detected) +- [Credential Extraction indicative of use of Mimikatz modules](#credential-extraction-indicative-of-use-of-mimikatz-modules) +- [Credential Dumping via Copy Command from Shadow Copy](#credential-dumping-via-copy-command-from-shadow-copy) +- [Scheduled Task Deleted Or Created via CMD](#scheduled-task-deleted-or-created-via-cmd) +- [Suspicious wevtutil Usage](#suspicious-wevtutil-usage) +- [WMI Permanent Event Subscription - Sysmon](#wmi-permanent-event-subscription---sysmon) +- [Malicious PowerShell Process With Obfuscation Techniques](#malicious-powershell-process-with-obfuscation-techniques) +- [Detect mshta renamed](#detect-mshta-renamed) +- [Suspicious Rundll32 StartW](#suspicious-rundll32-startw) +- [Detect HTML Help Spawn Child Process](#detect-html-help-spawn-child-process) +- [Detect Kerberoasting](#detect-kerberoasting) +- [Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules](#reconnaissance-of-privilege-escalation-opportunities-via-powersploit-modules) +- [Applying Stolen Credentials via Mimikatz modules](#applying-stolen-credentials-via-mimikatz-modules) +- [Detect HTML Help URL in Command Line](#detect-html-help-url-in-command-line) +- [Detect Use of cmd exe to Launch Script Interpreters](#detect-use-of-cmd-exe-to-launch-script-interpreters) +- [Illegal Deletion of Logs via Mimikatz modules](#illegal-deletion-of-logs-via-mimikatz-modules) +- [Detect Excessive User Account Lockouts](#detect-excessive-user-account-lockouts) +- [Suspicious MSBuild Rename](#suspicious-msbuild-rename) +- [Attempt To Add Certificate To Untrusted Store](#attempt-to-add-certificate-to-untrusted-store) +- [Illegal Account Creation via PowerSploit modules](#illegal-account-creation-via-powersploit-modules) +- [System Information Discovery Detection](#system-information-discovery-detection) +- [Illegal Privilege Elevation and Persistence via PowerSploit modules](#illegal-privilege-elevation-and-persistence-via-powersploit-modules) +- [Detect Activity Related to Pass the Hash Attacks](#detect-activity-related-to-pass-the-hash-attacks) +- [Suspicious mshta child process](#suspicious-mshta-child-process) +- [Detect Regasm with no Command Line Arguments](#detect-regasm-with-no-command-line-arguments) +- [Malicious PowerShell Process - Execution Policy Bypass](#malicious-powershell-process---execution-policy-bypass) +- [Suspicious MSBuild Spawn](#suspicious-msbuild-spawn) +- [Process Creating LNK file in Suspicious Location](#process-creating-lnk-file-in-suspicious-location) +- [Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals](#credential-extraction-via-get-addbaccount-module-present-in-powersploit-and-dsinternals) +- [Batch File Write to System32](#batch-file-write-to-system32) +- [Detect Dump LSASS Memory using comsvcs](#detect-dump-lsass-memory-using-comsvcs) +- [Create or delete windows shares using net exe](#create-or-delete-windows-shares-using-net-exe) +- [NLTest Domain Trust Discovery](#nltest-domain-trust-discovery) +- [Creation of Shadow Copy](#creation-of-shadow-copy) +- [Registry Keys Used For Privilege Escalation](#registry-keys-used-for-privilege-escalation) +- [Detect Excessive Account Lockouts From Endpoint](#detect-excessive-account-lockouts-from-endpoint) +- [Credential Extraction native Microsoft debuggers via z command line option](#credential-extraction-native-microsoft-debuggers-via-z-command-line-option) +- [Creation of lsass Dump with Taskmgr](#creation-of-lsass-dump-with-taskmgr) +- [Hiding Files And Directories With Attrib exe](#hiding-files-and-directories-with-attrib-exe) +- [First time seen command line argument](#first-time-seen-command-line-argument) +- [Applying Stolen Credentials via PowerSploit modules](#applying-stolen-credentials-via-powersploit-modules) +- [Detect PsExec With accepteula Flag](#detect-psexec-with-accepteula-flag) +- [Detect Path Interception By Creation Of program exe](#detect-path-interception-by-creation-of-program-exe) +- [Suspicious mshta spawn](#suspicious-mshta-spawn) +- [Detect Pass the Hash](#detect-pass-the-hash) +- [Reconnaissance and Access to Operating System Elements via PowerSploit modules](#reconnaissance-and-access-to-operating-system-elements-via-powersploit-modules) +- [Registry Keys Used For Persistence](#registry-keys-used-for-persistence) +- [Windows AdFind Exe](#windows-adfind-exe) +- [More than usual number of LOLBAS applications in short time period](#more-than-usual-number-of-lolbas-applications-in-short-time-period) +- [Setting Credentials via Mimikatz modules](#setting-credentials-via-mimikatz-modules) +- [Dump LSASS via procdump Rename](#dump-lsass-via-procdump-rename) +- [First Time Seen Child Process of Zoom](#first-time-seen-child-process-of-zoom) +- [Kerberoasting spn request with RC4 encryption](#kerberoasting-spn-request-with-rc4-encryption) +- [Processes Tapping Keyboard Events](#processes-tapping-keyboard-events) +- [Child Processes of Spoolsv exe](#child-processes-of-spoolsv-exe) +- [WMI Permanent Event Subscription](#wmi-permanent-event-subscription) +- [Detect Baron Samedit CVE-2021-3156 Segfault](#detect-baron-samedit-cve-2021-3156-segfault) +- [Spike in File Writes](#spike-in-file-writes) +- [Detect Baron Samedit CVE-2021-3156 via OSQuery](#detect-baron-samedit-cve-2021-3156-via-osquery) +- [Detection of tools built by NirSoft](#detection-of-tools-built-by-nirsoft) +- [Detect Oulook exe writing a zip file](#detect-oulook-exe-writing-a--zip-file) +- [First Time Seen Running Windows Service](#first-time-seen-running-windows-service) +- [WMI Temporary Event Subscription](#wmi-temporary-event-subscription) +- [Sunburst Correlation DLL and Network Event](#sunburst-correlation-dll-and-network-event) +- [MacOS - Re-opened Applications](#macos---re-opened-applications) +- [Detect Baron Samedit CVE-2021-3156](#detect-baron-samedit-cve-2021-3156) +- [Remote Desktop Process Running On System](#remote-desktop-process-running-on-system) +
+ +## Network +
+ View + +- [Protocols passing authentication in cleartext](#protocols-passing-authentication-in-cleartext) +- [Large Volume of DNS ANY Queries](#large-volume-of-dns-any-queries) +- [Hosts receiving high volume of network traffic from email server](#hosts-receiving-high-volume-of-network-traffic-from-email-server) +- [Detect ARP Poisoning](#detect-arp-poisoning) +- [Prohibited Network Traffic Allowed](#prohibited-network-traffic-allowed) +- [DNS record changed](#dns-record-changed) +- [Protocol or Port Mismatch](#protocol-or-port-mismatch) +- [Excessive DNS Failures](#excessive-dns-failures) +- [Detect Zerologon via Zeek](#detect-zerologon-via-zeek) +- [DNS Query Length Outliers - MLTK](#dns-query-length-outliers---mltk) +- [TOR Traffic](#tor-traffic) +- [Detect Large Outbound ICMP Packets](#detect-large-outbound-icmp-packets) +- [Detect SNICat SNI Exfiltration](#detect-snicat-sni-exfiltration) +- [SMB Traffic Spike](#smb-traffic-spike) +- [Detect Port Security Violation](#detect-port-security-violation) +- [SMB Traffic Spike - MLTK](#smb-traffic-spike---mltk) +- [Remote Desktop Network Traffic](#remote-desktop-network-traffic) +- [DNS Query Length With High Standard Deviation](#dns-query-length-with-high-standard-deviation) +- [Detect Unauthorized Assets by MAC address](#detect-unauthorized-assets-by-mac-address) +- [Detect Outbound SMB Traffic](#detect-outbound-smb-traffic) +- [Detect Windows DNS SIGRed via Zeek](#detect-windows-dns-sigred-via-zeek) +- [Detect hosts connecting to dynamic domain providers](#detect-hosts-connecting-to-dynamic-domain-providers) +- [Detect Traffic Mirroring](#detect-traffic-mirroring) +- [Unusually Long Content-Type Length](#unusually-long-content-type-length) +- [Detect Rogue DHCP Server](#detect-rogue-dhcp-server) +- [Detect IPv6 Network Infrastructure Threats](#detect-ipv6-network-infrastructure-threats) +- [Detect Windows DNS SIGRed via Splunk Stream](#detect-windows-dns-sigred-via-splunk-stream) +- [Remote Desktop Network Bruteforce](#remote-desktop-network-bruteforce) +- [Detect Software Download To Network Device](#detect-software-download-to-network-device) +
+ +## Application +
+ View + +- [Email files written outside of the Outlook directory](#email-files-written-outside-of-the-outlook-directory) +- [Web Servers Executing Suspicious Processes](#web-servers-executing-suspicious-processes) +- [Multiple Okta Users With Invalid Credentials From The Same IP](#multiple-okta-users-with-invalid-credentials-from-the-same-ip) +- [Okta Failed SSO Attempts](#okta-failed-sso-attempts) +- [Okta Account Lockout Events](#okta-account-lockout-events) +- [Okta User Logins From Multiple Cities](#okta-user-logins-from-multiple-cities) +- [Detect New Login Attempts to Routers](#detect-new-login-attempts-to-routers) +- [Email Attachments With Lots Of Spaces](#email-attachments-with-lots-of-spaces) +- [Phishing Email Detection by Machine Learning Method - SSA](#phishing-email-detection-by-machine-learning-method---ssa) +- [Suspicious Email - UBA Anomaly](#suspicious-email---uba-anomaly) +- [No Windows Updates in a time frame](#no-windows-updates-in-a-time-frame) +- [Suspicious Email Attachment Extensions](#suspicious-email-attachment-extensions) +- [Monitor Email For Brand Abuse](#monitor-email-for-brand-abuse) +- [Email servers sending high volume traffic to hosts](#email-servers-sending-high-volume-traffic-to-hosts) +- [Suspicious Java Classes](#suspicious-java-classes) +- [Spectre and Meltdown Vulnerable Systems](#spectre-and-meltdown-vulnerable-systems) +
+ +## Web +
+ View + +- [Detect F5 TMUI RCE CVE-2020-5902](#detect-f5-tmui-rce-cve-2020-5902) +- [Detect malicious requests to exploit JBoss servers](#detect-malicious-requests-to-exploit-jboss-servers) +- [Supernova Webshell](#supernova-webshell) +- [Detect attackers scanning for vulnerable JBoss servers](#detect-attackers-scanning-for-vulnerable-jboss-servers) +- [Monitor Web Traffic For Brand Abuse](#monitor-web-traffic-for-brand-abuse) +- [SQL Injection with Long URLs](#sql-injection-with-long-urls) +- [Web Fraud - Anomalous User Clickspeed](#web-fraud---anomalous-user-clickspeed) +- [Web Fraud - Account Harvesting](#web-fraud---account-harvesting) +- [Web Fraud - Password Sharing Across Accounts](#web-fraud---password-sharing-across-accounts) +
+ + + +### Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules +This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1543, T1055, T1574 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::detours/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1543 | x | x | +| T1055 | x | x | +| T1574 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz +* https://en.wikipedia.org/wiki/Microsoft_Detours + +#### Test Dataset + +_version_: 1 +
+--- +### Schtasks used for forcing a reboot +This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1053.005 +- **Last Updated**: 2020-12-07 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_used_for_forcing_a_reboot_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053.005 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_shutdown/windows-sysmon.log + +_version_: 4 +
+--- +### Single Letter Process On Endpoint +This search looks for process names that consist only of a single letter. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1204.002 +- **Last Updated**: 2020-12-08 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name | `single_letter_process_on_endpoint_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1204.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/single_letter_exe/windows-sysmon.log + +_version_: 3 +
+--- +### Suspicious Rundll32 no CommandLine Arguments +The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-09 + +
+ View + +#### Search +``` +`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) | regex CommandLine="(rundll32\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_no_commandline_arguments_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Detect Rare Executables +This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-03-16 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | rex field=user "(?.*)\\\\(?.*)" | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_allow_list`| table process ] | `detect_rare_executables_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_allow_list` searches two lookup files for allowed processes. These consist of `rare_process_allow_list_default.csv` and `rare_process_allow_list_local.csv`. To add your own processes to the allow list, add them to `rare_process_allow_list_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the allow_list value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation +* Command and Control +* Actions on Objectives + +#### Known False Positives +Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_allow_list_local.csv` to filter them out of your search results. + +#### References + +#### Test Dataset + +_version_: 5 +
+--- +### Dump LSASS via comsvcs DLL +Detect the usage of comsvcs.dll for dumping the lsass process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2020-02-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_comsvcs_dll_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/ +* https://twitter.com/SBousseaden/status/1167417096374050817 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Malicious PowerShell Process - Connect To Internet With Hidden Window +This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001 +- **Last Updated**: 2020-11-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +Legitimate process can have this combination of command-line options, but it's not common. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/hidden_powershell/windows-sysmon.log + +_version_: 5 +
+--- +### Suspicious Rundll32 dllregisterserver +The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-09 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_dllregisterserver_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/seedworm-apt-iran-middle-east +* https://github.com/pan-unit42/tweets/blob/master/2020-12-10-IOCs-from-Ursnif-infection-with-Delf-variant.txt +* https://www.crowdstrike.com/blog/duck-hunting-with-falcon-complete-qakbot-zip-based-campaign/ +* https://msdn.microsoft.com/en-us/library/windows/desktop/ms682162(v=vs.85).aspx + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Illegal Access To User Content via PowerSploit modules +This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1021, T1113, T1123, T1563 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-HttpStatus/)=true OR match_regex(cmd_line, /(?i)Get-Keystrokes/)=true OR match_regex(cmd_line, /(?i)Get-MicrophoneAudio/)=true OR match_regex(cmd_line, /(?i)Get-NetRDPSession/)=true OR match_regex(cmd_line, /(?i)Get-TimedScreenshot/)=true OR match_regex(cmd_line, /(?i)Get-WebConfig/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021 | x | x | +| T1113 | x | x | +| T1123 | x | x | +| T1563 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Reconnaissance of Access and Persistence Opportunities via PowerSploit modules +This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1053, T1068, T1078, T1543, T1547, T1574 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-LocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainAcl/)=true OR match_regex(cmd_line, /(?i)Invoke-ACLScanner/)=true OR match_regex(cmd_line, /(?i)Find-PathDLLHijack/)=true OR match_regex(cmd_line, /(?i)Find-ProcessDLLHijack/)=true OR match_regex(cmd_line, /(?i)Get-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-DomainPolicy/)=true OR match_regex(cmd_line, /(?i)Get-ModifiablePath/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableRegistryAutoRun/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableScheduledTaskFile/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableService/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableServiceFile/)=true OR match_regex(cmd_line, /(?i)Get-PathAcl/)=true OR match_regex(cmd_line, /(?i)Get-UnattendedInstallFile/)=true OR match_regex(cmd_line, /(?i)Get-UnquotedService/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053 | x | x | +| T1068 | x | x | +| T1078 | x | x | +| T1543 | x | x | +| T1547 | x | x | +| T1574 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Shim Database File Creation +This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1546.011 +- **Last Updated**: 2020-12-08 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1546.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+--- +### Processes launching netsh +This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.004 +- **Last Updated**: 2020-07-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`processes_launching_netsh_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+--- +### Credential Extraction indicative of use of DSInternals credential conversion modules +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)ConvertFrom-ADManagedPasswordBlob/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-UnicodePassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-KerberosKey/)=true OR match_regex(cmd_line, /(?i)ConvertTo-LMHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-NTHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-OrgIdHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-UnicodePassword/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* process_name +* parent_process_name +* _time +* process_path +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/MichaelGrafnetter/DSInternals + +#### Test Dataset + +_version_: 1 +
+--- +### Schtasks scheduling job on remote system +This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1053.005 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053.005 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Certutil exe certificate extraction +This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation + +#### Known False Positives +Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log + +_version_: 1 +
+--- +### Unusually Long Command Line +Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-10-06 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) | where cmd_line!=null and dest_user_id!=null | eval cmd_line_norm=replace(cast(cmd_line, "string"), /\s(--?\w+)|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) | select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input | adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 | where label AND quantile>0.99 | first_time_event input_columns=["dest_device_id", "cmd_line"] | where first_time_dest_device_id_cmd_line | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting sysmon endpoint data that monitors command lines. + +#### Required fields +* process_name +* _time +* dest_device_id +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +This detection may flag suspiciously long command lines when there is not sufficient evidence (samples) for a given process that this detection is tracking; or when there is high variability in the length of the command line for the tracked process. Also, some legitimate applications may use long command lines. Such is the case of Ansible, that encodes Powershell scripts using long base64. Attackers may use this technique to obfuscate their payloads. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Reconnaissance of Connectivity via PowerSploit modules +This detection identifies access to PowerSploit modules for reconnaissance of connectivity. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1021.002, T1135, T1039 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainDNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DomainDNSZone/)=true OR match_regex(cmd_line, /(?i)Get-DNSZone/)=true OR match_regex(cmd_line, /(?i)Invoke-ReverseDnsLookup/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegCachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-CachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegProxy/)=true OR match_regex(cmd_line, /(?i)Get-Proxy/)=true OR match_regex(cmd_line, /(?i)Invoke-Portscan/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | +| T1135 | x | x | +| T1039 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Create Remote Thread into LSASS +Detect remote thread creation into LSASS consistent with credential dumping. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2019-12-06 + +
+ View + +#### Search +``` +`sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `create_remote_thread_into_lsass_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Reconnaissance and Access to Shared Resources via PowerSploit modules +This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1021.002, T1135, T1039 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainShare/)=true OR match_regex(cmd_line, /(?i)Invoke-ShareFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainShareFile/)=true OR match_regex(cmd_line, /(?i)Invoke-FileFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingFile/)=true OR match_regex(cmd_line, /(?i)Get-DomainDFSShare/)=true OR match_regex(cmd_line, /(?i)Get-DFSshare/)=true OR match_regex(cmd_line, /(?i)Get-NetShare/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | +| T1135 | x | x | +| T1039 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Attempted Credential Dump From Registry via Reg exe +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.002 +- **Last Updated**: 2019-12-02 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Detect processes used for System Network Configuration Discovery +This search looks for fast execution of processes used for system network configuration discovery on the endpoint. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1016 +- **Last Updated**: 2020-11-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime lastTime dest user process_name process parent_process eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1016 | x | x | + +#### Kill Chain Phases +* Installation +* Command and Control +* Actions on Objectives + +#### Known False Positives +It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/discovery_commands/windows-sysmon.log + +_version_: 2 +
+--- +### Setting Credentials via DSInternals modules +This detection identifies illegal setting of credentials via DSInternals modules. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1068, T1078, T1098 +- **Last Updated**: 2020-11-03 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-ADDBSidHistory/)=true OR match_regex(cmd_line, /(?i)Add-ADReplNgcKey/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPassword/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-ADDBBootKey/)=true OR match_regex(cmd_line, /(?i)Set-SamAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-AzureADUserEx/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* process_name +* parent_process_name +* _time +* process_path +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | +| T1078 | x | x | +| T1098 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/MichaelGrafnetter/DSInternals + +#### Test Dataset + +_version_: 1 +
+--- +### Common Ransomware Extensions +The search looks for file modifications with extensions commonly used by Ransomware + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1485 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` | `common_ransomware_extensions_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\ +This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ +1. \ +1. **Label:** File Extension, **Field:** file_extension\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1485 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_extensions/windows-sysmon.log + +_version_: 4 +
+--- +### Assessment of Credential Strength via DSInternals modules +This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1078, T1098, T1087, T1201, T1552, T1555 +- **Last Updated**: 2020-11-03 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-PasswordQuality/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | +| T1098 | x | x | +| T1087 | x | x | +| T1201 | x | x | +| T1552 | x | x | +| T1555 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/MichaelGrafnetter/DSInternals + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Rundll32 Application Control Bypass - setupapi +The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-04 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___setupapi_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use setupapi triggering a false positive. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://lolbas-project.github.io/lolbas/Libraries/Setupapi/ +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Suspicious microsoft workflow compiler rename +The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1127, T1036.003 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_microsoft_workflow_compiler_rename_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1127, T1036.003 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive. + +#### References +* https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log + +_version_: 1 +
+--- +### Execution of File with Multiple Extensions +This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1036.003 +- **Last Updated**: 2020-11-18 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_multiple_extensions_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+--- +### Illegal Management of Active Directory Elements and Policies via DSInternals modules +This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1098, T1207, T1484 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Remove-ADDBObject/)=true OR match_regex(cmd_line, /(?i)Set-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Set-ADDBPrimaryGroup/)=true OR match_regex(cmd_line, /(?i)Set-LsaPolicyInformation/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1098 | x | x | +| T1207 | x | x | +| T1484 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/MichaelGrafnetter/DSInternals + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Prohibited Applications Spawning cmd exe +This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.003 +- **Last Updated**: 2020-11-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] | `detect_prohibited_applications_spawning_cmd_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.003 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/powershell_spawn_cmd/windows-sysmon.log + +_version_: 5 +
+--- +### Illegal Service and Process Control via PowerSploit modules +This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1055, T1106, T1569 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Install-SSP/)=true OR match_regex(cmd_line, /(?i)Set-CriticalProcess/)=true OR match_regex(cmd_line, /(?i)Install-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Restore-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Write-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Set-ServiceBinaryPath/)=true OR match_regex(cmd_line, /(?i)Invoke-ReflectivePEInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-DllInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-ServiceAbuse/)=true OR match_regex(cmd_line, /(?i)Invoke-Shellcode/)=true OR match_regex(cmd_line, /(?i)Invoke-WScriptUACBypass/)=true OR match_regex(cmd_line, /(?i)Invoke-WmiCommand/)=true OR match_regex(cmd_line, /(?i)Write-HijackDll/)=true OR match_regex(cmd_line, /(?i)Add-ServiceDacl/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1055 | x | x | +| T1106 | x | x | +| T1569 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Computer Changed with Anonymous Account +This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1210 +- **Last Updated**: 2020-09-18 + +
+ View + +#### Search +``` +`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 | stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event Logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1210 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None thus far found + +#### References +* https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/ + +#### Test Dataset + +_version_: 1 +
+--- +### Suspicious writes to windows Recycle Bin +This search detects writes to the recycle bin by a process other than explorer.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1036 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`| search [| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")` | table process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/write_to_recycle_bin/windows-sysmon.log + +_version_: 4 +
+--- +### BCDEdit Failure Recovery Modification +This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1490 +- **Last Updated**: 2020-12-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bcdedit_failure_recovery_modification_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1490 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators may modify the boot configuration. + +#### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Access LSASS Memory for Dump Creation +Detect memory dumping of the LSASS process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2019-12-06 + +
+ View + +#### Search +``` +`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 2 +
+--- +### Ntdsutil export ntds +Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \ +ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ +This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2021-01-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `ntdsutil_export_ntds_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives. + +#### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil +* https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11) +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf +* https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Detect Regsvcs with No Command Line Arguments +The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-12 + +
+ View + +#### Search +``` +`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) | regex CommandLine="(regsvcs\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_no_command_line_arguments_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Credential Extraction indicative of use of DSInternals modules +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ADDBBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Get-ADDBKdsRootKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBSchemaAttribute/)=true OR match_regex(cmd_line, /(?i)Get-ADKeyCredential/)=true OR match_regex(cmd_line, /(?i)Get-ADReplAccount/)=true OR match_regex(cmd_line, /(?i)Get-ADReplBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADSIAccount/)=true OR match_regex(cmd_line, /(?i)Get-AzureADUserEx/)=true OR match_regex(cmd_line, /(?i)Get-BootKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaPolicyInformation/)=true OR match_regex(cmd_line, /(?i)Get-SamPasswordPolicy/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* process_name +* parent_process_name +* _time +* process_path +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/MichaelGrafnetter/DSInternals + +#### Test Dataset + +_version_: 1 +
+--- +### Attempted Credential Dump From Registry via Reg exe +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-6-04 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) | where process_name="cmd.exe" OR process_name="reg.exe" | where cmd_line != null AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting windows endpoint data that tracks process activity, including parent-child relationships from your endpoints. + +#### Required fields +* process_name +* _time +* dest_device_id +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/splunk/security_content/blob/55a17c65f9f56c2220000b62701765422b46125d/detections/attempted_credential_dump_from_registry_via_reg_exe.yml + +#### Test Dataset + +_version_: 1 +
+--- +### Reg exe Manipulating Windows Services Registry Keys +The search looks for reg.exe modifying registry keys that define Windows services and their configurations. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1574.011 +- **Last Updated**: 2020-11-26 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1574.011 | x | x | + +#### Kill Chain Phases +* Installation + +#### Known False Positives +It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log + +_version_: 5 +
+--- +### Suspicious Rundll32 Rename +The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011, T1036.003 +- **Last Updated**: 2021-02-04 + +
+ View + +#### Search +``` +`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_rundll32_rename_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | +| T1036.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Credential Dumping via Symlink to Shadow Copy +This search detects the creation of a symlink to a shadow copy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2019-12-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_symlink_to_shadow_copy_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Malicious PowerShell Process - Encoded Command +This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1027 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `malicious_powershell_process___encoded_command_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1027 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +System administrators may use this option, but it's not common. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Reconnaissance of Defensive Tools via PowerSploit modules +This detection identifies use of PowerSploit modules for assessment of presence of defensive tools. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1595.002, T1592.002 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-AVSignature/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1595.002 | x | x | +| T1592.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### File with Samsam Extension +The search looks for file writes with extensions consistent with a SamSam ransomware attack. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-12-14 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml | `file_with_samsam_extension_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation + +#### Known False Positives +Because these extensions are not typically used in normal operations, you should investigate all results. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/samsam_extension/windows-sysmon.log + +_version_: 1 +
+--- +### Script Execution via WMI +This search looks for scripts launched via WMI. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1047 +- **Last Updated**: 2020-03-16 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `script_execution_via_wmi_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1047 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/execution_scrcons/windows-sysmon.log + +_version_: 3 +
+--- +### Process Execution via WMI +This search looks for processes launched via WMI. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1047 +- **Last Updated**: 2020-03-16 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `process_execution_via_wmi_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1047 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, administrators may use wmi to execute commands for legitimate purposes. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+--- +### Illegal Privilege Elevation via Mimikatz modules +This detection identifies use of Mimikatz modules for illegal privilege elevation. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1134, T1548 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)privilege::debug/)=true OR match_regex(cmd_line, /(?i)token::elevate/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1134 | x | x | +| T1548 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Regsvcs with Network Connection +The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-16 + +
+ View + +#### Search +``` +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_network_connection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Credential Extraction indicative of FGDump and CacheDump with s option +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND process_name != null AND parent_process_name != null AND match_regex(parent_process_name, /(?i)System32\\services.exe/)=true AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-s/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* process_name +* parent_process_name +* _time +* process_path +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Monitor Registry Keys for Print Monitors +This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1547.010 +- **Last Updated**: 2020-11-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `monitor_registry_keys_for_print_monitors_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1547.010 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +You will encounter noise from legitimate print-monitor registry entries. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.010/atomic_red_team/windows-sysmon.log + +_version_: 2 +
+--- +### Shim Database Installation With Suspicious Parameters +This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1546.011 +- **Last Updated**: 2020-11-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1546.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Suspicious Regsvr32 Register Suspicious Path +Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.010 +- **Last Updated**: 2021-01-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_regsvr32_register_suspicious_path_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.010 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues. + +#### References +* https://attack.mitre.org/techniques/T1218/010/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ +* https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 +* https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Detect Regsvr32 Application Control Bypass +Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ +Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.010 +- **Last Updated**: 2021-01-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_regsvr32_application_control_bypass_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.010 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Limited false positives related to third party software registering .DLL's. + +#### References +* https://attack.mitre.org/techniques/T1218/010/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ +* https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules +This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1199, T1482, T1590, T1591, T1595 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainSID/)=true OR match_regex(cmd_line, /(?i)Get-DomainSite/)=true OR match_regex(cmd_line, /(?i)Get-NetSite/)=true OR match_regex(cmd_line, /(?i)Get-DomainSubnet/)=true OR match_regex(cmd_line, /(?i)Get-NetSubnet/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrustMapping/)=true OR match_regex(cmd_line, /(?i)Invoke-MapDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-Forest/)=true OR match_regex(cmd_line, /(?i)Get-NetForest/)=true OR match_regex(cmd_line, /(?i)Get-ForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-NetForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-ForestGlobalCatalog/)=true OR match_regex(cmd_line, /(?i)Get-NetForestCatalog/)=true OR match_regex(cmd_line, /(?i)Get-ForestTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetForestTrust/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1199 | x | x | +| T1482 | x | x | +| T1590 | x | x | +| T1591 | x | x | +| T1595 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Probing Access with Stolen Credentials via PowerSploit modules +This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1078, T1098 +- **Last Updated**: 2020-11-04 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-AdminAccess/)=true OR match_regex(cmd_line, /(?i)Invoke-CheckLocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Test-ServiceDaclPermission/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_user_id +* dest_device_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | +| T1098 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Unusually Long Command Line - MLTK +Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2019-05-08 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime lastTime user dest process_name process processlen count | `unusually_long_command_line___mltk_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Credential Dumping through LSASS access +This search looks for reading lsass memory consistent with credential dumping. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2019-12-03 + +
+ View + +#### Search +``` +`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_credential_dumping_through_lsass_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+--- +### Detect Rundll32 Inline HTA Execution +The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.005 +- **Last Updated**: 2021-01-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_rundll32_inline_hta_execution_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.005 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +#### References +* https://github.com/redcanaryco/AtomicTestHarnesses +* https://redcanary.com/blog/introducing-atomictestharnesses/ +* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Illegal Service and Process Control via Mimikatz modules +This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1055, T1106, T1569 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::start/)=true OR match_regex(cmd_line, /(?i)service::\+/)=true OR match_regex(cmd_line, /(?i)service::\-/)=true OR match_regex(cmd_line, /(?i)service::start/)=true OR match_regex(cmd_line, /(?i)service::stop/)=true OR match_regex(cmd_line, /(?i)service::suspend/)=true OR match_regex(cmd_line, /(?i)misc::memssp/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1055 | x | x | +| T1106 | x | x | +| T1569 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+--- +### Registry Keys for Creating SHIM Databases +This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1546.011 +- **Last Updated**: 2020-11-26 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_for_creating_shim_databases_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response product, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1546.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+--- +### Short Lived Windows Accounts +This search detects accounts that were created and deleted in a short time period. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136.001 +- **Last Updated**: 2020-07-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 | transaction user connected=false maxspan=240m | table firstTime lastTime count user dest result_id | `short_lived_windows_accounts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/ + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136.001 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + +_version_: 2 +
+--- +### Credential Extraction indicative of use of PowerSploit modules +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ApplicationHost/)=true OR match_regex(cmd_line, /(?i)Get-CachedGPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-GPPAutologon/)=true OR match_regex(cmd_line, /(?i)Get-GPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-RegistryAutoLogon/)=true OR match_regex(cmd_line, /(?i)Get-SiteListPassword/)=true OR match_regex(cmd_line, /(?i)Get-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Request-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Get-VaultCredential/)=true OR match_regex(cmd_line, /(?i)Invoke-Kerberoast/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Windows Event Log Cleared +This search looks for Windows events that indicate one of the Windows event logs has been purged. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1070.001 +- **Last Updated**: 2020-07-06 + +
+ View + +#### Search +``` +(`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_log_cleared_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting Windows event logs from your hosts. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1070.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It is possible that these logs may be legitimately cleared by Administrators. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-system.log + +_version_: 4 +
+--- +### Detect Prohibited Applications Spawning cmd exe +This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. This is a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe` by @bpatel. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1059 +- **Last Updated**: 2020-7-13 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) +| where process_name="cmd.exe" | rex field=parent_process "(?[^\\\\]+)$" | where field0="winword.exe" OR field0="excel.exe" OR field0="outlook.exe" OR field0="powerpnt.exe" OR field0="visio.exe" OR field0="mspub.exe" OR field0="acrobat.exe" OR field0="acrord32.exe" OR field0="chrome.exe" OR field0="iexplore.exe" OR field0="opera.exe" OR field0="firefox.exe" OR field0="java.exe" OR field0="powershell.exe" +| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body="TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting sysmon logs. This search has been modified to process raw sysmon data from attack_range's nxlogs on DSP. + +#### Required fields +* process_name +* parent_process_name +* _time +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Unload Sysmon Filter Driver +Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.001 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` |`unload_sysmon_filter_driver_filter`| table firstTime lastTime dest user count process_name process_id parent_process_name process +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives + + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+--- +### Unusually Long Command Line +Command lines that are extremely long may be indicative of malicious activity on your hosts. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-12-08 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process | `unusually_long_command_line_filter` |eval threshold = 3 | where maxlen > ((threshold*stdevperhost) + avgperhost) +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the process field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Some legitimate applications start with long command lines. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+--- +### Detect Rundll32 Application Control Bypass - advpack +The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-04 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___advpack_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://lolbas-project.github.io/lolbas/Libraries/Advpack/ +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules +This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1078, T1087, T1484 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Invoke-EnumerateLocalAdmin/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Invoke-EventHunter/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserLocation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserHunter/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignGroupMember/)=true OR match_regex(cmd_line, /(?i)Find-ForeignGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignUser/)=true OR match_regex(cmd_line, /(?i)Find-ForeignUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPO/)=true OR match_regex(cmd_line, /(?i)Get-NetGPO/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOComputerLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOComputerAdmin/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGPOGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOUserLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOLocation/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-DomainManagedSecurityGroup/)=true OR match_regex(cmd_line, /(?i)Find-ManagedSecurityGroups/)=true OR match_regex(cmd_line, /(?i)Get-DomainOU/)=true OR match_regex(cmd_line, /(?i)Get-NetOU/)=true OR match_regex(cmd_line, /(?i)Get-DomainUser/)=true OR match_regex(cmd_line, /(?i)Get-NetUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Get-UserEvent/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetLoggedon/)=true OR match_regex(cmd_line, /(?i)Get-RegLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegLastLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-LastLoggedOn/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | +| T1087 | x | x | +| T1484 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Create local admin accounts using net exe +This search looks for the creation of local administrator accounts using net.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`create_local_admin_accounts_using_net_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators often leverage net.exe to create admin accounts. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Reconnaissance and Access to Computers via Mimikatz modules +This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1592 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::ServerInfo/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1592 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Regasm with Network Connection +The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-16 + +
+ View + +#### Search +``` +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_network_connection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### System Process Running from Unexpected Location +An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1036 +- **Last Updated**: 2020-08-25 + +
+ View + +#### Search +``` + $ssa_input = | from read_ssa_enriched_events() | eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), user=ucast(map_get(input_event, "dest_user_id"), "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=lower(ucast(map_get(input_event, "process_path"), "string", null)); +$cond_1 = | from $ssa_input | where process_name="arp.exe" OR process_name="adaptertroubleshooter.exe" OR process_name="applicationframehost.exe" OR process_name="atbroker.exe" OR process_name="authhost.exe" OR process_name="autoworkplace.exe" OR process_name="axinstui.exe" OR process_name="backgroundtransferhost.exe" OR process_name="bdehdcfg.exe" OR process_name="bdeuisrv.exe" OR process_name="bdeunlockwizard.exe" OR process_name="bitlockerdeviceencryption.exe" OR process_name="bitlockerwizard.exe" OR process_name="bitlockerwizardelev.exe" OR process_name="bytecodegenerator.exe" OR process_name="camerasettingsuihost.exe" OR process_name="castsrv.exe" OR process_name="certenrollctrl.exe" OR process_name="checknetisolation.exe" OR process_name="clipup.exe" OR process_name="cloudexperiencehostbroker.exe" OR process_name="cloudnotifications.exe" OR process_name="cloudstoragewizard.exe" OR process_name="compmgmtlauncher.exe" OR process_name="compattelrunner.exe" OR process_name="computerdefaults.exe" OR process_name="credentialuibroker.exe" OR process_name="dfdwiz.exe" OR process_name="dwwin.exe" OR process_name="dataexchangehost.exe" OR process_name="defrag.exe" OR process_name="devicedisplayobjectprovider.exe" OR process_name="deviceeject.exe" OR process_name="deviceenroller.exe" OR process_name="devicepairingwizard.exe" OR process_name="deviceproperties.exe" OR process_name="disksnapshot.exe" OR process_name="dism.exe" OR process_name="displayswitch.exe" OR process_name="dmnotificationbroker.exe" OR process_name="dmomacpmo.exe" OR process_name="dpiscaling.exe" OR process_name="dsmusertask.exe" OR process_name="dxpserver.exe" OR process_name="edpcleanup.exe" OR process_name="eosnotify.exe" OR process_name="eap3host.exe" OR process_name="easpoliciesbrokerhost.exe" OR process_name="easeofaccessdialog.exe" OR process_name="ehstorauthn.exe" OR process_name="fxscover.exe" OR process_name="fxssvc.exe" OR process_name="fxsunatd.exe" OR process_name="filehistory.exe" OR process_name="fondue.exe" OR process_name="gamepanel.exe" OR process_name="genvalobj.exe" OR process_name="gettingstarted.exe" OR process_name="hostname.exe" OR process_name="icsentitlementhost.exe" OR process_name="infdefaultinstall.exe" OR process_name="installagent.exe" OR process_name="languagecomponentsinstallercomhandler.exe" OR process_name="launchtm.exe" OR process_name="launchwinapp.exe" OR process_name="legacynetuxhost.exe" OR process_name="licensemanagershellext.exe" OR process_name="licensingui.exe" OR process_name="locationnotificationwindows.exe" OR process_name="locationnotifications.exe" OR process_name="locator.exe" OR process_name="lockapphost.exe" OR process_name="lockscreencontentserver.exe" OR process_name="logonui.exe" OR process_name="lsaiso.exe" OR process_name="mdeserver.exe" OR process_name="mdmagent.exe" OR process_name="mdmappinstaller.exe" OR process_name="mrinfo.exe" OR process_name="mrt.exe" OR process_name="mschedexe.exe" OR process_name="magnify.exe" OR process_name="mbaeparsertask.exe" OR process_name="mdres.exe" OR process_name="mdsched.exe" OR process_name="migautoplay.exe" OR process_name="mpsigstub.exe" OR process_name="msspellcheckinghost.exe" OR process_name="muiunattend.exe" OR process_name="multidigimon.exe" OR process_name="musnotification.exe" OR process_name="musnotificationux.exe" OR process_name="napstat.exe" OR process_name="netstat.exe" OR process_name="narrator.exe" OR process_name="netcfgnotifyobjecthost.exe" OR process_name="netevtfwdr.exe" OR process_name="netproj.exe" OR process_name="netplwiz.exe" OR process_name="networkuxbroker.exe"; +$cond_2 = | from $ssa_input | where process_name="openwith.exe" OR process_name="optionalfeatures.exe" OR process_name="pathping.exe" OR process_name="ping.exe" OR process_name="passwordonwakesettingflyout.exe" OR process_name="pickerhost.exe" OR process_name="pkgmgr.exe" OR process_name="pnpunattend.exe" OR process_name="pnputil.exe" OR process_name="presentationhost.exe" OR process_name="presentationsettings.exe" OR process_name="printbrmui.exe" OR process_name="printdialoghost.exe" OR process_name="printdialoghost3d.exe" OR process_name="printisolationhost.exe" OR process_name="proximityuxhost.exe" OR process_name="rdspnf.exe" OR process_name="rmactivate.exe" OR process_name="rmactivate_isv.exe" OR process_name="rmactivate_ssp.exe" OR process_name="rmactivate_ssp_isv.exe" OR process_name="route.exe" OR process_name="rdpsa.exe" OR process_name="rdpsaproxy.exe" OR process_name="rdpsauachelper.exe" OR process_name="reagentc.exe" OR process_name="recoverydrive.exe" OR process_name="register-cimprovider.exe" OR process_name="registeriepkeys.exe" OR process_name="relpost.exe" OR process_name="remoteposworker.exe" OR process_name="rmclient.exe" OR process_name="robocopy.exe" OR process_name="rpcping.exe" OR process_name="runlegacycplelevated.exe" OR process_name="runtimebroker.exe" OR process_name="sihclient.exe" OR process_name="searchfilterhost.exe" OR process_name="searchindexer.exe" OR process_name="searchprotocolhost.exe" OR process_name="secedit.exe" OR process_name="sensordataservice.exe" OR process_name="setieinstalleddate.exe" OR process_name="settingsynchost.exe" OR process_name="slidetoshutdown.exe" OR process_name="smartscreensettings.exe" OR process_name="sndvol.exe" OR process_name="snippingtool.exe" OR process_name="soundrecorder.exe" OR process_name="spaceagent.exe" OR process_name="sppextcomobj.exe" OR process_name="srtasks.exe" OR process_name="stikynot.exe" OR process_name="synchost.exe" OR process_name="sysreseterr.exe" OR process_name="systempropertiesadvanced.exe" OR process_name="systempropertiescomputername.exe" OR process_name="systempropertiesdataexecutionprevention.exe" OR process_name="systempropertieshardware.exe" OR process_name="systempropertiesperformance.exe" OR process_name="systempropertiesprotection.exe" OR process_name="systempropertiesremote.exe" OR process_name="systemsettingsadminflows.exe" OR process_name="systemsettingsbroker.exe" OR process_name="systemsettingsremovedevice.exe" OR process_name="tcpsvcs.exe" OR process_name="tracert.exe" OR process_name="tstheme.exe" OR process_name="tswbprxy.exe" OR process_name="tapiunattend.exe" OR process_name="taskmgr.exe" OR process_name="thumbnailextractionhost.exe" OR process_name="tokenbrokercookies.exe" OR process_name="tpminit.exe" OR process_name="tswpfwrp.exe" OR process_name="ui0detect.exe" OR process_name="upgraderesultsui.exe" OR process_name="useraccountbroker.exe" OR process_name="useraccountcontrolsettings.exe" OR process_name="usoclient.exe" OR process_name="utilman.exe" OR process_name="vssvc.exe" OR process_name="vaultcmd.exe" OR process_name="vaultsysui.exe" OR process_name="wfs.exe" OR process_name="wmpdmc.exe" OR process_name="wpdshextautoplay.exe" OR process_name="wscollect.exe" OR process_name="wsmanhttpconfig.exe" OR process_name="wsreset.exe" OR process_name="wudfhost.exe" OR process_name="wwahost.exe" OR process_name="wallpaperhost.exe" OR process_name="webcache.exe" OR process_name="werfault.exe" OR process_name="werfaultsecure.exe" OR process_name="winsat.exe" OR process_name="windows.media.backgroundplayback.exe" OR process_name="windowsactiondialog.exe" OR process_name="windowsanytimeupgrade.exe" OR process_name="windowsanytimeupgraderesults.exe"; +$cond_3 = | from $ssa_input | where process_name="windowsanytimeupgradeui.exe" OR process_name="windowsupdateelevatedinstaller.exe" OR process_name="workfolders.exe" OR process_name="wpcmon.exe" OR process_name="acu.exe" OR process_name="aitagent.exe" OR process_name="aitstatic.exe" OR process_name="alg.exe" OR process_name="appidcertstorecheck.exe" OR process_name="appidpolicyconverter.exe" OR process_name="at.exe" OR process_name="attrib.exe" OR process_name="audiodg.exe" OR process_name="auditpol.exe" OR process_name="autochk.exe" OR process_name="autoconv.exe" OR process_name="autofmt.exe" OR process_name="baaupdate.exe" OR process_name="backgroundtaskhost.exe" OR process_name="bcastdvr.exe" OR process_name="bcdboot.exe" OR process_name="bcdedit.exe" OR process_name="bdechangepin.exe" OR process_name="bdeunlock.exe" OR process_name="bitsadmin.exe" OR process_name="bootcfg.exe" OR process_name="bootim.exe" OR process_name="bootsect.exe" OR process_name="bridgeunattend.exe" OR process_name="browser_broker.exe" OR process_name="bthudtask.exe" OR process_name="cacls.exe" OR process_name="calc.exe" OR process_name="cdpreference.exe" OR process_name="certreq.exe" OR process_name="certutil.exe" OR process_name="change.exe" OR process_name="changepk.exe" OR process_name="charmap.exe" OR process_name="chglogon.exe" OR process_name="chgport.exe" OR process_name="chgusr.exe" OR process_name="chkdsk.exe" OR process_name="chkntfs.exe" OR process_name="choice.exe" OR process_name="cipher.exe" OR process_name="cleanmgr.exe" OR process_name="cliconfg.exe" OR process_name="clip.exe" OR process_name="cmd.exe" OR process_name="cmdkey.exe" OR process_name="cmdl32.exe" OR process_name="cmmon32.exe" OR process_name="cmstp.exe" OR process_name="cofire.exe" OR process_name="colorcpl.exe" OR process_name="comp.exe" OR process_name="compact.exe" OR process_name="conhost.exe" OR process_name="consent.exe" OR process_name="control.exe" OR process_name="convert.exe" OR process_name="credwiz.exe" OR process_name="cscript.exe" OR process_name="csrss.exe" OR process_name="ctfmon.exe" OR process_name="cttune.exe" OR process_name="cttunesvr.exe" OR process_name="dashost.exe" OR process_name="dccw.exe" OR process_name="dcomcnfg.exe" OR process_name="ddodiag.exe" OR process_name="dfrgui.exe" OR process_name="dialer.exe" OR process_name="diantz.exe" OR process_name="dinotify.exe" OR process_name="diskpart.exe" OR process_name="diskperf.exe" OR process_name="diskraid.exe" OR process_name="dispdiag.exe" OR process_name="djoin.exe" OR process_name="dllhost.exe" OR process_name="dllhst3g.exe" OR process_name="dmcertinst.exe" OR process_name="dmcfghost.exe" OR process_name="dmclient.exe" OR process_name="dnscacheugc.exe" OR process_name="doskey.exe" OR process_name="dpapimig.exe" OR process_name="dpnsvr.exe" OR process_name="driverquery.exe" OR process_name="drvcfg.exe" OR process_name="drvinst.exe" OR process_name="dsregcmd.exe" OR process_name="dstokenclean.exe" OR process_name="dvdplay.exe" OR process_name="dvdupgrd.exe" OR process_name="dwm.exe" OR process_name="dxdiag.exe" OR process_name="easinvoker.exe" OR process_name="efsui.exe"; +$cond_4 = | from $ssa_input | where process_name="embeddedapplauncher.exe" OR process_name="esentutl.exe" OR process_name="eudcedit.exe" OR process_name="eventcreate.exe" OR process_name="eventvwr.exe" OR process_name="expand.exe" OR process_name="extrac32.exe" OR process_name="fc.exe" OR process_name="fhmanagew.exe" OR process_name="find.exe" OR process_name="findstr.exe" OR process_name="finger.exe" OR process_name="fixmapi.exe" OR process_name="fltmc.exe" OR process_name="fodhelper.exe" OR process_name="fontdrvhost.exe" OR process_name="fontview.exe" OR process_name="forfiles.exe" OR process_name="fsavailux.exe" OR process_name="fsquirt.exe" OR process_name="fsutil.exe" OR process_name="ftp.exe" OR process_name="fvenotify.exe" OR process_name="fveprompt.exe" OR process_name="getmac.exe" OR process_name="gpresult.exe" OR process_name="gpscript.exe" OR process_name="gpupdate.exe" OR process_name="grpconv.exe" OR process_name="hdwwiz.exe" OR process_name="help.exe" OR process_name="hwrcomp.exe" OR process_name="hwrreg.exe" OR process_name="icacls.exe" OR process_name="icardagt.exe" OR process_name="icsunattend.exe" OR process_name="ie4uinit.exe" OR process_name="ieunatt.exe" OR process_name="ieetwcollector.exe" OR process_name="iexpress.exe" OR process_name="immersivetpmvscmgrsvr.exe" OR process_name="ipconfig.exe" OR process_name="irftp.exe" OR process_name="iscsicli.exe" OR process_name="iscsicpl.exe" OR process_name="isoburn.exe" OR process_name="klist.exe" OR process_name="ksetup.exe" OR process_name="ktmutil.exe" OR process_name="label.exe" OR process_name="licensingdiag.exe" OR process_name="lodctr.exe" OR process_name="logagent.exe" OR process_name="logman.exe" OR process_name="logoff.exe" OR process_name="lpkinstall.exe" OR process_name="lpksetup.exe" OR process_name="lpremove.exe" OR process_name="lsass.exe" OR process_name="lsm.exe" OR process_name="makecab.exe" OR process_name="manage-bde.exe" OR process_name="mblctr.exe" OR process_name="mcbuilder.exe" OR process_name="mctadmin.exe" OR process_name="mfpmp.exe" OR process_name="mmc.exe" OR process_name="mobsync.exe" OR process_name="mountvol.exe" OR process_name="mpnotify.exe" OR process_name="msconfig.exe" OR process_name="msdt.exe" OR process_name="msdtc.exe" OR process_name="msfeedssync.exe" OR process_name="msg.exe" OR process_name="mshta.exe" OR process_name="msiexec.exe" OR process_name="msinfo32.exe" OR process_name="mspaint.exe" OR process_name="msra.exe" OR process_name="mstsc.exe" OR process_name="mtstocom.exe" OR process_name="nbtstat.exe" OR process_name="ndadmin.exe" OR process_name="net.exe" OR process_name="net1.exe" OR process_name="netbtugc.exe" OR process_name="netcfg.exe" OR process_name="netiougc.exe" OR process_name="netsh.exe" OR process_name="newdev.exe" OR process_name="nltest.exe" OR process_name="notepad.exe" OR process_name="nslookup.exe" OR process_name="ntoskrnl.exe" OR process_name="ntprint.exe" OR process_name="ocsetup.exe" OR process_name="odbcad32.exe" OR process_name="odbcconf.exe" OR process_name="omadmclient.exe" OR process_name="omadmprc.exe"; +$cond_5 = | from $ssa_input | where process_name="openfiles.exe" OR process_name="osk.exe" OR process_name="p2phost.exe" OR process_name="pcalua.exe" OR process_name="pcaui.exe" OR process_name="pcawrk.exe" OR process_name="pcwrun.exe" OR process_name="perfmon.exe" OR process_name="phoneactivate.exe" OR process_name="plasrv.exe" OR process_name="poqexec.exe" OR process_name="powercfg.exe" OR process_name="prevhost.exe" OR process_name="print.exe" OR process_name="printfilterpipelinesvc.exe" OR process_name="printui.exe" OR process_name="proquota.exe" OR process_name="provtool.exe" OR process_name="psr.exe" OR process_name="pwlauncher.exe" OR process_name="qappsrv.exe" OR process_name="qprocess.exe" OR process_name="query.exe" OR process_name="quser.exe" OR process_name="qwinsta.exe" OR process_name="rasautou.exe" OR process_name="rasdial.exe" OR process_name="raserver.exe" OR process_name="rasphone.exe" OR process_name="rdpclip.exe" OR process_name="rdpinput.exe" OR process_name="rdrleakdiag.exe" OR process_name="recdisc.exe" OR process_name="recover.exe" OR process_name="reg.exe" OR process_name="regedt32.exe" OR process_name="regini.exe" OR process_name="regsvr32.exe" OR process_name="rekeywiz.exe" OR process_name="relog.exe" OR process_name="repair-bde.exe" OR process_name="replace.exe" OR process_name="reset.exe" OR process_name="resmon.exe" OR process_name="rmttpmvscmgrsvr.exe" OR process_name="rrinstaller.exe" OR process_name="rstrui.exe" OR process_name="runas.exe" OR process_name="rundll32.exe" OR process_name="runonce.exe" OR process_name="rwinsta.exe" OR process_name="sbunattend.exe" OR process_name="sc.exe" OR process_name="schtasks.exe" OR process_name="sdbinst.exe" OR process_name="sdchange.exe" OR process_name="sdclt.exe" OR process_name="sdiagnhost.exe" OR process_name="secinit.exe" OR process_name="services.exe" OR process_name="sessionmsg.exe" OR process_name="sethc.exe" OR process_name="setspn.exe" OR process_name="setupcl.exe" OR process_name="setupugc.exe" OR process_name="setx.exe" OR process_name="sfc.exe" OR process_name="shadow.exe" OR process_name="shrpubw.exe" OR process_name="shutdown.exe" OR process_name="sigverif.exe" OR process_name="sihost.exe" OR process_name="slui.exe" OR process_name="smss.exe" OR process_name="snmptrap.exe" OR process_name="sort.exe" OR process_name="spinstall.exe" OR process_name="spoolsv.exe" OR process_name="sppsvc.exe" OR process_name="spreview.exe" OR process_name="srdelayed.exe" OR process_name="subst.exe" OR process_name="svchost.exe" OR process_name="sxstrace.exe" OR process_name="syskey.exe" OR process_name="systeminfo.exe" OR process_name="systemreset.exe" OR process_name="systray.exe" OR process_name="tabcal.exe" OR process_name="takeown.exe" OR process_name="taskeng.exe" OR process_name="taskhost.exe" OR process_name="taskhostw.exe" OR process_name="taskkill.exe" OR process_name="tasklist.exe" OR process_name="taskmgr.exe" OR process_name="tcmsetup.exe" OR process_name="timeout.exe" OR process_name="tpmvscmgr.exe" OR process_name="tpmvscmgrsvr.exe"; +$cond_6 = | from $ssa_input | where process_name="tracerpt.exe" OR process_name="tscon.exe" OR process_name="tsdiscon.exe" OR process_name="tskill.exe" OR process_name="typeperf.exe" OR process_name="tzsync.exe" OR process_name="tzutil.exe" OR process_name="ucsvc.exe" OR process_name="unlodctr.exe" OR process_name="unregmp2.exe" OR process_name="upnpcont.exe" OR process_name="userinit.exe" OR process_name="vds.exe" OR process_name="vdsldr.exe" OR process_name="verclsid.exe" OR process_name="verifier.exe" OR process_name="verifiergui.exe" OR process_name="vmicsvc.exe" OR process_name="vssadmin.exe" OR process_name="w32tm.exe" OR process_name="waitfor.exe" OR process_name="wbadmin.exe" OR process_name="wbengine.exe" OR process_name="wecutil.exe" OR process_name="wermgr.exe" OR process_name="wevtutil.exe" OR process_name="wextract.exe" OR process_name="where.exe" OR process_name="whoami.exe" OR process_name="wiaacmgr.exe" OR process_name="wiawow64.exe" OR process_name="wifitask.exe" OR process_name="wimserv.exe" OR process_name="wininit.exe" OR process_name="winload.exe" OR process_name="winlogon.exe" OR process_name="winresume.exe" OR process_name="winrs.exe" OR process_name="winrshost.exe" OR process_name="winver.exe" OR process_name="wisptis.exe" OR process_name="wkspbroker.exe" OR process_name="wksprt.exe" OR process_name="wlanext.exe" OR process_name="wlrmdr.exe" OR process_name="wowreg32.exe" OR process_name="wpnpinst.exe" OR process_name="wpr.exe" OR process_name="write.exe" OR process_name="wscript.exe" OR process_name="wsmprovhost.exe" OR process_name="wsqmcons.exe" OR process_name="wuapihost.exe" OR process_name="wuapp.exe" OR process_name="wuauclt.exe" OR process_name="wusa.exe" OR process_name="xcopy.exe" OR process_name="xpsrchvw.exe" OR process_name="xwizard.exe"; +| from $cond_1 | union $cond_2 | union $cond_3 | union $cond_4 | union $cond_5 | union $cond_6 | where process_path!="c:\\windows\\system32" AND process_path!="c:\\windows\\syswow64" | eval start_time = timestamp, end_time = timestamp, entities = mvappend(device, user), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +Collect endpoint data such as sysmon or 4688 events. + +#### Required fields +* dest_device_id +* process_name +* _time +* dest_user_id +* process_path + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Rare Parent-Child Process Relationship +An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1203, T1059, T1053, T1072 +- **Last Updated**: 2020-08-13 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) | where parent_process_name!=null | select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id | conditional_anomaly conditional="parent_process_name" target="process_name" | rename output as input | where input < 1 | adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L | where label AND quantile<0.1 AND (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe") +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +Collect endpoint data such as sysmon or 4688 events. + +#### Required fields +* process_name +* parent_process_name +* _time +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1203 | x | x | +| T1059 | x | x | +| T1053 | x | x | +| T1072 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. + + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Setting Credentials via PowerSploit modules +This detection identifies illegal setting of credentials via PowerSploit modules. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1068, T1078, T1098 +- **Last Updated**: 2020-11-03 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainUserPassword/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | +| T1078 | x | x | +| T1098 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Dump LSASS via procdump +Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\ +During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2021-02-01 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://attack.mitre.org/techniques/T1003/001/ +* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Disabling Remote User Account Control +The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1548.002 +- **Last Updated**: 2020-11-18 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1548.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Reconnaissance of Credential Stores and Services via Mimikatz modules +This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1589.001, T1590.001, T1590.003, T1068, T1078, T1098 +- **Last Updated**: 2020-11-03 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)crypto::capi/)=true OR match_regex(cmd_line, /(?i)crypto::cng/)=true OR match_regex(cmd_line, /(?i)crypto::providers/)=true OR match_regex(cmd_line, /(?i)crypto::stores/)=true OR match_regex(cmd_line, /(?i)crypto::sc/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1589.001 | x | x | +| T1590.001 | x | x | +| T1590.003 | x | x | +| T1068 | x | x | +| T1078 | x | x | +| T1098 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+--- +### Creation of Shadow Copy with wmic and powershell +This search detects the use of wmic and Powershell to create a shadow copy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2019-12-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_with_wmic_and_powershell_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Legtimate administrator usage of wmic to create a shadow copy. + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Detect HTML Help Renamed +The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.001 +- **Last Updated**: 2021-02-11 + +
+ View + +#### Search +``` +`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_renamed_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed hh.exe may be used. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed. + +#### References +* https://attack.mitre.org/techniques/T1218/001/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Windows Security Account Manager Stopped +The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1489 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_security_account_manager_stopped_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1489 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log + +_version_: 1 +
+--- +### Reconnaissance and Access to Accounts and Groups via Mimikatz modules +This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1078, T1087, T1484 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::user/)=true OR match_regex(cmd_line, /(?i)net::group/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | +| T1087 | x | x | +| T1484 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Rundll32 Application Control Bypass - syssetup +The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-04 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___syssetup_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://lolbas-project.github.io/lolbas/Libraries/Syssetup/ +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### System Processes Run From Unexpected Locations +This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1036.003 +- **Last Updated**: 2020-12-08 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name Processes.process_hash| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file` | `system_processes_run_from_unexpected_locations_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+--- +### USN Journal Deletion +The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1070 +- **Last Updated**: 2018-12-03 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1070 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/atomic_red_team/windows-sysmon.log + +_version_: 2 +
+--- +### Detect Regasm Spawning a Process +The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_spawning_a_process_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ +* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Credential Extraction indicative of Lazagne command line options +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003, T1555 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)all\s+\-oA\s+\-output/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | +| T1555 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Credential Extraction indicative of FGDump and CacheDump with v option +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null) | where cmd_line != null AND process_name != null AND process_path != null AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-v/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* process_name +* _time +* process_path +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect HTML Help Using InfoTech Storage Handlers +The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.001 +- **Last Updated**: 2021-02-11 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_using_infotech_storage_handlers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed. + +#### References +* https://attack.mitre.org/techniques/T1218/001/ +* https://www.kb.cert.org/vuls/id/851869 +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ +* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 +* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Common Ransomware Notes +The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1485 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_notes` | `common_ransomware_notes_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1485 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible that a legitimate file could be created with the same name used by ransomware note files. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/windows-sysmon.log + +_version_: 4 +
+--- +### Reconnaissance and Access to Processes and Services via Mimikatz modules +This detection identifies use of Mimikatz modules for discovery and access to services and processes. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1007, T1046, T1057 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::list/)=true OR match_regex(cmd_line, /(?i)service::list/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1007 | x | x | +| T1046 | x | x | +| T1057 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+--- +### Sc exe Manipulating Windows Services +This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1543.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1543.003 | x | x | + +#### Kill Chain Phases +* Installation + +#### Known False Positives +Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Overwriting Accessibility Binaries +Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1546.008 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1546.008 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.008/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Detect Regsvcs Spawning a Process +The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_spawning_a_process_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Detect MSHTA Url in Command Line +This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.005 +- **Last Updated**: 2021-01-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.005 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +It is possible legitimate applications may perform this behavior and will need to be filtered. + +#### References +* https://github.com/redcanaryco/AtomicTestHarnesses +* https://redcanary.com/blog/introducing-atomictestharnesses/ +* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### WBAdmin Delete System Backups +This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1490 +- **Last Updated**: 2021-01-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `wbadmin_delete_system_backups_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1490 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators may modify the boot configuration. + +#### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md +* https://thedfirreport.com/2020/10/08/ryuks-return/ +* https://attack.mitre.org/techniques/T1490/ +* https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Illegal Management of Computers and Active Directory Elements via PowerSploit modules +This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1098, T1207, T1484 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainObject/)=true OR match_regex(cmd_line, /(?i)Set-ADObject/)=true OR match_regex(cmd_line, /(?i)Set-DomainObjectOwner/)=true OR match_regex(cmd_line, /(?i)Set-MasterBootRecord/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1098 | x | x | +| T1207 | x | x | +| T1484 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Suspicious Reg exe Process +This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1112 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup process_id| table process_id dest] | `suspicious_reg_exe_process_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1112 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. + +#### References +* https://car.mitre.org/wiki/CAR-2013-03-001 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Detect New Local Admin account +This search looks for newly created accounts that have been elevated to local administrators. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136.001 +- **Last Updated**: 2020-07-08 + +
+ View + +#### Search +``` +`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) | transaction member_id connected=false maxspan=180m | rename member_id as user | stats count min(_time) as firstTime max(_time) as lastTime by user dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_local_admin_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732 + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives +* Command and Control + +#### Known False Positives +The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + +_version_: 2 +
+--- +### Reconnaissance and Access to Computers and Domains via PowerSploit modules +This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1592, T1590, T1087 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ComputerDetail/)=true OR match_regex(cmd_line, /(?i)Get-Domain/)=true OR match_regex(cmd_line, /(?i)Get-NetDomain/)=true OR match_regex(cmd_line, /(?i)Get-DomainComputer/)=true OR match_regex(cmd_line, /(?i)Get-NetComputer/)=true OR match_regex(cmd_line, /(?i)Get-DomainController/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainController/)=true OR match_regex(cmd_line, /(?i)Get-DomainFileServer/)=true OR match_regex(cmd_line, /(?i)Get-NetFileServer/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1592 | x | x | +| T1590 | x | x | +| T1087 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Suspicious msbuild path +The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1127.001, T1036.003 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `suspicious_msbuild_path_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1127.001 | x | x | +| T1036.003 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on. + +#### References +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + +_version_: 1 +
+--- +### Credential Extraction native Microsoft debuggers peek into the kernel +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND parent_process_name != null AND process_name != null AND ( match_regex(parent_process_name, /(?i)ntkd\.exe/)=true OR match_regex(parent_process_name, /(?i)livekd\.exe/)=true ) AND match_regex(process_name, /(?i)conhost\.exe/)=true AND match_regex(cmd_line, /(?i)0xffffffff/)=true AND match_regex(cmd_line, /(?i)\-ForceV1/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* process_name +* parent_process_name +* _time +* dest_device_id +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. + +#### References +* https://medium.com/@clermont1050/covid-19-cyber-infection-c615ead7c29 + +#### Test Dataset + +_version_: 1 +
+--- +### Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass +Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log + +_version_: 6 +
+--- +### Remote Process Instantiation via WMI +This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1047 +- **Last Updated**: 2020-11-30 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1047 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+--- +### Detect mshta inline hta execution +The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.005 +- **Last Updated**: 2021-01-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_inline_hta_execution_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.005 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +#### References +* https://github.com/redcanaryco/AtomicTestHarnesses +* https://redcanary.com/blog/introducing-atomictestharnesses/ +* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+--- +### Samsam Test File Write +The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1486 +- **Last Updated**: 2018-12-14 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1486 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +No false positives have been identified. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/sam_sam_note/windows-sysmon.log + +_version_: 1 +
+--- +### Suspicious microsoft workflow compiler usage +The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1127 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_usage_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1127 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM. + +#### References +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log + +_version_: 1 +
+--- +### Illegal Enabling or Disabling of Accounts via DSInternals modules +This detection identifies use of DSInternals modules that enable or disable accounts illegaly. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1078, T1098 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Disable-ADDBAccount/)=true OR match_regex(cmd_line, /(?i)Enable-ADDBAccount/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | +| T1098 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/MichaelGrafnetter/DSInternals + +#### Test Dataset + +_version_: 1 +
+--- +### Deleting Shadow Copies +The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1490 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `deleting_shadow_copies_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1490 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Reconnaissance and Access to Shared Resources via Mimikatz modules +This detection identifies use of Mimikatz modules for discovery and access to network shares. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1021.002, T1135, T1039 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::share/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | +| T1135 | x | x | +| T1039 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+--- +### Attempt To Stop Security Service +This search looks for attempts to stop security-related services on the endpoint. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |lookup security_services_lookup service as process OUTPUTNEW category, description | search category=security | `attempt_to_stop_security_service_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.001 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +None identified. Attempts to disable security-related services should be identified and understood. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+--- +### RunDLL Loading DLL By Ordinal +This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2020-11-30 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll_loading_dll_by_ordinal_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Installation + +#### Known False Positives +While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Ryuk Test Files Detected +The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1486 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1486 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log + +_version_: 1 +
+--- +### Credential Extraction indicative of use of Mimikatz modules +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)CRYPTO::Certificates/)=true OR match_regex(cmd_line, /(?i)CRYPTO::keys/)=true OR match_regex(cmd_line, /(?i)kerberos::list/)=true OR match_regex(cmd_line, /(?i)kerberos::tgt/)=true OR match_regex(cmd_line, /(?i)lsadump::sam/)=true OR match_regex(cmd_line, /(?i)lsadump::secrets/)=true OR match_regex(cmd_line, /(?i)lsadump::cache/)=true OR match_regex(cmd_line, /(?i)lsadump::lsa/)=true OR match_regex(cmd_line, /(?i)lsadump::trust/)=true OR match_regex(cmd_line, /(?i)lsadump::backupkeys/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+--- +### Credential Dumping via Copy Command from Shadow Copy +This search detects credential dumping using copy command from a shadow copy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2019-12-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_copy_command_from_shadow_copy_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Scheduled Task Deleted Or Created via CMD +This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1053.005 +- **Last Updated**: 2020-12-17 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_deleted_or_created_via_cmd_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053.005 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Tasks should not be manually created via CLI, this is rarely done by admins as well + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+--- +### Suspicious wevtutil Usage +The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1070.001 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `suspicious_wevtutil_usage_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1070.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+--- +### WMI Permanent Event Subscription - Sysmon +This search looks for the creation of WMI permanent event subscriptions. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1546.003 +- **Last Updated**: 2020-12-08 + +
+ View + +#### Search +``` +`sysmon` EventCode=21 | rename host as dest | table _time, dest, user, Operation, EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1546.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, administrators may use event subscriptions for legitimate purposes. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log + +_version_: 2 +
+--- +### Malicious PowerShell Process With Obfuscation Techniques +This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001 +- **Last Updated**: 2021-01-19 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) | `malicious_powershell_process_with_obfuscation_techniques_filter` | search num_obfuscation > 10 +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +These characters might be legitimately on the command-line, but it is not common. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/obfuscated_powershell/windows-sysmon.log + +_version_: 4 +
+--- +### Detect mshta renamed +The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.005 +- **Last Updated**: 2021-01-20 + +
+ View + +#### Search +``` +`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `detect_mshta_renamed_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.005 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive. + +#### References +* https://github.com/redcanaryco/AtomicTestHarnesses +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + +_version_: 1 +
+--- +### Suspicious Rundll32 StartW +The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-04 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_startw_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://www.cobaltstrike.com/help-windows-executable +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Detect HTML Help Spawn Child Process +The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.001 +- **Last Updated**: 2021-02-11 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_spawn_child_process_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed. + +#### References +* https://attack.mitre.org/techniques/T1218/001/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ +* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 +* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Detect Kerberoasting +This search detects a potential kerberoasting attack via service principal name requests + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1558.003 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() | eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id") | where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" | first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] | where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID | eval start_time=_time, end_time=_time, body="TBD", entities="TBD" | select start_time, end_time, entities, body | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2 + +#### Required fields +* service_name +* _time +* event_code +* ticket_encryption_type +* service_id +* ticket_options + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1558.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Older systems that support kerberos RC4 by default NetApp may generate false positives + +#### References +* Initial ESCU implementation by Jose Hernandez and Patrick Bareiss + +#### Test Dataset + +_version_: 1 +
+--- +### Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules +This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1068, T1078, T1098 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-PrivescAudit/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | +| T1078 | x | x | +| T1098 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Applying Stolen Credentials via Mimikatz modules +This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1055, T1068, T1078, T1098, T1134, T1543, T1547, T1548, T1554, T1556, T1558 +- **Last Updated**: 2020-11-03 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)kerberos::ptt/)=true OR match_regex(cmd_line, /(?i)kerberos::golden/)=true OR match_regex(cmd_line, /(?i)kerberos::silver/)=true OR match_regex(cmd_line, /(?i)misc::skeleton/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1055 | x | x | +| T1068 | x | x | +| T1078 | x | x | +| T1098 | x | x | +| T1134 | x | x | +| T1543 | x | x | +| T1547 | x | x | +| T1548 | x | x | +| T1554 | x | x | +| T1556 | x | x | +| T1558 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz +* https://adsecurity.org/?p=1275 + +#### Test Dataset + +_version_: 1 +
+--- +### Detect HTML Help URL in Command Line +The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.001 +- **Last Updated**: 2021-02-11 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_url_in_command_line_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed. + +#### References +* https://attack.mitre.org/techniques/T1218/001/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ +* https://blog.sevagas.com/?Hacking-around-HTA-files +* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 +* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Detect Use of cmd exe to Launch Script Interpreters +This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.003 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Some legitimate applications may exhibit this behavior. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/cmd_spawns_cscript/windows-sysmon.log + +_version_: 4 +
+--- +### Illegal Deletion of Logs via Mimikatz modules +This detection identifies access to PowerSploit modules that delete event logs. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1070 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)event::drop/)=true OR match_regex(cmd_line, /(?i)event::clear/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1070 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Excessive User Account Lockouts +This search detects user accounts that have been locked out a relatively high number of times in a short period. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_user_account_lockouts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.003 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log + +_version_: 3 +
+--- +### Suspicious MSBuild Rename +The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1127.001, T1036.003 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_msbuild_rename_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1127.001 | x | x | +| T1036.003 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive. + +#### References +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md +* https://github.com/infosecn1nja/MaliciousMacroMSBuild/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + +_version_: 1 +
+--- +### Attempt To Add Certificate To Untrusted Store +Attempt to add a certificate to the certificate store + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1553.004 +- **Last Updated**: 2020-11-03 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1553.004 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log + +_version_: 6 +
+--- +### Illegal Account Creation via PowerSploit modules +This detection identifies access to PowerSploit modules that create accounts illegaly. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1585 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)New-DomainUser/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1585 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### System Information Discovery Detection +Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1082 +- **Last Updated**: 2020-10-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest by dest | where dc_processes_by_dest > 2 | stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1082 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators debugging servers + +#### References +* https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Illegal Privilege Elevation and Persistence via PowerSploit modules +This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1053, T1134, T1548 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Add-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Enable-Privilege/)=true OR match_regex(cmd_line, /(?i)New-ElevatedPersistenceOption/)=true OR match_regex(cmd_line, /(?i)New-UserPersistenceOption/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053 | x | x | +| T1134 | x | x | +| T1548 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Activity Related to Pass the Hash Attacks +This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1550.002 +- **Last Updated**: 2020-10-15 + +
+ View + +#### Search +``` +`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_activity_related_to_pass_the_hash_attacks_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1550.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.002/atomic_red_team/windows-security.log + +_version_: 5 +
+--- +### Suspicious mshta child process +The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.005 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_child_process_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.005 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +#### References +* https://github.com/redcanaryco/AtomicTestHarnesses +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Detect Regasm with no Command Line Arguments +The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-12 + +
+ View + +#### Search +``` +`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) | regex CommandLine="(regasm\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_no_command_line_arguments_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Malicious PowerShell Process - Execution Policy Bypass +This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/windows-sysmon.log + +_version_: 4 +
+--- +### Suspicious MSBuild Spawn +The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1127.001 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_msbuild_spawn_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1127.001 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +#### References +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + +_version_: 1 +
+--- +### Process Creating LNK file in Suspicious Location +This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1566.002 +- **Last Updated**: 2021-01-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_id as lnk_pid | join lnk_pid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` | rename parent_process_id as lnk_pid | fields _time lnk_pid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `process_creating_lnk_file_in_suspicious_location_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1566.002 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories. + +#### References +* https://attack.mitre.org/techniques/T1566/001/ +* https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log + +_version_: 4 +
+--- +### Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)Get-ADDBAccount/)=true AND match_regex(cmd_line, /(?i)\-dbpath[\s;:\.\|]+/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Batch File Write to System32 +The search looks for a batch file (.bat) written to the Windows system directory tree. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1204.002 +- **Last Updated**: 2018-12-14 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat | `batch_file_write_to_system32_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1204.002 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/batch_file_in_system32/windows-sysmon.log + +_version_: 1 +
+--- +### Detect Dump LSASS Memory using comsvcs +This search detects the memory of lsass.exe being dumped for offline credential theft attack. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2020-09-15 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() | eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)) | where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true | eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including Windows command line logging. You can see how we test this with [Event Code 4688](https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4688a) on the [attack_range](https://github.com/splunk/attack_range/blob/develop/ansible/roles/windows_common/tasks/windows-enable-4688-cmd-line-audit.yml). + +#### Required fields +* process_name +* _tenant +* _time +* dest_device_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset + +_version_: 1 +
+--- +### Create or delete windows shares using net exe +This search looks for the creation or deletion of hidden shares using net.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1070.005 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*share* | `create_or_delete_windows_shares_using_net_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1070.005 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. + +#### References +* https://attack.mitre.org/techniques/T1070/005 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+--- +### NLTest Domain Trust Discovery +This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1482 +- **Last Updated**: 2021-01-25 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `nltest_domain_trust_discovery_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1482 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Administrators may use nltest for troubleshooting purposes, otherwise, rarely used. + +#### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md +* https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104 +* https://attack.mitre.org/techniques/T1482/ +* https://www.owasp.org/images/4/4b/Red_Team_Operating_in_a_Modern_Environment.pdf +* https://ss64.com/nt/nltest.html +* https://redcanary.com/threat-detection-report/techniques/domain-trust-discovery/ +* https://thedfirreport.com/2020/10/08/ryuks-return/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Creation of Shadow Copy +Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2019-12-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Legitimate administrator usage of Vssadmin or Wmic will create false positives. + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Registry Keys Used For Privilege Escalation +This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1546.012 +- **Last Updated**: 2020-11-27 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_privilege_escalation_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1546.012 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. + +#### References +* https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### Detect Excessive Account Lockouts From Endpoint +This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.002 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +(Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\ + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.002 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log + +_version_: 5 +
+--- +### Credential Extraction native Microsoft debuggers via z command line option +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) | where cmd_line != null AND process_name != null AND ( match_regex(process_name, /^(?i)ntkd\.exe/)=true OR match_regex(process_name, /^(?i)kd\.exe/)=true ) AND match_regex(cmd_line, /(?i)\-z\s+/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* process_name +* _time +* dest_device_id +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Creation of lsass Dump with Taskmgr +Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2020-02-03 + +
+ View + +#### Search +``` +`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp | stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `creation_of_lsass_dump_with_taskmgr_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. + +#### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager +* https://attack.mitre.org/techniques/T1003/001/ +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Hiding Files And Directories With Attrib exe +Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1222.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `hiding_files_and_directories_with_attrib_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1222.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Some applications and users may legitimately use attrib.exe to interact with the files. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+--- +### First time seen command line argument +This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1059, T1117, T1202 +- **Last Updated**: 2021-2-1 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), cmd_line_norm=lower(cmd_line), cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/, "GUID"), cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/, "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N") | where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true | select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id | first_time_event input_columns=["cmd_line_norm"] | where first_time_cmd_line_norm | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be populating the endpoint data model for SSA and specifically the process_name and the process fields + +#### Required fields +* process_name +* _time +* dest_device_id +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059 | x | x | +| T1117 | x | x | +| T1202 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Applying Stolen Credentials via PowerSploit modules +Stolen credentials are applied by methods such as user impersonation, credential injection, spoofing of authentication processes or getting hold of critical accounts. This detection indicates such activities carried out by PowerSploit exploit kit APIs. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1055, T1068, T1078, T1098, T1134, T1543, T1547, T1548, T1554, T1556, T1558 +- **Last Updated**: 2020-11-03 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-CredentialInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-TokenManipulation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserImpersonation/)=true OR match_regex(cmd_line, /(?i)Get-System/)=true OR match_regex(cmd_line, /(?i)Invoke-RevertToSelf/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1055 | x | x | +| T1068 | x | x | +| T1078 | x | x | +| T1098 | x | x | +| T1134 | x | x | +| T1543 | x | x | +| T1547 | x | x | +| T1548 | x | x | +| T1554 | x | x | +| T1556 | x | x | +| T1558 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Detect PsExec With accepteula Flag +This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.002 +- **Last Updated**: 2020-11-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+--- +### Detect Path Interception By Creation Of program exe +The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1574.009 +- **Last Updated**: 2020-07-03 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest index | `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?[^\\\\]*\.(?:exe|bat|com|ps1))" | eval process_name = lower(process_name) | eval service_process = lower(service_process) | where process_name != service_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1574.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References +* https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+--- +### Suspicious mshta spawn +The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.005 +- **Last Updated**: 2021-01-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_spawn_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.005 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +#### References +* https://codewhitesec.blogspot.com/2018/07/lethalhta.html +* https://github.com/redcanaryco/AtomicTestHarnesses +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### Detect Pass the Hash +This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1550.002 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() | eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), LogonType=map_get(input_event, "logon_type"), LogonProcess=map_get(input_event, "logon_process"), ComputerName=map_get(input_event, "dest_ip_primary_artifact"), AccountName=map_get(input_event, "dest_user_primary_artifact") | where (LogonType="3" AND LogonProcess="NtLmSsp" AND AccountName IS NOT NULL) OR (LogonType="9" AND LogonProcess="seclogo") | first_time_event input_columns=["EventCode","LogonProcess","ComputerName"] | where first_time_EventCode_LogonProcess_ComputerName | eval start_time=_time, end_time=_time, body="TBD", entities="TBD" | select start_time, end_time, entities, body | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2 + +#### Required fields +* logon_process +* dest_user_primary_artifact +* _time +* event_code +* dest_ip_primary_artifact +* logon_type + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1550.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. + +#### References +* Initial ESCU implementation by Bhavin Patel and Patrick Bareiss + +#### Test Dataset + +_version_: 1 +
+--- +### Reconnaissance and Access to Operating System Elements via PowerSploit modules +This detection identifies access to PowerSploit modules that discover and access operating system elements, such as processes, services, registry locations, security packages and files. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1007, T1012, T1046, T1047, T1057, T1083, T1518, T1592.002 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainProcess/)=true OR match_regex(cmd_line, /(?i)Invoke-ProcessHunter/)=true OR match_regex(cmd_line, /(?i)Get-ServiceDetail/)=true OR match_regex(cmd_line, /(?i)Get-WMIProcess/)=true OR match_regex(cmd_line, /(?i)Get-NetProcess/)=true OR match_regex(cmd_line, /(?i)Get-SecurityPackage/)=true OR match_regex(cmd_line, /(?i)Find-DomainObjectPropertyOutlier/)=true OR match_regex(cmd_line, /(?i)Get-DomainObject/)=true OR match_regex(cmd_line, /(?i)Get-ADObject/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegMountedDrive/)=true OR match_regex(cmd_line, /(?i)Get-RegistryMountedDrive/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1007 | x | x | +| T1012 | x | x | +| T1046 | x | x | +| T1047 | x | x | +| T1057 | x | x | +| T1083 | x | x | +| T1518 | x | x | +| T1592.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+--- +### Registry Keys Used For Persistence +The search looks for modifications to registry keys that can be used to launch an application or service at system startup. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1547.001 +- **Last Updated**: 2020-11-27 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_persistence_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1547.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+--- +### Windows AdFind Exe +This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1018 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_adfind_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1018 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +administrators rarely use adfind, usually not used for legitimate reasons + +#### References +* https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/ +* https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### More than usual number of LOLBAS applications in short time period +Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1059, T1053 +- **Last Updated**: 2020-08-25 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() | eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" | stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) | eval lolbas_counter=lolbas_counter*1.0 | rename window_end as timestamp | adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L | where label AND quantile>0.99 | eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +Collect endpoint data such as sysmon or 4688 events. + +#### Required fields +* dest_device_id +* _time +* process_name + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059 | x | x | +| T1053 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Some administrative tasks may involve multiple use of LOLBAS applications in a short period of time. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. + + +#### References +* https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries + +#### Test Dataset + +_version_: 1 +
+--- +### Setting Credentials via Mimikatz modules +This detection identifies illegal setting of credentials via Mimikatz modules. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1068, T1078, T1098 +- **Last Updated**: 2020-11-03 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::addsid/)=true OR match_regex(cmd_line, /(?i)CRYPTO::scauth/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | +| T1078 | x | x | +| T1098 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+--- +### Dump LSASS via procdump Rename +Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\ +During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2021-02-01 + +
+ View + +#### Search +``` +`sysmon` OriginalFileName=procdump process_name!=procdump*.exe EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_rename_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://attack.mitre.org/techniques/T1003/001/ +* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+--- +### First Time Seen Child Process of Zoom +This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1068 +- **Last Updated**: 2020-05-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest | `drop_dm_object_name(Processes)` | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/zoom_child_process/windows-sysmon.log + +_version_: 1 +
+--- +### Kerberoasting spn request with RC4 encryption +This search detects a potential kerberoasting attack via service principal name requests + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1558.003 +- **Last Updated**: 2020-10-16 + +
+ View + +#### Search +``` +`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 | stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kerberoasting_spn_request_with_rc4_encryption_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1558.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Older systems that support kerberos RC4 by default NetApp may generate false positives + +#### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md +* https://www.trimarcsecurity.com/post/trimarcresearch-detecting-kerberoasting-activity + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-security.log + +_version_: 3 +
+--- +### Email files written outside of the Outlook directory +The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_written_outside_of_the_outlook_directory_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Web Servers Executing Suspicious Processes +This search looks for suspicious processes on all systems labeled as web servers. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1082 +- **Last Updated**: 2019-04-01 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1082 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Multiple Okta Users With Invalid Credentials From The Same IP +This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`okta` outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search distinct_users > 5| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.001 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Okta Failed SSO Attempts +Detect failed Okta SSO events + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`okta` displayMessage="User attempted unauthorized access to app" | stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_failed_sso_attempts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.001 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +There may be a faulty config preventing legitmate users from accessing apps they should have access to. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Okta Account Lockout Events +Detect Okta user lockout events + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`okta` displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, country, state, city, src_ip | `okta_account_lockout_events_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.001 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Okta User Logins From Multiple Cities +This search detects logins from the same user from different cities in a 24 hour period. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `okta_user_logins_from_multiple_cities_filter` | search locations > 1 +``` +#### Associated Analytic Story + +#### How To Implement +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.001 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### O365 Add App Role Assignment Grant User +This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136.003 +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." | stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_add_app_role_assignment_grant_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136.003 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. + +#### References +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json + +_version_: 1 +
+--- +### AWS SAML Access by Provider User and Principal +This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`cloudtrail` eventName=Assumerolewithsaml | stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_access_by_provider_user_and_principal_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks. + +#### References +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf +* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/assume_role_with_saml/assume_role_with_saml.json + +_version_: 1 +
+--- +### Cloud Compute Instance Created In Previously Unused Region +This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2020-09-02 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") | table firstTime, user, dest, count , vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_in_previously_unused_region_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### O365 Excessive SSO logon errors +This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1556 +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired | stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId | where count > 5 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_excessive_sso_logon_errors_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1556 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack. + +#### References +* https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json + +_version_: 1 +
+--- +### O365 Suspicious Admin Email Forwarding +This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.003 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_admin_email_forwarding_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json + +_version_: 1 +
+--- +### Cloud Provisioning Activity From Previously Unseen City +This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-10-09 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) | lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, City, user, object, command | `cloud_provisioning_activity_from_previously_unseen_city_filter` | `security_content_ctime(firstTime)` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### Detect New Open GCP Storage Buckets +This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2020-08-05 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions | spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action | spath output=user path=data.protoPayload.authenticationInfo.principalEmail | spath output=location path=data.protoPayload.resourceLocation.currentLocations{} | spath output=src path=data.protoPayload.requestMetadata.callerIp | spath output=bucketName path=data.protoPayload.resourceName | spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role | spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member | search (member=allUsers AND action=ADD) | table _time, bucketName, src, user, location, action, role, member | search `detect_new_open_gcp_storage_buckets_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Abnormally High Number Of Cloud Instances Destroyed +This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-08-21 + +
+ View + +#### Search +``` +| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] | where cardinality >=16 | apply cloud_excessive_instances_destroyed_v1 threshold=0.005 | rename "IsOutlier(instances_destroyed)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_destroyed - expected_upper_threshold | table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability density function. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Many service accounts configured within a cloud infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### High Number of Login Failures from a single source +This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1110.001 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory | stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app | search accounts_locked >= 5| `high_number_of_login_failures_from_a_single_source_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1110.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect AWS Console Login by User from New City +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2020-10-07 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user City | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user City | fields earliestseen user City] | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCity = "New City" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user City userStatus userCity | `detect_aws_console_login_by_user_from_new_city_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### Detect AWS Console Login by User from New Country +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2020-10-07 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Country | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Country | fields earliestseen user Country] | eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") | eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCountry = "New Country" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Country userStatus userCountry | `detect_aws_console_login_by_user_from_new_country_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### Cloud Instance Modified By Previously Unseen User +This search looks for cloud instances being modified by users who have not previously modified them. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-29 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime user command object_id count | `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### Detect Spike in S3 Bucket deletion +This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2018-11-27 + +
+ View + +#### Search +``` +`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### AWS Network Access Control List Deleted +Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.007 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_deleted_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.007 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible that a user has legitimately deleted a network ACL. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/aws_cloudtrail_events.json + +_version_: 2 +
+--- +### Cloud Compute Instance Created By Previously Unseen User +This search looks for cloud compute instances created by users who have not created them before. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-08-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime, user, dest, count vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_by_previously_unseen_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### AWS Detect Users with KMS keys performing encryption S3 +This search provides detection of users with KMS keys performing encryption specifically against S3 buckets. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1486 +- **Last Updated**: 2021-01-11 + +
+ View + +#### Search +``` +`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" | rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1486 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +bucket with S3 encryption + +#### References +* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ +* https://github.com/d1vious/git-wild-hunt +* https://www.youtube.com/watch?v=PgzNib37g0M + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json + +_version_: 1 +
+--- +### O365 Suspicious User Email Forwarding +This search detects when multiple user configured a forwarding rule to the same destination. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.003 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingSmtpAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_user_email_forwarding_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json + +_version_: 1 +
+--- +### Cloud API Calls From Previously Unseen User Roles +This search looks for new commands from each user role. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-09-04 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUserApiCall=min(firstTimeSeen) | where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") | table firstTime, user, object, command |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `cloud_api_calls_from_previously_unseen_user_roles_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### O365 PST export alert +This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" | stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_pst_export_alert_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored. + +#### References +* https://attack.mitre.org/techniques/T1114/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json + +_version_: 1 +
+--- +### AWS Network Access Control List Created with All Open Ports +The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.007 +- **Last Updated**: 2021-01-11 + +
+ View + +#### Search +``` +`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 | eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' | where port_range>1024] | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.007 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/aws_cloudtrail_events.json + +_version_: 2 +
+--- +### Abnormally High Number Of Cloud Instances Launched +This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-08-21 + +
+ View + +#### Search +``` +| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] | where cardinality >=16 | apply cloud_excessive_instances_created_v1 threshold=0.005 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_launched - expected_upper_threshold | table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_launched_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Launched` to create the probability density function. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Cloud Provisioning Activity From Previously Unseen Country +This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-10-09 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | table firstTime, src, Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter` | `security_content_ctime(firstTime)` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### Cloud Compute Instance Created With Previously Unseen Instance Type +Find EC2 instances being created with previously unseen instance types. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-09-12 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where instance_type != "unknown" | lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenInstanceType=min(firstTimeSeen) | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") | table firstTime, user, dest, count, instance_type | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### AWS Detect Users creating keys with encrypt policy without MFA +This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1486 +- **Last Updated**: 2021-01-11 + +
+ View + +#### Search +``` +`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy | spath input=requestParameters.policy output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | spath input=key_policy_statements output=key_policy_action_2 path=Action{} | eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) | spath input=key_policy_statements output=key_policy_principal path=Principal.AWS | search key_policy_action="kms:Encrypt" AND key_policy_principal="*" | stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1486 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +unknown + +#### References +* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ +* https://github.com/d1vious/git-wild-hunt +* https://www.youtube.com/watch?v=PgzNib37g0M + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json + +_version_: 1 +
+--- +### O365 Suspicious Rights Delegation +This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.002 +- **Last Updated**: 2020-12-15 + +
+ View + +#### Search +``` +`o365_management_activity` Operation=Add-MailboxPermission | spath input=Parameters | rename User AS src_user, Identity AS dest_user | search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf | stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_rights_delegation_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Service Accounts + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json + +_version_: 1 +
+--- +### New container uploaded to AWS ECR +This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1525 +- **Last Updated**: 2020-02-20 + +
+ View + +#### Search +``` +| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")` | `new_container_uploaded_to_aws_ecr_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1525 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Uploading container is a normal behavior from developers or users with access to container registry. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Spike in blocked Outbound Traffic from your AWS +This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-05-07 + +
+ View + +#### Search +``` +`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives +* Command and Control + +#### Known False Positives +The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect New Open S3 buckets +This search looks for CloudTrail events where a user has created an open/public S3 bucket. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex field=_raw "(?{.+})" | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} | search grantees=* | mvexpand grantees | spath input=grantees output=uri path=Grantee.URI | spath input=grantees output=permission path=Permission | search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") | search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") | rename requestParameters.bucketName AS bucketName | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + +_version_: 2 +
+--- +### Detect AWS Console Login by User from New Region +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2020-10-07 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Region | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Region | fields earliestseen user Region] | eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userRegion = "New Region" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Region userStatus userRegion | `detect_aws_console_login_by_user_from_new_region_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### Detect AWS Console Login by New User +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-28 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)` | inputlookup append=t previously_seen_users_console_logins | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") |where userStatus="First Time Logging into AWS Console" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `detect_aws_console_login_by_new_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### Detect Spike in AWS Security Hub Alerts for User +This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename findings{}.Resources{}.Id as user | bucket span=4h _time | stats count AS alerts by _time user | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev | eval threshold_value = 2 | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +None + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Cloud Provisioning Activity From Previously Unseen IP Address +This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-08-16 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, user, object_id, command | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` | `security_content_ctime(firstTime)` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### O365 Disable MFA +This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1556 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Operation="Disable Strong Authentication." | stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_disable_mfa_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1556 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +Unless it is a special case, it is uncommon to disable MFA or Strong Authentication + +#### References +* https://attack.mitre.org/techniques/T1556/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json + +_version_: 1 +
+--- +### Detect New Open S3 Buckets over AWS CLI +This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") | rename requestParameters.bucketName AS bucketName | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_over_aws_cli_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + +_version_: 1 +
+--- +### O365 Excessive Authentication Failures Alert +This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1110 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed | stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user | where count > 10 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_excessive_authentication_failures_alert_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1110 | x | x | + +#### Kill Chain Phases +* Not Applicable + +#### Known False Positives +The threshold for alert is above 10 attempts and this should reduce the number of false positives. + +#### References +* https://attack.mitre.org/techniques/T1110/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json + +_version_: 1 +
+--- +### O365 Added Service Principal +This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136.003 +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." | stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_added_service_principal_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136.003 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. + +#### References +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html +* https://www.sygnia.co/golden-saml-advisory + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json + +_version_: 1 +
+--- +### Detect Spike in AWS Security Hub Alerts for EC2 Instance +This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket span=4h _time | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest | eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev | eval threshold_value = 3 | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +None + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json + +_version_: 3 +
+--- +### Cloud Compute Instance Created With Previously Unseen Image +This search looks for cloud compute instances being created with previously unseen image IDs. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-10-12 + +
+ View + +#### Search +``` +| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenImage=min(firstTimeSeen) | where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") | table firstTime, user, image_id, count, dest | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### O365 Bypass MFA via Trusted IP +This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.007 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy | rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") | mvexpand ip_addresses_new_added | where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) |stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account status user_id action | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `o365_bypass_mfa_via_trusted_ip_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.007 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration. + +#### References +* https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf +* https://attack.mitre.org/techniques/T1562/007/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json + +_version_: 1 +
+--- +### Abnormally High Number Of Cloud Infrastructure API Calls +This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-09-07 + +
+ View + +#### Search +``` +| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_api_calls_v1 threshold=0.005 | rename "IsOutlier(api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold = api_calls - expected_upper_threshold | table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to create the probability density function. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives + + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### Detect GCP Storage access from a new IP +This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2020-08-10 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` | multikv | rename sc_status_ as status | rename cs_object_ as bucket_name | rename c_ip_ as remote_ip | rename cs_uri_ as request_uri | rename cs_method_ as operation | search status="\"200\"" | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri | table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri | inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri | outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) | where newIP=1 | eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") | table first_time last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour. It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### AWS Cross Account Activity From Previously Unseen Account +This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. This search is deprecated and have been translated to use the latest Authentication Datamodel. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-28 + +
+ View + +#### Search +``` +| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime | eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") | where status = "New Cross Account Activity" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_cross_account_activity_from_previously_unseen_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen AWS Cross Account Activity - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen AWS Cross Account Activity - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `aws_cross_account_activity_from_previously_unseen_account_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### O365 New Federated Domain Added +This search detects the addition of a new Federated domain. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136.003 +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" | stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_federated_domain_added_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136.003 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider. + +#### References +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html +* https://www.sygnia.co/golden-saml-advisory +* https://o365blog.com/post/aadbackdoor/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json + +_version_: 1 +
+--- +### Detect S3 access from a new IP +This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2018-06-28 + +
+ View + +#### Search +``` +`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Cloud Provisioning Activity From Previously Unseen Region +This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-08-16 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region) | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, Region, user, object, command | `cloud_provisioning_activity_from_previously_unseen_region_filter` | `security_content_ctime(firstTime)` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### Abnormally High Number Of Cloud Security Group API Calls +This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-09-07 + +
+ View + +#### Search +``` +| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 | rename "IsOutlier(security_group_api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where security_group_api_calls > expected_upper_threshold | eval distance_from_threshold = security_group_api_calls - expected_upper_threshold | table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to create the probability density function model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives + + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+--- +### AWS SAML Update identity provider +This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`cloudtrail` eventName=UpdateSAMLProvider | stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_update_identity_provider_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored. + +#### References +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf +* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json + +_version_: 1 +
+--- +### AWS Cloud Provisioning From Previously Unseen Region +This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2018-03-16 + +
+ View + +#### Search +``` +`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_region_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### First time seen command line argument +This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001, T1059.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest| `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table process] | `first_time_seen_command_line_argument_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | +| T1059.003 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name + +#### References + +#### Test Dataset + +_version_: 5 +
+--- +### Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments +This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001 +- **Last Updated**: 2021-01-19 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* | `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +Legitimate process can have this combination of command-line options, but it's not common. + +#### References + +#### Test Dataset + +_version_: 6 +
+--- +### Detect Mimikatz Using Loaded Images +This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2019-12-03 + +
+ View + +#### Search +``` +`sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_using_loaded_images_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Other tools can import the same DLLs. These tools should be part of a whitelist. + +#### References +* https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html + +#### Test Dataset + +_version_: 1 +
+--- +### EC2 Instance Started With Previously Unseen Instance Type +This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-02-07 + +
+ View + +#### Search +``` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_instance_type_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### EC2 Instance Started With Previously Unseen AMI +This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-03-12 + +
+ View + +#### Search +``` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID | table firstTime, lastTime, arn, amiID, dest, instanceType | `ec2_instance_started_with_previously_unseen_ami_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Splunk Enterprise Information Disclosure +This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-06-14 + +
+ View + +#### Search +``` +index=_internal sourcetype=splunkd_ui_access server-info | search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" | rename clientip as src_ip, splunk_server as dest | stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_enterprise_information_disclosure_filter` +``` +#### Associated Analytic Story + +#### How To Implement +The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Execution of File With Spaces Before Extension +This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1036.003 +- **Last Updated**: 2020-11-19 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_spaces_before_extension_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### EC2 Instance Started In Previously Unseen Region +This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2018-02-23 + +
+ View + +#### Search +``` +`cloudtrail` earliest=-1h StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | inputlookup append=t previously_seen_aws_regions.csv | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where regionStatus="Instance Started in a New Region" | `ec2_instance_started_in_previously_unseen_region_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect new API calls from user roles +This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2018-04-16 + +
+ View + +#### Search +``` +`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | table eventName userName] |rename userName as user| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | `detect_new_api_calls_from_user_roles_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect AWS API Activities From Unapproved Accounts +This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` errorCode=success | rename userName as identity | search NOT [| inputlookup identity_lookup_expanded | fields identity] | search NOT [| inputlookup aws_service_accounts | fields identity] | rename identity as user | stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_aws_api_activities_from_unapproved_accounts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ +This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** First Time, **Field:** firstTime\ +1. \ +1. **Label:** Last Time, **Field:** lastTime\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Monitor DNS For Brand Abuse +This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)`| `brand_abuse_dns` | `monitor_dns_for_brand_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery +* Actions on Objectives + +#### Known False Positives +None at this time + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### AWS Cloud Provisioning From Previously Unseen City +This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2018-03-16 + +
+ View + +#### Search +``` +`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_city_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Osquery pack - ColdRoot detection +This search looks for ColdRoot events from the osx-attacks osquery pack. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2019-01-29 + +
+ View + +#### Search +``` +| from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path | bucket _time span=30s | stats count(path) by _time, host, user, path | `osquery_pack___coldroot_detection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation +* Command and Control + +#### Known False Positives +There are no known false positives. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Scheduled tasks used in BadRabbit ransomware +This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1053.005 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) | `scheduled_tasks_used_in_badrabbit_ransomware_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053.005 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +No known false positives + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### DNS Query Requests Resolved by Unauthorized DNS Servers +This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` | `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.004 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Suspicious Changes to File Associations +This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1546.001 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table process_id dest registry_path]| `suspicious_changes_to_file_associations_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1546.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. + +#### References + +#### Test Dataset + +_version_: 4 +
+--- +### Identify New User Accounts +This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.002 +- **Last Updated**: 2017-09-12 + +
+ View + +#### Search +``` +| from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity empStatus endDate startDate | `identify_new_user_accounts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.002 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Spike in Security Group Activity +This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2018-04-18 + +
+ View + +#### Search +``` +`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup security_group_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_security_group_activity_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Abnormally High AWS Instances Terminated by User - MLTK +This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | apply ec2_excessive_terminateinstances_v1 | rename "IsOutlier(instances_terminated)" as isOutlier | where isOutlier=1 +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Detect Spike in AWS API Activity +This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_aws_api_activity_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ +This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\ +1. \ +1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives + + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Prohibited Software On Endpoint +This search looks for applications on the endpoint that you have marked as prohibited. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2019-10-11 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `prohibited_softwares` | `prohibited_software_on_endpoint_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation +* Command and Control +* Actions on Objectives + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### AWS Cloud Provisioning From Previously Unseen Country +This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2018-03-16 + +
+ View + +#### Search +``` +`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_country_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect web traffic to dynamic domain providers +This search looks for web connections to dynamic DNS providers. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic` | `detect_web_traffic_to_dynamic_domain_providers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ +This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` Deprecated because duplicate. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.001 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Remote WMI Command Attempt +This search looks for wmic.exe being launched with parameters to operate on remote systems. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1047 +- **Last Updated**: 2018-12-03 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_wmi_command_attempt_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1047 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators may use this legitimately to gather info from remote systems. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Windows DisableAntiSpyware Registry +The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.001 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.001 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +It is unusual to turn this feature on a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Remote Registry Key modifications +This search monitors for remote modifications to registry keys. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-03-02 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `remote_registry_key_modifications_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### GCP GCR container uploaded +This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1525 +- **Last Updated**: 2020-02-20 + +
+ View + +#### Search +``` +|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path | `drop_dm_object_name("Storage")` | `gcp_gcr_container_uploaded_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1525 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Clients Connecting to Multiple DNS Servers +This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 | `clients_connecting_to_multiple_dns_servers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ +This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048.003 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Suspicious writes to System Volume Information +This search detects writes to the 'System Volume Information' folder by something other than the System process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1036 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +(`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* | stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_writes_to_system_volume_information_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Detect new user AWS Console Login +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | stats earliest(_time) as firstTime latest(_time) as lastTime by user | inputlookup append=t previously_seen_users_console_logins_cloudtrail | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| where userStatus ="First Time Logging into AWS Console" | `detect_new_user_aws_console_login_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Detection of DNS Tunnels +This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048.003 +- **Last Updated**: 2017-09-18 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" | rename "DNS.src" as src "DNS.query" as message | eval length=len(message) | stats sum(length) as length by src | append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" | rename "DNS.src" as src "DNS.answer" as message | eval message=if(message=="unknown","", message) | eval length=len(message) | stats sum(length) as length by src ] | stats sum(length) as length by src | where length > 10000 | `detection_of_dns_tunnels_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048.003 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Detect DNS requests to Phishing Sites leveraging EvilGinx2 +This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1566.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\ + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1566.003 | x | x | + +#### Kill Chain Phases +* Delivery +* Command and Control + +#### Known False Positives +If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Unsuccessful Netbackup backups +This search gives you the hosts where a backup was attempted and then failed. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-12 + +
+ View + +#### Search +``` +`netbackup` | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature | `unsuccessful_netbackup_backups_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Mimikatz Via PowerShell And EventCode 4703 +This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2019-02-27 + +
+ View + +#### Search +``` +`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_via_powershell_and_eventcode_4703_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Processes created by netsh +This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.004 +- **Last Updated**: 2020-11-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `processes_created_by_netsh_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft. + +#### References + +#### Test Dataset + +_version_: 5 +
+--- +### Open Redirect in Splunk Web +This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-19 + +
+ View + +#### Search +``` +index=_internal sourcetype=splunk_web_access return_to="/%09/*" | `open_redirect_in_splunk_web_filter` +``` +#### Associated Analytic Story + +#### How To Implement +No extra steps needed to implement this search. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Cloud Network Access Control List Deleted +Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-09-08 + +
+ View + +#### Search +``` +`cloudtrail` eventName=DeleteNetworkAcl|rename userIdentity.arn as arn | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `cloud_network_access_control_list_deleted_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the `cloud_network_access_control_list_deleted_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible that a user has legitimately deleted a network ACL. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Abnormally High AWS Instances Launched by User - MLTK +This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | apply ec2_excessive_runinstances_v1 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Reg exe used to hide files directories via registry keys +The search looks for command-line arguments used to hide a file or directory using the reg add command. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1564.001 +- **Last Updated**: 2019-02-27 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)" | `reg_exe_used_to_hide_files_directories_via_registry_keys_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1564.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None at the moment + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Detect Long DNS TXT Record Response +This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" | `detect_long_dns_txt_record_response_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048.003 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### AWS Cloud Provisioning From Previously Unseen IP Address +This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-03-16 + +
+ View + +#### Search +``` +`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_ip_address_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Unsigned Image Loaded by LSASS +This search detects loading of unsigned images by LSASS. Deprecated because too noisy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2019-12-06 + +
+ View + +#### Search +``` +`sysmon` EventID=7 Image=*lsass.exe Signed=false | stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `unsigned_image_loaded_by_lsass_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset + +_version_: 1 +
+--- +### Detect USB device insertion +The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-11-27 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `detect_usb_device_insertion_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +Legitimate USB activity will also be detected. Please verify and investigate as appropriate. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Abnormally High AWS Instances Launched by User +This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev | `abnormally_high_aws_instances_launched_by_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### EC2 Instance Modified With Previously Unseen User +This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest | `ec2_instance_modified_with_previously_unseen_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Windows hosts file modification +The search looks for modifications to the hosts file on all Windows endpoints across your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-11-02 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* | `drop_dm_object_name(Filesystem)` | `windows_hosts_file_modification_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +There may be legitimate reasons for system administrators to add entries to this file. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Abnormally High AWS Instances Terminated by User +This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` eventName=TerminateInstances errorCode=success | bucket span=10m _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev | `abnormally_high_aws_instances_terminated_by_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Windows connhost exe started forcefully +The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.003 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_connhost_exe_started_forcefully_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.003 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +This process should not be ran forcefully, we have not see any false positives for this detection + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Suspicious File Write +The search looks for files created with names that have been linked to malicious activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2019-04-25 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` | `suspicious_writes` | `suspicious_file_write_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### EC2 Instance Started With Previously Unseen User +This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Detect Spike in Network ACL Activity +This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.007 +- **Last Updated**: 2018-05-21 + +
+ View + +#### Search +``` +`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup network_acl_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_network_acl_activity_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.007 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect API activity from users without MFA +This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-05-17 + +
+ View + +#### Search +``` +`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity as user]| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_api_activity_from_users_without_mfa_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ +This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** AWS User ARN, **Field:** userIdentity.arn\ +1. \ +1. **Label:** AWS User Type, **Field:** userIdentity.type\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Extended Period Without Successful Netbackup Backups +This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-12 + +
+ View + +#### Search +``` +`netbackup` MESSAGE="Disk/Partition backup completed successfully." | stats latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest | `extended_period_without_successful_netbackup_backups_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Uncommon Processes On Endpoint +This search looks for applications on the endpoint that you have marked as uncommon. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1204.002 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_on_endpoint_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1204.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 4 +
+--- +### Detect F5 TMUI RCE CVE-2020-5902 +This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1190 +- **Last Updated**: 2020-08-02 + +
+ View + +#### Search +``` +`f5_bigip_rogue` | regex _raw="(hsqldb;|.*\\.\\.;.*)" | search `detect_f5_tmui_rce_cve_2020_5902_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog. As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it. I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;). + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1190 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/ +* https://support.f5.com/csp/article/K52145254 +* https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/ + +#### Test Dataset + +_version_: 1 +
+--- +### Detect malicious requests to exploit JBoss servers +This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +No known false positives for this detection. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Supernova Webshell +This search aims to detect the Supernova webshell used in the SUNBURST attack. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1505.003 +- **Last Updated**: 2021-01-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Web.Web where web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s | `supernova_webshell_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1505.003 | x | x | + +#### Kill Chain Phases +* Exfiltration + +#### Known False Positives +There might be false positives associted with this detection since items like args as a web argument is pretty generic. + +#### References +* https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html +* https://www.guidepointsecurity.com/supernova-solarwinds-net-webshell-analysis/ + +#### Test Dataset + +_version_: 1 +
+--- +### Detect attackers scanning for vulnerable JBoss servers +This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1082 +- **Last Updated**: 2017-09-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1082 | x | x | + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Monitor Web Traffic For Brand Abuse +This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +None at this time + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### SQL Injection with Long URLs +This search looks for long URLs that have several SQL commands visible within them. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1190 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1190 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Protocols passing authentication in cleartext +This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-11-04 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocols_passing_authentication_in_cleartext_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Reconnaissance +* Actions on Objectives + +#### Known False Positives +Some networks may use kerberized FTP or telnet servers, however, this is rare. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Large Volume of DNS ANY Queries +The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1498.002 +- **Last Updated**: 2017-09-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1498.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Hosts receiving high volume of network traffic from email server +This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.002 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Detect ARP Poisoning +By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1200, T1498, T1557.002 +- **Last Updated**: 2020-08-11 + +
+ View + +#### Search +``` +`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_arp_poisoning_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1200 | x | x | +| T1498 | x | x | +| T1557.002 | x | x | + +#### Kill Chain Phases +* Reconnaissance +* Delivery +* Actions on Objectives + +#### Known False Positives +This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely). + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Prohibited Network Traffic Allowed +This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048 | x | x | + +#### Kill Chain Phases +* Delivery +* Command and Control + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### DNS record changed +The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| inputlookup discovered_dns_records | rename answer as discovered_answer | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.004 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Protocol or Port Mismatch +This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter` +``` +#### Associated Analytic Story + +#### How To Implement +Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048.003 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Excessive DNS Failures +This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query"| `drop_dm_object_name("DNS")`| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain| where isnull(domain)| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank| where isnull(rank)| stats sum(count) as count mode(queries) as queries by src| `get_asset(src)`| where count>50 | `excessive_dns_failures_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.004 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Processes Tapping Keyboard Events +This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2019-01-25 + +
+ View + +#### Search +``` +| from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id| dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Child Processes of Spoolsv exe +This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1068 +- **Last Updated**: 2020-03-16 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `child_processes_of_spoolsv_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### WMI Permanent Event Subscription +This search looks for the creation of WMI permanent event subscriptions. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1047 +- **Last Updated**: 2018-10-23 + +
+ View + +#### Search +``` +`wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | rename ComputerName as dest | `wmi_permanent_event_subscription_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1047 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, administrators may use event subscriptions for legitimate purposes. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Baron Samedit CVE-2021-3156 Segfault +This search detects the heap-based buffer overflow of sudoedit + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1068 +- **Last Updated**: 2021-01-29 + +
+ View + +#### Search +``` +`linux_hosts` | search sudoedit segfault | stats count min(_time) as firstTime max(_time) as lastTime by host | search count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter` +``` +#### Associated Analytic Story + +#### How To Implement +Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged. This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +If sudoedit is throwing segfaults for other reasons this will pick those up too. + +#### References +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + +#### Test Dataset + +_version_: 1 +
+--- +### Spike in File Writes +The search looks for a sharp increase in the number of files written to a particular host + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-03-16 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Detect Baron Samedit CVE-2021-3156 via OSQuery +This search detects the heap-based buffer overflow of sudoedit + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1068 +- **Last Updated**: 2021-01-28 + +
+ View + +#### Search +``` +`osquery_process` | search "columns.cmdline"="sudoedit -s \\*" | `detect_baron_samedit_cve_2021_3156_via_osquery_filter` +``` +#### Associated Analytic Story + +#### How To Implement +OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + +#### Test Dataset + +_version_: 1 +
+--- +### Detection of tools built by NirSoft +This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1072 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1072 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Detect Oulook exe writing a zip file +This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1566.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| rename parent_process_id as outlook_id| join malicious_id type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] | table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path | where file_name != "" | `detect_oulook_exe_writing_a__zip_file_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1566.001 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +It is not uncommon for outlook to write legitimate zip files to the disk. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### First Time Seen Running Windows Service +This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1569.002 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`wineventlog_system` EventCode=7036 | rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | where state="running" | lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) | table _time dest service | `first_time_seen_running_windows_service_filter` +``` +#### Associated Analytic Story + +#### How To Implement +While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1569.002 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. + +#### References + +#### Test Dataset + +_version_: 4 +
+--- +### WMI Temporary Event Subscription +This search looks for the creation of WMI temporary event subscriptions. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1047 +- **Last Updated**: 2018-10-23 + +
+ View + +#### Search +``` +`wmi` EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?[^;|^$]+)" | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1047 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Sunburst Correlation DLL and Network Event +The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1203 +- **Last Updated**: 2020-12-14 + +
+ View + +#### Search +``` +(`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) | eventstats dc(EventCode) AS dc_events | where dc_events=2 | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host | rename host as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `sunburst_correlation_dll_and_network_event_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1203 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References +* https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html + +#### Test Dataset + +_version_: 1 +
+--- +### MacOS - Re-opened Applications +This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-02-07 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `macos___re_opened_applications_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation +* Command and Control + +#### Known False Positives +At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Baron Samedit CVE-2021-3156 +This search detects the heap-based buffer overflow of sudoedit + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1068 +- **Last Updated**: 2021-01-27 + +
+ View + +#### Search +``` +`linux_hosts` | search "sudoedit -s \\" | `detect_baron_samedit_cve_2021_3156_filter` +``` +#### Associated Analytic Story + +#### How To Implement +Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + +#### Test Dataset + +_version_: 1 +
+--- +### Remote Desktop Process Running On System +This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Remote Desktop may be used legitimately by users on the network. + +#### References + +#### Test Dataset + +_version_: 5 +
+--- +### Kubernetes Azure detect sensitive role access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=clusterroles OR clusterrolebindings | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes AWS detect sensitive role access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_aws_detect_sensitive_role_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes GCP detect sensitive object access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.resource=configmaps OR secrets | table data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name data.protoPayload.request.metadata.namespace data.labels.authorization.k8s.io/decision | dedup data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name |`kubernetes_gcp_detect_sensitive_object_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk add on for GCP . This search works with pubsub messaging service logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes Azure scan fingerprint +This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1526 +- **Last Updated**: 2020-05-19 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason |`kubernetes_azure_scan_fingerprint_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1526 | x | x | + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes AWS detect service accounts forbidden failure access +This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure | table sourceIPs{} user.username userAgent verb responseStatus.status requestURI | `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes GCP detect most active service accounts by pod +This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-07-10 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.request.spec.group{}=system:serviceaccounts | table src_ip src_user http_user_agent data.protoPayload.request.spec.nonResourceAttributes.verb data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource | top src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource |`kubernetes_gcp_detect_most_active_service_accounts_by_pod_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk GCP add on. This search works with pubsub messaging service logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### aws detect permanent key creation +This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-07-27 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` CreateAccessKey | spath eventName | search eventName=CreateAccessKey "userIdentity.type"=IAMUser | table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId |`aws_detect_permanent_key_creation_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### GCP Detect high risk permissions by resource and account +This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-10-09 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.permission=iam.serviceAccounts.getaccesstoken OR iam.serviceAccounts.setIamPolicy OR iam.serviceAccounts.actas OR dataflow.jobs.create OR composer.environments.create OR dataproc.clusters.create |table data.protoPayload.requestMetadata.callerIp data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.response.bindings{}.members{} data.resource.labels.project_id | `gcp_detect_high_risk_permissions_by_resource_and_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +High risk permissions are part of any GCP environment, however it is important to track resource and accounts usage, this search may produce false positives. + +#### References +* https://github.com/dxa4481/gcploit +* https://www.youtube.com/watch?v=Ml09R38jpok +* https://cloud.google.com/iam/docs/permissions-reference + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes AWS detect suspicious kubectl calls +This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous | table src_ip src_user verb userAgent requestURI | stats count by src_ip src_user verb userAgent requestURI |`kubernetes_aws_detect_suspicious_kubectl_calls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes AWS detect RBAC authorization by account +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason | stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_aws_detect_rbac_authorization_by_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes Azure detect most active service accounts by pod namespace +This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-26 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace | top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### GCP Detect gcploit framework +This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-10-08 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s | table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent | `gcp_detect_gcploit_framework_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects + +#### References +* https://github.com/dxa4481/gcploit +* https://www.youtube.com/watch?v=Ml09R38jpok + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes Azure detect service accounts forbidden failure access +This search provides information on Kubernetes service accounts with failure or forbidden access status + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes AWS detect most active service accounts by pod +This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts objectRef.resource=pods | table sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision | top sourceIPs{} user.username verb annotations.authorization.k8s.io/decision |`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Amazon EKS Kubernetes cluster scan detection +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1526 +- **Last Updated**: 2020-04-15 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`amazon_eks_kubernetes_cluster_scan_detection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1526 | x | x | + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes Azure detect RBAC authorization by account +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-26 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | search annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason |stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_azure_detect_rbac_authorization_by_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### aws detect attach to role policy +This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-07-27 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` attach policy| spath requestParameters.policyArn | table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate | `aws_detect_attach_to_role_policy_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### AWS EKS Kubernetes cluster sensitive object access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 |table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`aws_eks_kubernetes_cluster_sensitive_object_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. This search works with cloudwatch logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### GCP Kubernetes cluster pod scan detection +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1526 +- **Last Updated**: 2020-07-17 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` category=kube-audit |spath input=properties.log |search responseStatus.code=401 |table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod | `gcp_kubernetes_cluster_pod_scan_detection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1526 | x | x | + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### GCP Kubernetes cluster scan detection +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1526 +- **Last Updated**: 2020-04-15 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `gcp_kubernetes_cluster_scan_detection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1526 | x | x | + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes GCP detect suspicious kubectl calls +This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous | table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path |dedup src_ip src_user |`kubernetes_gcp_detect_suspicious_kubectl_calls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk add on for GCP. This search works with pubsub messaging logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### gcp detect oauth token abuse +This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-09-01 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` type.googleapis.com/google.cloud.audit.AuditLog |table protoPayload.@type protoPayload.status.details{}.@type protoPayload.status.details{}.violations{}.callerIp protoPayload.status.details{}.violations{}.type protoPayload.status.message | `gcp_detect_oauth_token_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +GCP Oauth token abuse detection will only work if there are access policies in place along with audit logs. + +#### References +* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-1 +* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-2 + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes Azure detect suspicious kubectl calls +This search provides information on rare Kubectl calls with IP, verb namespace and object access context + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-26 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration | search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 | table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI | rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI |`kubernetes_azure_detect_suspicious_kubectl_calls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes GCP detect sensitive role access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole dest=apis/rbac.authorization.k8s.io/v1 src_ip!=::1 | table src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason | dedup src_ip src_user |`kubernetes_gcp_detect_sensitive_role_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk add on for GCP. This search works with pubsub messaging servicelogs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sensitive role resource access is necessary for cluster operation, however source IP, user agent, decision and reason may indicate possible malicious use. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### aws detect sts get session token abuse +This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1550 +- **Last Updated**: 2020-07-27 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` ASIA userIdentity.type=IAMUser| spath eventName | search eventName=GetSessionToken | table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region | `aws_detect_sts_get_session_token_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1550 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Amazon EKS Kubernetes Pod scan detection +This search provides detection information on unauthenticated requests against Kubernetes' Pods API + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1526 +- **Last Updated**: 2020-04-15 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1526 | x | x | + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### aws detect role creation +This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-07-27 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* | table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate | `aws_detect_role_creation_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes GCP detect RBAC authorizations by account +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole | table src_ip src_user data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason | rare src_user data.labels.authorization.k8s.io/reason |`kubernetes_gcp_detect_rbac_authorizations_by_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on for GCP. This search works with pubsub messaging service logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes Azure pod scan fingerprint +This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod |`kubernetes_azure_pod_scan_fingerprint_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### aws detect sts assume role abuse +This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-07-27 + +
+ View + +#### Search +``` +`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role | table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate | `aws_detect_sts_assume_role_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes Azure detect sensitive object access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_object_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### GCP Detect accounts with high risk roles by project +This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-10-09 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}.role=roles/owner OR roles/editor OR roles/iam.serviceAccountUser OR roles/iam.serviceAccountAdmin OR roles/iam.serviceAccountTokenCreator OR roles/dataflow.developer OR roles/dataflow.admin OR roles/composer.admin OR roles/dataproc.admin OR roles/dataproc.editor | table data.resource.type data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.authorizationInfo{}.resource data.protoPayload.response.bindings{}.role data.protoPayload.response.bindings{}.members{} | `gcp_detect_accounts_with_high_risk_roles_by_project_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Accounts with high risk roles should be reduced to the minimum number needed, however specific tasks and setups may be simply expected behavior within organization + +#### References +* https://github.com/dxa4481/gcploit +* https://www.youtube.com/watch?v=Ml09R38jpok +* https://cloud.google.com/iam/docs/understanding-roles + +#### Test Dataset + +_version_: 1 +
+--- +### Kubernetes GCP detect service accounts forbidden failure access +This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` system:serviceaccounts data.protoPayload.response.status.allowed!=* | table src_ip src_user http_user_agent data.protoPayload.response.spec.resourceAttributes.namespace data.resource.labels.cluster_name data.protoPayload.response.spec.resourceAttributes.verb data.protoPayload.request.status.allowed data.protoPayload.response.status.reason data.labels.authorization.k8s.io/decision | dedup src_ip src_user | `kubernetes_gcp_detect_service_accounts_forbidden_failure_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk add on for GCP. This search works with pubsub messaging service logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect New Login Attempts to Routers +The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Legitimate router connections may appear as new connections + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Email Attachments With Lots Of Spaces +Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-19 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address "(?.*)@" | `email_attachments_with_lots_of_spaces_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ + **Splunk Phantom Playbook Integration**\ +If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +None at this time + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Phishing Email Detection by Machine Learning Method - SSA +Malicious mails can conduct phishing that induces readers to open attachment, click links or trigger third party service. This detect uses Natural Language Processing (NLP) approach to analyze an email message's content (Sender, Subject and Body) and judge whether it is a phishing email. The detection adopts a deep learning (neural network) model that employs character level embeddings plus LSTM layers to perform classification. The model is pre-trained and then published as ONNX format. Current sample model is trained using the dataset published at https://github.com/splunk/attack_data/tree/master/datasets/T1566_Phishing_Email/splunk_train.json User are expected to re-train the model by combining with their own training data for better accuracy using the provided model file (SMLE notebook). DSP pipeline then processes the email message and passes it as an event to Apply ML Models function, which returns the probability of a phishing email. Current implementation assumes the email is fed to DSP in JSON format contains at least email's sender, subject and its message body, including reply content, if any. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1566 +- **Last Updated**: 2020-08-25 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() | eval eventLine=concat(ucast(map_get(input_event, "From"), "string", " "), " ", ucast(map_get(input_event, "Subject"), "string", " "), " ", ucast(map_get(input_event, "Content"), "string", " "), " "), _time=map_get(input_event, "_time") | where eventLine IS NOT NULL | eval mapC={" ": 32, "!": 33, "\"": 34, "#": 35, "$": 36, "%": 37, "&": 38, "`": 39, "(": 40, ")": 41, "*": 42, "+": 43, ",": 44, "-": 45, ".": 46, "/": 47, "0": 48, "1": 49, "2": 50, "3": 51, "4": 52, "5": 53, "6": 54, "7": 55, "8": 56, "9": 57, ":": 58, ";": 59, "<": 60, "=": 61, ">": 62, "?": 63, "@": 64, "A": 65, "B": 66, "C": 67, "D": 68, "E": 69, "F": 70, "G": 71, "H": 72, "I": 73, "J": 74, "K": 75, "L": 76, "M": 77, "N": 78, "O": 79, "P": 80, "Q": 81, "R": 82, "S": 83, "T": 84, "U": 85, "V": 86, "W": 87, "X": 88, "Y": 89, "Z": 90, "[": 91, "\\": 92, "]": 93, "^": 94, "_": 95, "`": 96, "a": 97, "b": 98, "c": 99, "d": 100, "e": 101, "f": 102, "g": 103, "h": 104, "i": 105, "j": 106, "k": 107, "l": 108, "m": 109, "n": 110, "o": 111, "p": 112, "q": 113, "r": 114, "s": 115, "t": 116, "u": 117, "v": 118, "w": 119, "x": 120, "y": 121, "z": 122, "{": 123, "|": 124, "}": 125, "~": 126}, ml_in = for_each(iterator(mvrange(1,129), "i"), cast(map_get(mapC, substr(eventLine, i, 1)), "float") ) | apply_model connection_id="YOUR_S3_ONNX_CONNECTOR_ID" name="phishing_email_v8" path="s3://smle-experiments/models/phishing_email" | eval probability = mvindex(ml_out, 0) | where probability > 0.5 | eval start_time=_time, end_time=_time, entities="TBD", body="TBD" | select probability, body, entities, start_time, end_time | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +Events are fed to DSP contains at least email's sender, subject and its message body. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1566 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Because of imbalance of anomaly data in training, the model will less likely report false positive. Instead, the model is more prone to false negative. Current best recall score is ~85% + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Suspicious Email - UBA Anomaly +This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1566 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model | `drop_dm_object_name(All_UEBA_Events)` | `drop_dm_object_name(UEBA_Anomalies)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_email___uba_anomaly_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1566 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and add the URL to an allow list if you determine that it is a legitimate sender. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### No Windows Updates in a time frame +This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-15 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) | `security_content_ctime(lastTime)` | search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Suspicious Email Attachment Extensions +This search looks for emails that have attachments with suspicious file extensions. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1566.001 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` | `suspicious_email_attachment_extensions_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ + **Splunk Phantom Playbook Integration**\ +If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1566.001 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Monitor Email For Brand Abuse +This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-01-05 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +None at this time + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Email servers sending high volume traffic to hosts +This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.002 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Suspicious Java Classes +This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-12-06 + +
+ View + +#### Search +``` +`stream_http` http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_java_classes_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +There are no known false positives. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Spectre and Meltdown Vulnerable Systems +The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-01-07 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest | `drop_dm_object_name(Vulnerabilities)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spectre_and_meltdown_vulnerable_systems_filter` +``` +#### Associated Analytic Story + +#### How To Implement +The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +It is possible that your vulnerability scanner is not detecting that the patches have been applied. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Zerologon via Zeek +This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1190 +- **Last Updated**: 2020-09-15 + +
+ View + +#### Search +``` +`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) | bin span=5m _time | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip | search opscount=3 authcount>4 passcount>0 | search `detect_zerologon_via_zeek_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json. These three operations are then correlated on the Zeek UID field. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1190 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://www.secura.com/blog/zero-logon +* https://github.com/SecuraBV/CVE-2020-1472 +* https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472 + +#### Test Dataset + +_version_: 1 +
+--- +### DNS Query Length Outliers - MLTK +This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.004 +- **Last Updated**: 2020-01-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +1. \ +1. **Label:** DNS Query Length, **Field:** query_length\ +1. \ +1. **Label:** Number of events, **Field:** count\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.004 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### TOR Traffic +This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.001 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `tor_traffic_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.001 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +None at this time + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Detect Large Outbound ICMP Packets +This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1095 +- **Last Updated**: 2018-06-01 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1095 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Detect SNICat SNI Exfiltration +This search looks for commands that the SNICat tool uses in the TLS SNI field. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1041 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` +`zeek_ssl` | rex field=server_name "(?(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)" | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1041 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Unknown + +#### References +* https://www.mnemonic.no/blog/introducing-snicat/ +* https://github.com/mnemonic-no/SNIcat +* https://attack.mitre.org/techniques/T1041/ + +#### Test Dataset + +_version_: 1 +
+--- +### SMB Traffic Spike +This search looks for spikes in the number of Server Message Block (SMB) traffic connections. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.002 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +A file server may experience high-demand loads that could cause this analytic to trigger. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Detect Port Security Violation +By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1200, T1498, T1557.002 +- **Last Updated**: 2020-10-28 + +
+ View + +#### Search +``` +`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_port_security_violation_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1200 | x | x | +| T1498 | x | x | +| T1557.002 | x | x | + +#### Kill Chain Phases +* Reconnaissance +* Delivery +* Exploitation +* Actions on Objectives + +#### Known False Positives +This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### SMB Traffic Spike - MLTK +This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.002 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count | `smb_traffic_spike___mltk_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ +1. **Label:** Number of events, **Field:** count\ +Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Remote Desktop Network Traffic +This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.001 +- **Last Updated**: 2020-07-07 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Remote Desktop may be used legitimately by users on the network. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### DNS Query Length With High Standard Deviation +This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048.003 +- **Last Updated**: 2021-01-18 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query | `drop_dm_object_name("DNS")` | eval query_length = len(query) | table query query_length record_type count | eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2) | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048.003 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +It's possible there can be long domain names that are legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log + +_version_: 3 +
+--- +### Detect Unauthorized Assets by MAC address +By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-13 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac | dedup All_Sessions.dest_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` | search NOT [| inputlookup asset_lookup_by_str |rename mac as dest_mac | fields + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Reconnaissance +* Delivery +* Actions on Objectives + +#### Known False Positives +This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Outbound SMB Traffic +This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.002 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(start_time)` | `security_content_ctime(end_time)` | `detect_outbound_smb_traffic_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives +* Command and Control + +#### Known False Positives +It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. + +#### References + +#### Test Dataset + +_version_: 3 +
+--- +### Detect Windows DNS SIGRed via Zeek +This search detects SIGRed via Zeek DNS and Zeek Conn data. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1203 +- **Last Updated**: 2020-07-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id | rename DNS.flow_id as flow_id | append [| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id | rename All_Traffic.flow_id as flow_id] | `detect_windows_dns_sigred_via_zeek_filter` | stats count by flow_id | where count>1 | fields - count +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The Network Resolution and Network Traffic datamodels are in use for this search. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1203 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + +#### Test Dataset + +_version_: 1 +
+--- +### Detect hosts connecting to dynamic domain providers +Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1189 +- **Last Updated**: 2021-01-14 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ +This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +1. \ +1. **Label:** DNS Answer, **Field:** answer\ +1. \ +1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1189 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log + +_version_: 3 +
+--- +### Detect Traffic Mirroring +Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1200, T1498, T1020.001 +- **Last Updated**: 2020-10-28 + +
+ View + +#### Search +``` +`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_traffic_mirroring_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1200 | x | x | +| T1498 | x | x | +| T1020.001 | x | x | + +#### Kill Chain Phases +* Delivery +* Actions on Objectives + +#### Known False Positives +This search will return false positives for any legitimate traffic captures by network administrators. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Unusually Long Content-Type Length +This search looks for unusually long strings in the Content-Type http header that the client sends the server. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-10-13 + +
+ View + +#### Search +``` +`stream_http` | eval cs_content_type_length = len(cs_content_type) | where cs_content_type_length > 100 | table endtime src_ip dest_ip cs_content_type_length cs_content_type url | `unusually_long_content_type_length_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +Very few legitimate Content-Type fields will have a length greater than 100 characters. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Rogue DHCP Server +By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1200, T1498, T1557 +- **Last Updated**: 2020-08-11 + +
+ View + +#### Search +``` +`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_rogue_dhcp_server_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1200 | x | x | +| T1498 | x | x | +| T1557 | x | x | + +#### Kill Chain Phases +* Reconnaissance +* Delivery +* Actions on Objectives + +#### Known False Positives +This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Detect IPv6 Network Infrastructure Threats +By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1200, T1498, T1557.002 +- **Last Updated**: 2020-10-28 + +
+ View + +#### Search +``` +`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") | eval src_interface=src_int_prefix_long+src_int_suffix | eval dest_interface=dest_int_prefix_long+dest_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface | table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detect_ipv6_network_infrastructure_threats_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1200 | x | x | +| T1498 | x | x | +| T1557.002 | x | x | + +#### Kill Chain Phases +* Reconnaissance +* Delivery +* Actions on Objectives + +#### Known False Positives +None currently known + +#### References +* https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html + +#### Test Dataset + +_version_: 1 +
+--- +### Detect Windows DNS SIGRed via Splunk Stream +This search detects SIGRed via Splunk Stream. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1203 +- **Last Updated**: 2020-07-28 + +
+ View + +#### Search +``` +`stream_dns` | spath "query_type{}" | search "query_type{}" IN (SIG,KEY) | spath protocol_stack | search protocol_stack="ip:tcp:dns" | append [search `stream_tcp` bytes_out>65000] | `detect_windows_dns_sigred_via_splunk_stream_filter` | stats count by flow_id | where count>1 | fields - count +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1203 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + +#### Test Dataset + +_version_: 1 +
+--- +### Remote Desktop Network Bruteforce +This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev | `remote_desktop_network_bruteforce_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must ensure that your network traffic data is populating the Network_Traffic data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.001 | x | x | + +#### Kill Chain Phases +* Reconnaissance +* Delivery + +#### Known False Positives +RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. + +#### References + +#### Test Dataset + +_version_: 2 +
+--- +### Detect Software Download To Network Device +Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1542.005 +- **Last Updated**: 2020-10-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1542.005 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices. + +#### References + +#### Test Dataset + +_version_: 1 +
+--- +### Web Fraud - Anomalous User Clickspeed +This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2018-10-08 + +
+ View + +#### Search +``` +`stream_http` http_content_type=text* | rex field=cookie "form_key=(?\w+)" | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) | `web_fraud___anomalous_user_clickspeed_filter` +``` +#### Associated Analytic Story + +#### How To Implement +Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior. + +#### References +* https://en.wikipedia.org/wiki/Session_ID +* https://en.wikipedia.org/wiki/Session_(computer_science) +* https://en.wikipedia.org/wiki/HTTP_cookie +* https://splunkbase.splunk.com/app/1809/ + +#### Test Dataset + +_version_: 1 +
+--- +### Web Fraud - Account Harvesting +This search is used to identify the creation of multiple user accounts using the same email domain name. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136 +- **Last Updated**: 2018-10-08 + +
+ View + +#### Search +``` +`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" | rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^&|^$]+)" | search Username=* | rex field=Username "@(?.*)" | stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain | where UniqueUsernames> 25 | `web_fraud___account_harvesting_filter` +``` +#### Associated Analytic Story + +#### How To Implement +We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. + +#### References +* https://splunkbase.splunk.com/app/2734/ +* https://splunkbase.splunk.com/app/1809/ + +#### Test Dataset + +_version_: 1 +
+--- +### Web Fraud - Password Sharing Across Accounts +This search is used to identify user accounts that share a common password. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-10-08 + +
+ View + +#### Search +``` +`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data "login\[username\]=(?[^&|^$]+)" | rex field=form_data "login\[password\]=(?[^&|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 | `web_fraud___password_sharing_across_accounts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. + +#### References +* https://en.wikipedia.org/wiki/Session_ID +* https://en.wikipedia.org/wiki/Session_(computer_science) +* https://en.wikipedia.org/wiki/HTTP_cookie +* https://splunkbase.splunk.com/app/1809/ + +#### Test Dataset + +_version_: 1 +
+--- From 9f3045df333e6f63c67cad6df50e809c40e81f74 Mon Sep 17 00:00:00 2001 From: divious1 Date: Thu, 25 Feb 2021 01:17:31 -0500 Subject: [PATCH 03/62] minor updates to jinja template --- bin/doc_gen.py | 3 +- .../doc_detections_markdown.j2 | 3 +- docs/detections.md | 28805 ++++++++-------- 3 files changed, 14590 insertions(+), 14221 deletions(-) diff --git a/bin/doc_gen.py b/bin/doc_gen.py index d979f6d03e..f3b115d558 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -37,11 +37,12 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): detection_yaml['kind'] = manifest_file.split('/')[-2] detections.append(detection_yaml) + sorted_detections= sorted(detections, key=lambda i: i['name']) j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), trim_blocks=True) template = j2_env.get_template('doc_detections_markdown.j2') output_path = path.join(OUTPUT_DIR + '/detections.md') - output = template.render(detections=detections) + output = template.render(detections=sorted_detections) with open(output_path, 'w', encoding="utf-8") as f: f.write(output) print("doc_gen.py wrote {0} detection documentation to: {1}".format(len(detections),output_path)) diff --git a/bin/jinja2_templates/doc_detections_markdown.j2 b/bin/jinja2_templates/doc_detections_markdown.j2 index de1853d709..31b665e9bf 100644 --- a/bin/jinja2_templates/doc_detections_markdown.j2 +++ b/bin/jinja2_templates/doc_detections_markdown.j2 @@ -117,5 +117,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by _version_: {{detection.version}} ---- + +===== {% endfor %} diff --git a/docs/detections.md b/docs/detections.md index ae69d2cef7..575b00dc50 100644 --- a/docs/detections.md +++ b/docs/detections.md @@ -7,327 +7,327 @@ All the detections shipped to different Splunk products. Below is a breakdown by
View -- [O365 Add App Role Assignment Grant User](#o365-add-app-role-assignment-grant-user) +- [AWS Cross Account Activity From Previously Unseen Account](#aws-cross-account-activity-from-previously-unseen-account) +- [AWS Detect Users creating keys with encrypt policy without MFA](#aws-detect-users-creating-keys-with-encrypt-policy-without-mfa) +- [AWS Detect Users with KMS keys performing encryption S3](#aws-detect-users-with-kms-keys-performing-encryption-s3) +- [AWS EKS Kubernetes cluster sensitive object access](#aws-eks-kubernetes-cluster-sensitive-object-access) +- [AWS Network Access Control List Created with All Open Ports](#aws-network-access-control-list-created-with-all-open-ports) +- [AWS Network Access Control List Deleted](#aws-network-access-control-list-deleted) - [AWS SAML Access by Provider User and Principal](#aws-saml-access-by-provider-user-and-principal) -- [Cloud Compute Instance Created In Previously Unused Region](#cloud-compute-instance-created-in-previously-unused-region) -- [O365 Excessive SSO logon errors](#o365-excessive-sso-logon-errors) -- [O365 Suspicious Admin Email Forwarding](#o365-suspicious-admin-email-forwarding) -- [Cloud Provisioning Activity From Previously Unseen City](#cloud-provisioning-activity-from-previously-unseen-city) -- [Detect New Open GCP Storage Buckets](#detect-new-open-gcp-storage-buckets) +- [AWS SAML Update identity provider](#aws-saml-update-identity-provider) +- [Abnormally High Number Of Cloud Infrastructure API Calls](#abnormally-high-number-of-cloud-infrastructure-api-calls) - [Abnormally High Number Of Cloud Instances Destroyed](#abnormally-high-number-of-cloud-instances-destroyed) -- [High Number of Login Failures from a single source](#high-number-of-login-failures-from-a-single-source) +- [Abnormally High Number Of Cloud Instances Launched](#abnormally-high-number-of-cloud-instances-launched) +- [Abnormally High Number Of Cloud Security Group API Calls](#abnormally-high-number-of-cloud-security-group-api-calls) +- [Amazon EKS Kubernetes Pod scan detection](#amazon-eks-kubernetes-pod-scan-detection) +- [Amazon EKS Kubernetes cluster scan detection](#amazon-eks-kubernetes-cluster-scan-detection) +- [Cloud API Calls From Previously Unseen User Roles](#cloud-api-calls-from-previously-unseen-user-roles) +- [Cloud Compute Instance Created By Previously Unseen User](#cloud-compute-instance-created-by-previously-unseen-user) +- [Cloud Compute Instance Created In Previously Unused Region](#cloud-compute-instance-created-in-previously-unused-region) +- [Cloud Compute Instance Created With Previously Unseen Image](#cloud-compute-instance-created-with-previously-unseen-image) +- [Cloud Compute Instance Created With Previously Unseen Instance Type](#cloud-compute-instance-created-with-previously-unseen-instance-type) +- [Cloud Instance Modified By Previously Unseen User](#cloud-instance-modified-by-previously-unseen-user) +- [Cloud Provisioning Activity From Previously Unseen City](#cloud-provisioning-activity-from-previously-unseen-city) +- [Cloud Provisioning Activity From Previously Unseen Country](#cloud-provisioning-activity-from-previously-unseen-country) +- [Cloud Provisioning Activity From Previously Unseen IP Address](#cloud-provisioning-activity-from-previously-unseen-ip-address) +- [Cloud Provisioning Activity From Previously Unseen Region](#cloud-provisioning-activity-from-previously-unseen-region) +- [Detect AWS Console Login by New User](#detect-aws-console-login-by-new-user) - [Detect AWS Console Login by User from New City](#detect-aws-console-login-by-user-from-new-city) - [Detect AWS Console Login by User from New Country](#detect-aws-console-login-by-user-from-new-country) -- [Cloud Instance Modified By Previously Unseen User](#cloud-instance-modified-by-previously-unseen-user) -- [Detect Spike in S3 Bucket deletion](#detect-spike-in-s3-bucket-deletion) -- [AWS Network Access Control List Deleted](#aws-network-access-control-list-deleted) -- [Cloud Compute Instance Created By Previously Unseen User](#cloud-compute-instance-created-by-previously-unseen-user) -- [AWS Detect Users with KMS keys performing encryption S3](#aws-detect-users-with-kms-keys-performing-encryption-s3) -- [O365 Suspicious User Email Forwarding](#o365-suspicious-user-email-forwarding) -- [Cloud API Calls From Previously Unseen User Roles](#cloud-api-calls-from-previously-unseen-user-roles) -- [O365 PST export alert](#o365-pst-export-alert) -- [AWS Network Access Control List Created with All Open Ports](#aws-network-access-control-list-created-with-all-open-ports) -- [Abnormally High Number Of Cloud Instances Launched](#abnormally-high-number-of-cloud-instances-launched) -- [Cloud Provisioning Activity From Previously Unseen Country](#cloud-provisioning-activity-from-previously-unseen-country) -- [Cloud Compute Instance Created With Previously Unseen Instance Type](#cloud-compute-instance-created-with-previously-unseen-instance-type) -- [AWS Detect Users creating keys with encrypt policy without MFA](#aws-detect-users-creating-keys-with-encrypt-policy-without-mfa) -- [O365 Suspicious Rights Delegation](#o365-suspicious-rights-delegation) -- [New container uploaded to AWS ECR](#new-container-uploaded-to-aws-ecr) -- [Detect Spike in blocked Outbound Traffic from your AWS](#detect-spike-in-blocked-outbound-traffic-from-your-aws) -- [Detect New Open S3 buckets](#detect-new-open-s3-buckets) - [Detect AWS Console Login by User from New Region](#detect-aws-console-login-by-user-from-new-region) -- [Detect AWS Console Login by New User](#detect-aws-console-login-by-new-user) -- [Detect Spike in AWS Security Hub Alerts for User](#detect-spike-in-aws-security-hub-alerts-for-user) -- [Cloud Provisioning Activity From Previously Unseen IP Address](#cloud-provisioning-activity-from-previously-unseen-ip-address) -- [O365 Disable MFA](#o365-disable-mfa) -- [Detect New Open S3 Buckets over AWS CLI](#detect-new-open-s3-buckets-over-aws-cli) -- [O365 Excessive Authentication Failures Alert](#o365-excessive-authentication-failures-alert) -- [O365 Added Service Principal](#o365-added-service-principal) -- [Detect Spike in AWS Security Hub Alerts for EC2 Instance](#detect-spike-in-aws-security-hub-alerts-for-ec2-instance) -- [Cloud Compute Instance Created With Previously Unseen Image](#cloud-compute-instance-created-with-previously-unseen-image) -- [O365 Bypass MFA via Trusted IP](#o365-bypass-mfa-via-trusted-ip) -- [Abnormally High Number Of Cloud Infrastructure API Calls](#abnormally-high-number-of-cloud-infrastructure-api-calls) - [Detect GCP Storage access from a new IP](#detect-gcp-storage-access-from-a-new-ip) -- [AWS Cross Account Activity From Previously Unseen Account](#aws-cross-account-activity-from-previously-unseen-account) -- [O365 New Federated Domain Added](#o365-new-federated-domain-added) +- [Detect New Open GCP Storage Buckets](#detect-new-open-gcp-storage-buckets) +- [Detect New Open S3 Buckets over AWS CLI](#detect-new-open-s3-buckets-over-aws-cli) +- [Detect New Open S3 buckets](#detect-new-open-s3-buckets) - [Detect S3 access from a new IP](#detect-s3-access-from-a-new-ip) -- [Cloud Provisioning Activity From Previously Unseen Region](#cloud-provisioning-activity-from-previously-unseen-region) -- [Abnormally High Number Of Cloud Security Group API Calls](#abnormally-high-number-of-cloud-security-group-api-calls) -- [AWS SAML Update identity provider](#aws-saml-update-identity-provider) -- [Kubernetes Azure detect sensitive role access](#kubernetes-azure-detect-sensitive-role-access) -- [Kubernetes AWS detect sensitive role access](#kubernetes-aws-detect-sensitive-role-access) -- [Kubernetes GCP detect sensitive object access](#kubernetes-gcp-detect-sensitive-object-access) -- [Kubernetes Azure scan fingerprint](#kubernetes-azure-scan-fingerprint) -- [Kubernetes AWS detect service accounts forbidden failure access](#kubernetes-aws-detect-service-accounts-forbidden-failure-access) -- [Kubernetes GCP detect most active service accounts by pod](#kubernetes-gcp-detect-most-active-service-accounts-by-pod) -- [aws detect permanent key creation](#aws-detect-permanent-key-creation) -- [GCP Detect high risk permissions by resource and account](#gcp-detect-high-risk-permissions-by-resource-and-account) -- [Kubernetes AWS detect suspicious kubectl calls](#kubernetes-aws-detect-suspicious-kubectl-calls) -- [Kubernetes AWS detect RBAC authorization by account](#kubernetes-aws-detect-rbac-authorization-by-account) -- [Kubernetes Azure detect most active service accounts by pod namespace](#kubernetes-azure-detect-most-active-service-accounts-by-pod-namespace) +- [Detect Spike in AWS Security Hub Alerts for EC2 Instance](#detect-spike-in-aws-security-hub-alerts-for-ec2-instance) +- [Detect Spike in AWS Security Hub Alerts for User](#detect-spike-in-aws-security-hub-alerts-for-user) +- [Detect Spike in S3 Bucket deletion](#detect-spike-in-s3-bucket-deletion) +- [Detect Spike in blocked Outbound Traffic from your AWS](#detect-spike-in-blocked-outbound-traffic-from-your-aws) +- [GCP Detect accounts with high risk roles by project](#gcp-detect-accounts-with-high-risk-roles-by-project) - [GCP Detect gcploit framework](#gcp-detect-gcploit-framework) -- [Kubernetes Azure detect service accounts forbidden failure access](#kubernetes-azure-detect-service-accounts-forbidden-failure-access) -- [Kubernetes AWS detect most active service accounts by pod](#kubernetes-aws-detect-most-active-service-accounts-by-pod) -- [Amazon EKS Kubernetes cluster scan detection](#amazon-eks-kubernetes-cluster-scan-detection) -- [Kubernetes Azure detect RBAC authorization by account](#kubernetes-azure-detect-rbac-authorization-by-account) -- [aws detect attach to role policy](#aws-detect-attach-to-role-policy) -- [AWS EKS Kubernetes cluster sensitive object access](#aws-eks-kubernetes-cluster-sensitive-object-access) +- [GCP Detect high risk permissions by resource and account](#gcp-detect-high-risk-permissions-by-resource-and-account) - [GCP Kubernetes cluster pod scan detection](#gcp-kubernetes-cluster-pod-scan-detection) - [GCP Kubernetes cluster scan detection](#gcp-kubernetes-cluster-scan-detection) -- [Kubernetes GCP detect suspicious kubectl calls](#kubernetes-gcp-detect-suspicious-kubectl-calls) -- [gcp detect oauth token abuse](#gcp-detect-oauth-token-abuse) -- [Kubernetes Azure detect suspicious kubectl calls](#kubernetes-azure-detect-suspicious-kubectl-calls) -- [Kubernetes GCP detect sensitive role access](#kubernetes-gcp-detect-sensitive-role-access) -- [aws detect sts get session token abuse](#aws-detect-sts-get-session-token-abuse) -- [Amazon EKS Kubernetes Pod scan detection](#amazon-eks-kubernetes-pod-scan-detection) -- [aws detect role creation](#aws-detect-role-creation) -- [Kubernetes GCP detect RBAC authorizations by account](#kubernetes-gcp-detect-rbac-authorizations-by-account) -- [Kubernetes Azure pod scan fingerprint](#kubernetes-azure-pod-scan-fingerprint) -- [aws detect sts assume role abuse](#aws-detect-sts-assume-role-abuse) +- [High Number of Login Failures from a single source](#high-number-of-login-failures-from-a-single-source) +- [Kubernetes AWS detect RBAC authorization by account](#kubernetes-aws-detect-rbac-authorization-by-account) +- [Kubernetes AWS detect most active service accounts by pod](#kubernetes-aws-detect-most-active-service-accounts-by-pod) +- [Kubernetes AWS detect sensitive role access](#kubernetes-aws-detect-sensitive-role-access) +- [Kubernetes AWS detect service accounts forbidden failure access](#kubernetes-aws-detect-service-accounts-forbidden-failure-access) +- [Kubernetes AWS detect suspicious kubectl calls](#kubernetes-aws-detect-suspicious-kubectl-calls) +- [Kubernetes Azure detect RBAC authorization by account](#kubernetes-azure-detect-rbac-authorization-by-account) +- [Kubernetes Azure detect most active service accounts by pod namespace](#kubernetes-azure-detect-most-active-service-accounts-by-pod-namespace) - [Kubernetes Azure detect sensitive object access](#kubernetes-azure-detect-sensitive-object-access) -- [GCP Detect accounts with high risk roles by project](#gcp-detect-accounts-with-high-risk-roles-by-project) +- [Kubernetes Azure detect sensitive role access](#kubernetes-azure-detect-sensitive-role-access) +- [Kubernetes Azure detect service accounts forbidden failure access](#kubernetes-azure-detect-service-accounts-forbidden-failure-access) +- [Kubernetes Azure detect suspicious kubectl calls](#kubernetes-azure-detect-suspicious-kubectl-calls) +- [Kubernetes Azure pod scan fingerprint](#kubernetes-azure-pod-scan-fingerprint) +- [Kubernetes Azure scan fingerprint](#kubernetes-azure-scan-fingerprint) +- [Kubernetes GCP detect RBAC authorizations by account](#kubernetes-gcp-detect-rbac-authorizations-by-account) +- [Kubernetes GCP detect most active service accounts by pod](#kubernetes-gcp-detect-most-active-service-accounts-by-pod) +- [Kubernetes GCP detect sensitive object access](#kubernetes-gcp-detect-sensitive-object-access) +- [Kubernetes GCP detect sensitive role access](#kubernetes-gcp-detect-sensitive-role-access) - [Kubernetes GCP detect service accounts forbidden failure access](#kubernetes-gcp-detect-service-accounts-forbidden-failure-access) +- [Kubernetes GCP detect suspicious kubectl calls](#kubernetes-gcp-detect-suspicious-kubectl-calls) +- [New container uploaded to AWS ECR](#new-container-uploaded-to-aws-ecr) +- [O365 Add App Role Assignment Grant User](#o365-add-app-role-assignment-grant-user) +- [O365 Added Service Principal](#o365-added-service-principal) +- [O365 Bypass MFA via Trusted IP](#o365-bypass-mfa-via-trusted-ip) +- [O365 Disable MFA](#o365-disable-mfa) +- [O365 Excessive Authentication Failures Alert](#o365-excessive-authentication-failures-alert) +- [O365 Excessive SSO logon errors](#o365-excessive-sso-logon-errors) +- [O365 New Federated Domain Added](#o365-new-federated-domain-added) +- [O365 PST export alert](#o365-pst-export-alert) +- [O365 Suspicious Admin Email Forwarding](#o365-suspicious-admin-email-forwarding) +- [O365 Suspicious Rights Delegation](#o365-suspicious-rights-delegation) +- [O365 Suspicious User Email Forwarding](#o365-suspicious-user-email-forwarding) +- [aws detect attach to role policy](#aws-detect-attach-to-role-policy) +- [aws detect permanent key creation](#aws-detect-permanent-key-creation) +- [aws detect role creation](#aws-detect-role-creation) +- [aws detect sts assume role abuse](#aws-detect-sts-assume-role-abuse) +- [aws detect sts get session token abuse](#aws-detect-sts-get-session-token-abuse) +- [gcp detect oauth token abuse](#gcp-detect-oauth-token-abuse)
## Endpoint
View -- [Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules](#reconnaissance-of-process-or-service-hijacking-opportunities-via-mimikatz-modules) -- [Schtasks used for forcing a reboot](#schtasks-used-for-forcing-a-reboot) -- [Single Letter Process On Endpoint](#single-letter-process-on-endpoint) -- [Suspicious Rundll32 no CommandLine Arguments](#suspicious-rundll32-no-commandline-arguments) -- [Detect Rare Executables](#detect-rare-executables) -- [Dump LSASS via comsvcs DLL](#dump-lsass-via-comsvcs-dll) -- [Malicious PowerShell Process - Connect To Internet With Hidden Window](#malicious-powershell-process---connect-to-internet-with-hidden-window) -- [Suspicious Rundll32 dllregisterserver](#suspicious-rundll32-dllregisterserver) -- [Illegal Access To User Content via PowerSploit modules](#illegal-access-to-user-content-via-powersploit-modules) -- [Reconnaissance of Access and Persistence Opportunities via PowerSploit modules](#reconnaissance-of-access-and-persistence-opportunities-via-powersploit-modules) -- [Shim Database File Creation](#shim-database-file-creation) -- [Processes launching netsh](#processes-launching-netsh) -- [Credential Extraction indicative of use of DSInternals credential conversion modules](#credential-extraction-indicative-of-use-of-dsinternals-credential-conversion-modules) -- [Schtasks scheduling job on remote system](#schtasks-scheduling-job-on-remote-system) -- [Certutil exe certificate extraction](#certutil-exe-certificate-extraction) -- [Unusually Long Command Line](#unusually-long-command-line) -- [Reconnaissance of Connectivity via PowerSploit modules](#reconnaissance-of-connectivity-via-powersploit-modules) -- [Create Remote Thread into LSASS](#create-remote-thread-into-lsass) -- [Reconnaissance and Access to Shared Resources via PowerSploit modules](#reconnaissance-and-access-to-shared-resources-via-powersploit-modules) -- [Attempted Credential Dump From Registry via Reg exe](#attempted-credential-dump-from-registry-via-reg-exe) -- [Detect processes used for System Network Configuration Discovery](#detect-processes-used-for-system-network-configuration-discovery) -- [Setting Credentials via DSInternals modules](#setting-credentials-via-dsinternals-modules) -- [Common Ransomware Extensions](#common-ransomware-extensions) -- [Assessment of Credential Strength via DSInternals modules](#assessment-of-credential-strength-via-dsinternals-modules) -- [Detect Rundll32 Application Control Bypass - setupapi](#detect-rundll32-application-control-bypass---setupapi) -- [Suspicious microsoft workflow compiler rename](#suspicious-microsoft-workflow-compiler-rename) -- [Execution of File with Multiple Extensions](#execution-of-file-with-multiple-extensions) -- [Illegal Management of Active Directory Elements and Policies via DSInternals modules](#illegal-management-of-active-directory-elements-and-policies-via-dsinternals-modules) -- [Detect Prohibited Applications Spawning cmd exe](#detect-prohibited-applications-spawning-cmd-exe) -- [Illegal Service and Process Control via PowerSploit modules](#illegal-service-and-process-control-via-powersploit-modules) -- [Detect Computer Changed with Anonymous Account](#detect-computer-changed-with-anonymous-account) -- [Suspicious writes to windows Recycle Bin](#suspicious-writes-to-windows-recycle-bin) -- [BCDEdit Failure Recovery Modification](#bcdedit-failure-recovery-modification) - [Access LSASS Memory for Dump Creation](#access-lsass-memory-for-dump-creation) -- [Ntdsutil export ntds](#ntdsutil-export-ntds) -- [Detect Regsvcs with No Command Line Arguments](#detect-regsvcs-with-no-command-line-arguments) -- [Credential Extraction indicative of use of DSInternals modules](#credential-extraction-indicative-of-use-of-dsinternals-modules) -- [Attempted Credential Dump From Registry via Reg exe](#attempted-credential-dump-from-registry-via-reg-exe) -- [Reg exe Manipulating Windows Services Registry Keys](#reg-exe-manipulating-windows-services-registry-keys) -- [Suspicious Rundll32 Rename](#suspicious-rundll32-rename) -- [Credential Dumping via Symlink to Shadow Copy](#credential-dumping-via-symlink-to-shadow-copy) -- [Malicious PowerShell Process - Encoded Command](#malicious-powershell-process---encoded-command) -- [Reconnaissance of Defensive Tools via PowerSploit modules](#reconnaissance-of-defensive-tools-via-powersploit-modules) -- [File with Samsam Extension](#file-with-samsam-extension) -- [Script Execution via WMI](#script-execution-via-wmi) -- [Process Execution via WMI](#process-execution-via-wmi) -- [Illegal Privilege Elevation via Mimikatz modules](#illegal-privilege-elevation-via-mimikatz-modules) -- [Detect Regsvcs with Network Connection](#detect-regsvcs-with-network-connection) -- [Credential Extraction indicative of FGDump and CacheDump with s option](#credential-extraction-indicative-of-fgdump-and-cachedump-with-s-option) -- [Monitor Registry Keys for Print Monitors](#monitor-registry-keys-for-print-monitors) -- [Shim Database Installation With Suspicious Parameters](#shim-database-installation-with-suspicious-parameters) -- [Suspicious Regsvr32 Register Suspicious Path](#suspicious-regsvr32-register-suspicious-path) -- [Detect Regsvr32 Application Control Bypass](#detect-regsvr32-application-control-bypass) -- [Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules](#reconnaissance-and-access-to-active-directoty-infrastructure-via-powersploit-modules) -- [Probing Access with Stolen Credentials via PowerSploit modules](#probing-access-with-stolen-credentials-via-powersploit-modules) -- [Unusually Long Command Line - MLTK](#unusually-long-command-line---mltk) -- [Detect Credential Dumping through LSASS access](#detect-credential-dumping-through-lsass-access) -- [Detect Rundll32 Inline HTA Execution](#detect-rundll32-inline-hta-execution) -- [Illegal Service and Process Control via Mimikatz modules](#illegal-service-and-process-control-via-mimikatz-modules) -- [Registry Keys for Creating SHIM Databases](#registry-keys-for-creating-shim-databases) -- [Short Lived Windows Accounts](#short-lived-windows-accounts) -- [Credential Extraction indicative of use of PowerSploit modules](#credential-extraction-indicative-of-use-of-powersploit-modules) -- [Windows Event Log Cleared](#windows-event-log-cleared) -- [Detect Prohibited Applications Spawning cmd exe](#detect-prohibited-applications-spawning-cmd-exe) -- [Unload Sysmon Filter Driver](#unload-sysmon-filter-driver) -- [Unusually Long Command Line](#unusually-long-command-line) -- [Detect Rundll32 Application Control Bypass - advpack](#detect-rundll32-application-control-bypass---advpack) -- [Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules](#reconnaissance-and-access-to-accounts-groups-and-policies-via-powersploit-modules) -- [Create local admin accounts using net exe](#create-local-admin-accounts-using-net-exe) -- [Reconnaissance and Access to Computers via Mimikatz modules](#reconnaissance-and-access-to-computers-via-mimikatz-modules) -- [Detect Regasm with Network Connection](#detect-regasm-with-network-connection) -- [System Process Running from Unexpected Location](#system-process-running-from-unexpected-location) -- [Rare Parent-Child Process Relationship](#rare-parent-child-process-relationship) -- [Setting Credentials via PowerSploit modules](#setting-credentials-via-powersploit-modules) -- [Dump LSASS via procdump](#dump-lsass-via-procdump) -- [Disabling Remote User Account Control](#disabling-remote-user-account-control) -- [Reconnaissance of Credential Stores and Services via Mimikatz modules](#reconnaissance-of-credential-stores-and-services-via-mimikatz-modules) -- [Creation of Shadow Copy with wmic and powershell](#creation-of-shadow-copy-with-wmic-and-powershell) -- [Detect HTML Help Renamed](#detect-html-help-renamed) -- [Windows Security Account Manager Stopped](#windows-security-account-manager-stopped) -- [Reconnaissance and Access to Accounts and Groups via Mimikatz modules](#reconnaissance-and-access-to-accounts-and-groups-via-mimikatz-modules) -- [Detect Rundll32 Application Control Bypass - syssetup](#detect-rundll32-application-control-bypass---syssetup) -- [System Processes Run From Unexpected Locations](#system-processes-run-from-unexpected-locations) -- [USN Journal Deletion](#usn-journal-deletion) -- [Detect Regasm Spawning a Process](#detect-regasm-spawning-a-process) -- [Credential Extraction indicative of Lazagne command line options](#credential-extraction-indicative-of-lazagne-command-line-options) -- [Credential Extraction indicative of FGDump and CacheDump with v option](#credential-extraction-indicative-of-fgdump-and-cachedump-with-v-option) -- [Detect HTML Help Using InfoTech Storage Handlers](#detect-html-help-using-infotech-storage-handlers) -- [Common Ransomware Notes](#common-ransomware-notes) -- [Reconnaissance and Access to Processes and Services via Mimikatz modules](#reconnaissance-and-access-to-processes-and-services-via-mimikatz-modules) -- [Sc exe Manipulating Windows Services](#sc-exe-manipulating-windows-services) -- [Overwriting Accessibility Binaries](#overwriting-accessibility-binaries) -- [Detect Regsvcs Spawning a Process](#detect-regsvcs-spawning-a-process) -- [Detect MSHTA Url in Command Line](#detect-mshta-url-in-command-line) -- [WBAdmin Delete System Backups](#wbadmin-delete-system-backups) -- [Illegal Management of Computers and Active Directory Elements via PowerSploit modules](#illegal-management-of-computers-and-active-directory-elements-via-powersploit-modules) -- [Suspicious Reg exe Process](#suspicious-reg-exe-process) -- [Detect New Local Admin account](#detect-new-local-admin-account) -- [Reconnaissance and Access to Computers and Domains via PowerSploit modules](#reconnaissance-and-access-to-computers-and-domains-via-powersploit-modules) -- [Suspicious msbuild path](#suspicious-msbuild-path) -- [Credential Extraction native Microsoft debuggers peek into the kernel](#credential-extraction-native-microsoft-debuggers-peek-into-the-kernel) +- [Applying Stolen Credentials via Mimikatz modules](#applying-stolen-credentials-via-mimikatz-modules) +- [Applying Stolen Credentials via PowerSploit modules](#applying-stolen-credentials-via-powersploit-modules) +- [Assessment of Credential Strength via DSInternals modules](#assessment-of-credential-strength-via-dsinternals-modules) +- [Attempt To Add Certificate To Untrusted Store](#attempt-to-add-certificate-to-untrusted-store) - [Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass](#attempt-to-set-default-powershell-execution-policy-to-unrestricted-or-bypass) -- [Remote Process Instantiation via WMI](#remote-process-instantiation-via-wmi) -- [Detect mshta inline hta execution](#detect-mshta-inline-hta-execution) -- [Samsam Test File Write](#samsam-test-file-write) -- [Suspicious microsoft workflow compiler usage](#suspicious-microsoft-workflow-compiler-usage) -- [Illegal Enabling or Disabling of Accounts via DSInternals modules](#illegal-enabling-or-disabling-of-accounts-via-dsinternals-modules) -- [Deleting Shadow Copies](#deleting-shadow-copies) -- [Reconnaissance and Access to Shared Resources via Mimikatz modules](#reconnaissance-and-access-to-shared-resources-via-mimikatz-modules) - [Attempt To Stop Security Service](#attempt-to-stop-security-service) +- [Attempted Credential Dump From Registry via Reg exe](#attempted-credential-dump-from-registry-via-reg-exe) +- [Attempted Credential Dump From Registry via Reg exe](#attempted-credential-dump-from-registry-via-reg-exe) +- [BCDEdit Failure Recovery Modification](#bcdedit-failure-recovery-modification) +- [Batch File Write to System32](#batch-file-write-to-system32) +- [Certutil exe certificate extraction](#certutil-exe-certificate-extraction) +- [Child Processes of Spoolsv exe](#child-processes-of-spoolsv-exe) +- [Common Ransomware Extensions](#common-ransomware-extensions) +- [Common Ransomware Notes](#common-ransomware-notes) +- [Create Remote Thread into LSASS](#create-remote-thread-into-lsass) +- [Create local admin accounts using net exe](#create-local-admin-accounts-using-net-exe) +- [Create or delete windows shares using net exe](#create-or-delete-windows-shares-using-net-exe) +- [Creation of Shadow Copy](#creation-of-shadow-copy) +- [Creation of Shadow Copy with wmic and powershell](#creation-of-shadow-copy-with-wmic-and-powershell) +- [Creation of lsass Dump with Taskmgr](#creation-of-lsass-dump-with-taskmgr) +- [Credential Dumping via Copy Command from Shadow Copy](#credential-dumping-via-copy-command-from-shadow-copy) +- [Credential Dumping via Symlink to Shadow Copy](#credential-dumping-via-symlink-to-shadow-copy) +- [Credential Extraction indicative of FGDump and CacheDump with s option](#credential-extraction-indicative-of-fgdump-and-cachedump-with-s-option) +- [Credential Extraction indicative of FGDump and CacheDump with v option](#credential-extraction-indicative-of-fgdump-and-cachedump-with-v-option) +- [Credential Extraction indicative of Lazagne command line options](#credential-extraction-indicative-of-lazagne-command-line-options) +- [Credential Extraction indicative of use of DSInternals credential conversion modules](#credential-extraction-indicative-of-use-of-dsinternals-credential-conversion-modules) +- [Credential Extraction indicative of use of DSInternals modules](#credential-extraction-indicative-of-use-of-dsinternals-modules) +- [Credential Extraction indicative of use of Mimikatz modules](#credential-extraction-indicative-of-use-of-mimikatz-modules) +- [Credential Extraction indicative of use of PowerSploit modules](#credential-extraction-indicative-of-use-of-powersploit-modules) +- [Credential Extraction native Microsoft debuggers peek into the kernel](#credential-extraction-native-microsoft-debuggers-peek-into-the-kernel) +- [Credential Extraction native Microsoft debuggers via z command line option](#credential-extraction-native-microsoft-debuggers-via-z-command-line-option) +- [Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals](#credential-extraction-via-get-addbaccount-module-present-in-powersploit-and-dsinternals) +- [Deleting Shadow Copies](#deleting-shadow-copies) +- [Detect Activity Related to Pass the Hash Attacks](#detect-activity-related-to-pass-the-hash-attacks) +- [Detect Baron Samedit CVE-2021-3156](#detect-baron-samedit-cve-2021-3156) +- [Detect Baron Samedit CVE-2021-3156 Segfault](#detect-baron-samedit-cve-2021-3156-segfault) +- [Detect Baron Samedit CVE-2021-3156 via OSQuery](#detect-baron-samedit-cve-2021-3156-via-osquery) +- [Detect Computer Changed with Anonymous Account](#detect-computer-changed-with-anonymous-account) +- [Detect Credential Dumping through LSASS access](#detect-credential-dumping-through-lsass-access) +- [Detect Dump LSASS Memory using comsvcs](#detect-dump-lsass-memory-using-comsvcs) +- [Detect Excessive Account Lockouts From Endpoint](#detect-excessive-account-lockouts-from-endpoint) +- [Detect Excessive User Account Lockouts](#detect-excessive-user-account-lockouts) +- [Detect HTML Help Renamed](#detect-html-help-renamed) +- [Detect HTML Help Spawn Child Process](#detect-html-help-spawn-child-process) +- [Detect HTML Help URL in Command Line](#detect-html-help-url-in-command-line) +- [Detect HTML Help Using InfoTech Storage Handlers](#detect-html-help-using-infotech-storage-handlers) +- [Detect Kerberoasting](#detect-kerberoasting) +- [Detect MSHTA Url in Command Line](#detect-mshta-url-in-command-line) +- [Detect New Local Admin account](#detect-new-local-admin-account) +- [Detect Oulook exe writing a zip file](#detect-oulook-exe-writing-a--zip-file) +- [Detect Pass the Hash](#detect-pass-the-hash) +- [Detect Path Interception By Creation Of program exe](#detect-path-interception-by-creation-of-program-exe) +- [Detect Prohibited Applications Spawning cmd exe](#detect-prohibited-applications-spawning-cmd-exe) +- [Detect Prohibited Applications Spawning cmd exe](#detect-prohibited-applications-spawning-cmd-exe) +- [Detect PsExec With accepteula Flag](#detect-psexec-with-accepteula-flag) +- [Detect Rare Executables](#detect-rare-executables) +- [Detect Regasm Spawning a Process](#detect-regasm-spawning-a-process) +- [Detect Regasm with Network Connection](#detect-regasm-with-network-connection) +- [Detect Regasm with no Command Line Arguments](#detect-regasm-with-no-command-line-arguments) +- [Detect Regsvcs Spawning a Process](#detect-regsvcs-spawning-a-process) +- [Detect Regsvcs with Network Connection](#detect-regsvcs-with-network-connection) +- [Detect Regsvcs with No Command Line Arguments](#detect-regsvcs-with-no-command-line-arguments) +- [Detect Regsvr32 Application Control Bypass](#detect-regsvr32-application-control-bypass) +- [Detect Rundll32 Application Control Bypass - advpack](#detect-rundll32-application-control-bypass---advpack) +- [Detect Rundll32 Application Control Bypass - setupapi](#detect-rundll32-application-control-bypass---setupapi) +- [Detect Rundll32 Application Control Bypass - syssetup](#detect-rundll32-application-control-bypass---syssetup) +- [Detect Rundll32 Inline HTA Execution](#detect-rundll32-inline-hta-execution) +- [Detect Use of cmd exe to Launch Script Interpreters](#detect-use-of-cmd-exe-to-launch-script-interpreters) +- [Detect mshta inline hta execution](#detect-mshta-inline-hta-execution) +- [Detect mshta renamed](#detect-mshta-renamed) +- [Detect processes used for System Network Configuration Discovery](#detect-processes-used-for-system-network-configuration-discovery) +- [Detection of tools built by NirSoft](#detection-of-tools-built-by-nirsoft) +- [Disabling Remote User Account Control](#disabling-remote-user-account-control) +- [Dump LSASS via comsvcs DLL](#dump-lsass-via-comsvcs-dll) +- [Dump LSASS via procdump](#dump-lsass-via-procdump) +- [Dump LSASS via procdump Rename](#dump-lsass-via-procdump-rename) +- [Execution of File with Multiple Extensions](#execution-of-file-with-multiple-extensions) +- [File with Samsam Extension](#file-with-samsam-extension) +- [First Time Seen Child Process of Zoom](#first-time-seen-child-process-of-zoom) +- [First Time Seen Running Windows Service](#first-time-seen-running-windows-service) +- [First time seen command line argument](#first-time-seen-command-line-argument) +- [Hiding Files And Directories With Attrib exe](#hiding-files-and-directories-with-attrib-exe) +- [Illegal Access To User Content via PowerSploit modules](#illegal-access-to-user-content-via-powersploit-modules) +- [Illegal Account Creation via PowerSploit modules](#illegal-account-creation-via-powersploit-modules) +- [Illegal Deletion of Logs via Mimikatz modules](#illegal-deletion-of-logs-via-mimikatz-modules) +- [Illegal Enabling or Disabling of Accounts via DSInternals modules](#illegal-enabling-or-disabling-of-accounts-via-dsinternals-modules) +- [Illegal Management of Active Directory Elements and Policies via DSInternals modules](#illegal-management-of-active-directory-elements-and-policies-via-dsinternals-modules) +- [Illegal Management of Computers and Active Directory Elements via PowerSploit modules](#illegal-management-of-computers-and-active-directory-elements-via-powersploit-modules) +- [Illegal Privilege Elevation and Persistence via PowerSploit modules](#illegal-privilege-elevation-and-persistence-via-powersploit-modules) +- [Illegal Privilege Elevation via Mimikatz modules](#illegal-privilege-elevation-via-mimikatz-modules) +- [Illegal Service and Process Control via Mimikatz modules](#illegal-service-and-process-control-via-mimikatz-modules) +- [Illegal Service and Process Control via PowerSploit modules](#illegal-service-and-process-control-via-powersploit-modules) +- [Kerberoasting spn request with RC4 encryption](#kerberoasting-spn-request-with-rc4-encryption) +- [MacOS - Re-opened Applications](#macos---re-opened-applications) +- [Malicious PowerShell Process - Connect To Internet With Hidden Window](#malicious-powershell-process---connect-to-internet-with-hidden-window) +- [Malicious PowerShell Process - Encoded Command](#malicious-powershell-process---encoded-command) +- [Malicious PowerShell Process - Execution Policy Bypass](#malicious-powershell-process---execution-policy-bypass) +- [Malicious PowerShell Process With Obfuscation Techniques](#malicious-powershell-process-with-obfuscation-techniques) +- [Monitor Registry Keys for Print Monitors](#monitor-registry-keys-for-print-monitors) +- [More than usual number of LOLBAS applications in short time period](#more-than-usual-number-of-lolbas-applications-in-short-time-period) +- [NLTest Domain Trust Discovery](#nltest-domain-trust-discovery) +- [Ntdsutil export ntds](#ntdsutil-export-ntds) +- [Overwriting Accessibility Binaries](#overwriting-accessibility-binaries) +- [Probing Access with Stolen Credentials via PowerSploit modules](#probing-access-with-stolen-credentials-via-powersploit-modules) +- [Process Creating LNK file in Suspicious Location](#process-creating-lnk-file-in-suspicious-location) +- [Process Execution via WMI](#process-execution-via-wmi) +- [Processes Tapping Keyboard Events](#processes-tapping-keyboard-events) +- [Processes launching netsh](#processes-launching-netsh) +- [Rare Parent-Child Process Relationship](#rare-parent-child-process-relationship) +- [Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules](#reconnaissance-and-access-to-accounts-groups-and-policies-via-powersploit-modules) +- [Reconnaissance and Access to Accounts and Groups via Mimikatz modules](#reconnaissance-and-access-to-accounts-and-groups-via-mimikatz-modules) +- [Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules](#reconnaissance-and-access-to-active-directoty-infrastructure-via-powersploit-modules) +- [Reconnaissance and Access to Computers and Domains via PowerSploit modules](#reconnaissance-and-access-to-computers-and-domains-via-powersploit-modules) +- [Reconnaissance and Access to Computers via Mimikatz modules](#reconnaissance-and-access-to-computers-via-mimikatz-modules) +- [Reconnaissance and Access to Operating System Elements via PowerSploit modules](#reconnaissance-and-access-to-operating-system-elements-via-powersploit-modules) +- [Reconnaissance and Access to Processes and Services via Mimikatz modules](#reconnaissance-and-access-to-processes-and-services-via-mimikatz-modules) +- [Reconnaissance and Access to Shared Resources via Mimikatz modules](#reconnaissance-and-access-to-shared-resources-via-mimikatz-modules) +- [Reconnaissance and Access to Shared Resources via PowerSploit modules](#reconnaissance-and-access-to-shared-resources-via-powersploit-modules) +- [Reconnaissance of Access and Persistence Opportunities via PowerSploit modules](#reconnaissance-of-access-and-persistence-opportunities-via-powersploit-modules) +- [Reconnaissance of Connectivity via PowerSploit modules](#reconnaissance-of-connectivity-via-powersploit-modules) +- [Reconnaissance of Credential Stores and Services via Mimikatz modules](#reconnaissance-of-credential-stores-and-services-via-mimikatz-modules) +- [Reconnaissance of Defensive Tools via PowerSploit modules](#reconnaissance-of-defensive-tools-via-powersploit-modules) +- [Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules](#reconnaissance-of-privilege-escalation-opportunities-via-powersploit-modules) +- [Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules](#reconnaissance-of-process-or-service-hijacking-opportunities-via-mimikatz-modules) +- [Reg exe Manipulating Windows Services Registry Keys](#reg-exe-manipulating-windows-services-registry-keys) +- [Registry Keys Used For Persistence](#registry-keys-used-for-persistence) +- [Registry Keys Used For Privilege Escalation](#registry-keys-used-for-privilege-escalation) +- [Registry Keys for Creating SHIM Databases](#registry-keys-for-creating-shim-databases) +- [Remote Desktop Process Running On System](#remote-desktop-process-running-on-system) +- [Remote Process Instantiation via WMI](#remote-process-instantiation-via-wmi) - [RunDLL Loading DLL By Ordinal](#rundll-loading-dll-by-ordinal) - [Ryuk Test Files Detected](#ryuk-test-files-detected) -- [Credential Extraction indicative of use of Mimikatz modules](#credential-extraction-indicative-of-use-of-mimikatz-modules) -- [Credential Dumping via Copy Command from Shadow Copy](#credential-dumping-via-copy-command-from-shadow-copy) +- [Samsam Test File Write](#samsam-test-file-write) +- [Sc exe Manipulating Windows Services](#sc-exe-manipulating-windows-services) - [Scheduled Task Deleted Or Created via CMD](#scheduled-task-deleted-or-created-via-cmd) -- [Suspicious wevtutil Usage](#suspicious-wevtutil-usage) -- [WMI Permanent Event Subscription - Sysmon](#wmi-permanent-event-subscription---sysmon) -- [Malicious PowerShell Process With Obfuscation Techniques](#malicious-powershell-process-with-obfuscation-techniques) -- [Detect mshta renamed](#detect-mshta-renamed) -- [Suspicious Rundll32 StartW](#suspicious-rundll32-startw) -- [Detect HTML Help Spawn Child Process](#detect-html-help-spawn-child-process) -- [Detect Kerberoasting](#detect-kerberoasting) -- [Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules](#reconnaissance-of-privilege-escalation-opportunities-via-powersploit-modules) -- [Applying Stolen Credentials via Mimikatz modules](#applying-stolen-credentials-via-mimikatz-modules) -- [Detect HTML Help URL in Command Line](#detect-html-help-url-in-command-line) -- [Detect Use of cmd exe to Launch Script Interpreters](#detect-use-of-cmd-exe-to-launch-script-interpreters) -- [Illegal Deletion of Logs via Mimikatz modules](#illegal-deletion-of-logs-via-mimikatz-modules) -- [Detect Excessive User Account Lockouts](#detect-excessive-user-account-lockouts) -- [Suspicious MSBuild Rename](#suspicious-msbuild-rename) -- [Attempt To Add Certificate To Untrusted Store](#attempt-to-add-certificate-to-untrusted-store) -- [Illegal Account Creation via PowerSploit modules](#illegal-account-creation-via-powersploit-modules) -- [System Information Discovery Detection](#system-information-discovery-detection) -- [Illegal Privilege Elevation and Persistence via PowerSploit modules](#illegal-privilege-elevation-and-persistence-via-powersploit-modules) -- [Detect Activity Related to Pass the Hash Attacks](#detect-activity-related-to-pass-the-hash-attacks) -- [Suspicious mshta child process](#suspicious-mshta-child-process) -- [Detect Regasm with no Command Line Arguments](#detect-regasm-with-no-command-line-arguments) -- [Malicious PowerShell Process - Execution Policy Bypass](#malicious-powershell-process---execution-policy-bypass) -- [Suspicious MSBuild Spawn](#suspicious-msbuild-spawn) -- [Process Creating LNK file in Suspicious Location](#process-creating-lnk-file-in-suspicious-location) -- [Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals](#credential-extraction-via-get-addbaccount-module-present-in-powersploit-and-dsinternals) -- [Batch File Write to System32](#batch-file-write-to-system32) -- [Detect Dump LSASS Memory using comsvcs](#detect-dump-lsass-memory-using-comsvcs) -- [Create or delete windows shares using net exe](#create-or-delete-windows-shares-using-net-exe) -- [NLTest Domain Trust Discovery](#nltest-domain-trust-discovery) -- [Creation of Shadow Copy](#creation-of-shadow-copy) -- [Registry Keys Used For Privilege Escalation](#registry-keys-used-for-privilege-escalation) -- [Detect Excessive Account Lockouts From Endpoint](#detect-excessive-account-lockouts-from-endpoint) -- [Credential Extraction native Microsoft debuggers via z command line option](#credential-extraction-native-microsoft-debuggers-via-z-command-line-option) -- [Creation of lsass Dump with Taskmgr](#creation-of-lsass-dump-with-taskmgr) -- [Hiding Files And Directories With Attrib exe](#hiding-files-and-directories-with-attrib-exe) -- [First time seen command line argument](#first-time-seen-command-line-argument) -- [Applying Stolen Credentials via PowerSploit modules](#applying-stolen-credentials-via-powersploit-modules) -- [Detect PsExec With accepteula Flag](#detect-psexec-with-accepteula-flag) -- [Detect Path Interception By Creation Of program exe](#detect-path-interception-by-creation-of-program-exe) -- [Suspicious mshta spawn](#suspicious-mshta-spawn) -- [Detect Pass the Hash](#detect-pass-the-hash) -- [Reconnaissance and Access to Operating System Elements via PowerSploit modules](#reconnaissance-and-access-to-operating-system-elements-via-powersploit-modules) -- [Registry Keys Used For Persistence](#registry-keys-used-for-persistence) -- [Windows AdFind Exe](#windows-adfind-exe) -- [More than usual number of LOLBAS applications in short time period](#more-than-usual-number-of-lolbas-applications-in-short-time-period) +- [Schtasks scheduling job on remote system](#schtasks-scheduling-job-on-remote-system) +- [Schtasks used for forcing a reboot](#schtasks-used-for-forcing-a-reboot) +- [Script Execution via WMI](#script-execution-via-wmi) +- [Setting Credentials via DSInternals modules](#setting-credentials-via-dsinternals-modules) - [Setting Credentials via Mimikatz modules](#setting-credentials-via-mimikatz-modules) -- [Dump LSASS via procdump Rename](#dump-lsass-via-procdump-rename) -- [First Time Seen Child Process of Zoom](#first-time-seen-child-process-of-zoom) -- [Kerberoasting spn request with RC4 encryption](#kerberoasting-spn-request-with-rc4-encryption) -- [Processes Tapping Keyboard Events](#processes-tapping-keyboard-events) -- [Child Processes of Spoolsv exe](#child-processes-of-spoolsv-exe) -- [WMI Permanent Event Subscription](#wmi-permanent-event-subscription) -- [Detect Baron Samedit CVE-2021-3156 Segfault](#detect-baron-samedit-cve-2021-3156-segfault) +- [Setting Credentials via PowerSploit modules](#setting-credentials-via-powersploit-modules) +- [Shim Database File Creation](#shim-database-file-creation) +- [Shim Database Installation With Suspicious Parameters](#shim-database-installation-with-suspicious-parameters) +- [Short Lived Windows Accounts](#short-lived-windows-accounts) +- [Single Letter Process On Endpoint](#single-letter-process-on-endpoint) - [Spike in File Writes](#spike-in-file-writes) -- [Detect Baron Samedit CVE-2021-3156 via OSQuery](#detect-baron-samedit-cve-2021-3156-via-osquery) -- [Detection of tools built by NirSoft](#detection-of-tools-built-by-nirsoft) -- [Detect Oulook exe writing a zip file](#detect-oulook-exe-writing-a--zip-file) -- [First Time Seen Running Windows Service](#first-time-seen-running-windows-service) -- [WMI Temporary Event Subscription](#wmi-temporary-event-subscription) - [Sunburst Correlation DLL and Network Event](#sunburst-correlation-dll-and-network-event) -- [MacOS - Re-opened Applications](#macos---re-opened-applications) -- [Detect Baron Samedit CVE-2021-3156](#detect-baron-samedit-cve-2021-3156) -- [Remote Desktop Process Running On System](#remote-desktop-process-running-on-system) +- [Suspicious MSBuild Rename](#suspicious-msbuild-rename) +- [Suspicious MSBuild Spawn](#suspicious-msbuild-spawn) +- [Suspicious Reg exe Process](#suspicious-reg-exe-process) +- [Suspicious Regsvr32 Register Suspicious Path](#suspicious-regsvr32-register-suspicious-path) +- [Suspicious Rundll32 Rename](#suspicious-rundll32-rename) +- [Suspicious Rundll32 StartW](#suspicious-rundll32-startw) +- [Suspicious Rundll32 dllregisterserver](#suspicious-rundll32-dllregisterserver) +- [Suspicious Rundll32 no CommandLine Arguments](#suspicious-rundll32-no-commandline-arguments) +- [Suspicious microsoft workflow compiler rename](#suspicious-microsoft-workflow-compiler-rename) +- [Suspicious microsoft workflow compiler usage](#suspicious-microsoft-workflow-compiler-usage) +- [Suspicious msbuild path](#suspicious-msbuild-path) +- [Suspicious mshta child process](#suspicious-mshta-child-process) +- [Suspicious mshta spawn](#suspicious-mshta-spawn) +- [Suspicious wevtutil Usage](#suspicious-wevtutil-usage) +- [Suspicious writes to windows Recycle Bin](#suspicious-writes-to-windows-recycle-bin) +- [System Information Discovery Detection](#system-information-discovery-detection) +- [System Process Running from Unexpected Location](#system-process-running-from-unexpected-location) +- [System Processes Run From Unexpected Locations](#system-processes-run-from-unexpected-locations) +- [USN Journal Deletion](#usn-journal-deletion) +- [Unload Sysmon Filter Driver](#unload-sysmon-filter-driver) +- [Unusually Long Command Line](#unusually-long-command-line) +- [Unusually Long Command Line](#unusually-long-command-line) +- [Unusually Long Command Line - MLTK](#unusually-long-command-line---mltk) +- [WBAdmin Delete System Backups](#wbadmin-delete-system-backups) +- [WMI Permanent Event Subscription](#wmi-permanent-event-subscription) +- [WMI Permanent Event Subscription - Sysmon](#wmi-permanent-event-subscription---sysmon) +- [WMI Temporary Event Subscription](#wmi-temporary-event-subscription) +- [Windows AdFind Exe](#windows-adfind-exe) +- [Windows Event Log Cleared](#windows-event-log-cleared) +- [Windows Security Account Manager Stopped](#windows-security-account-manager-stopped)
## Network
View -- [Protocols passing authentication in cleartext](#protocols-passing-authentication-in-cleartext) -- [Large Volume of DNS ANY Queries](#large-volume-of-dns-any-queries) -- [Hosts receiving high volume of network traffic from email server](#hosts-receiving-high-volume-of-network-traffic-from-email-server) -- [Detect ARP Poisoning](#detect-arp-poisoning) -- [Prohibited Network Traffic Allowed](#prohibited-network-traffic-allowed) -- [DNS record changed](#dns-record-changed) -- [Protocol or Port Mismatch](#protocol-or-port-mismatch) -- [Excessive DNS Failures](#excessive-dns-failures) -- [Detect Zerologon via Zeek](#detect-zerologon-via-zeek) - [DNS Query Length Outliers - MLTK](#dns-query-length-outliers---mltk) -- [TOR Traffic](#tor-traffic) -- [Detect Large Outbound ICMP Packets](#detect-large-outbound-icmp-packets) -- [Detect SNICat SNI Exfiltration](#detect-snicat-sni-exfiltration) -- [SMB Traffic Spike](#smb-traffic-spike) -- [Detect Port Security Violation](#detect-port-security-violation) -- [SMB Traffic Spike - MLTK](#smb-traffic-spike---mltk) -- [Remote Desktop Network Traffic](#remote-desktop-network-traffic) - [DNS Query Length With High Standard Deviation](#dns-query-length-with-high-standard-deviation) -- [Detect Unauthorized Assets by MAC address](#detect-unauthorized-assets-by-mac-address) -- [Detect Outbound SMB Traffic](#detect-outbound-smb-traffic) -- [Detect Windows DNS SIGRed via Zeek](#detect-windows-dns-sigred-via-zeek) -- [Detect hosts connecting to dynamic domain providers](#detect-hosts-connecting-to-dynamic-domain-providers) -- [Detect Traffic Mirroring](#detect-traffic-mirroring) -- [Unusually Long Content-Type Length](#unusually-long-content-type-length) -- [Detect Rogue DHCP Server](#detect-rogue-dhcp-server) +- [DNS record changed](#dns-record-changed) +- [Detect ARP Poisoning](#detect-arp-poisoning) - [Detect IPv6 Network Infrastructure Threats](#detect-ipv6-network-infrastructure-threats) -- [Detect Windows DNS SIGRed via Splunk Stream](#detect-windows-dns-sigred-via-splunk-stream) -- [Remote Desktop Network Bruteforce](#remote-desktop-network-bruteforce) +- [Detect Large Outbound ICMP Packets](#detect-large-outbound-icmp-packets) +- [Detect Outbound SMB Traffic](#detect-outbound-smb-traffic) +- [Detect Port Security Violation](#detect-port-security-violation) +- [Detect Rogue DHCP Server](#detect-rogue-dhcp-server) +- [Detect SNICat SNI Exfiltration](#detect-snicat-sni-exfiltration) - [Detect Software Download To Network Device](#detect-software-download-to-network-device) +- [Detect Traffic Mirroring](#detect-traffic-mirroring) +- [Detect Unauthorized Assets by MAC address](#detect-unauthorized-assets-by-mac-address) +- [Detect Windows DNS SIGRed via Splunk Stream](#detect-windows-dns-sigred-via-splunk-stream) +- [Detect Windows DNS SIGRed via Zeek](#detect-windows-dns-sigred-via-zeek) +- [Detect Zerologon via Zeek](#detect-zerologon-via-zeek) +- [Detect hosts connecting to dynamic domain providers](#detect-hosts-connecting-to-dynamic-domain-providers) +- [Excessive DNS Failures](#excessive-dns-failures) +- [Hosts receiving high volume of network traffic from email server](#hosts-receiving-high-volume-of-network-traffic-from-email-server) +- [Large Volume of DNS ANY Queries](#large-volume-of-dns-any-queries) +- [Prohibited Network Traffic Allowed](#prohibited-network-traffic-allowed) +- [Protocol or Port Mismatch](#protocol-or-port-mismatch) +- [Protocols passing authentication in cleartext](#protocols-passing-authentication-in-cleartext) +- [Remote Desktop Network Bruteforce](#remote-desktop-network-bruteforce) +- [Remote Desktop Network Traffic](#remote-desktop-network-traffic) +- [SMB Traffic Spike](#smb-traffic-spike) +- [SMB Traffic Spike - MLTK](#smb-traffic-spike---mltk) +- [TOR Traffic](#tor-traffic) +- [Unusually Long Content-Type Length](#unusually-long-content-type-length)
## Application
View -- [Email files written outside of the Outlook directory](#email-files-written-outside-of-the-outlook-directory) -- [Web Servers Executing Suspicious Processes](#web-servers-executing-suspicious-processes) -- [Multiple Okta Users With Invalid Credentials From The Same IP](#multiple-okta-users-with-invalid-credentials-from-the-same-ip) -- [Okta Failed SSO Attempts](#okta-failed-sso-attempts) -- [Okta Account Lockout Events](#okta-account-lockout-events) -- [Okta User Logins From Multiple Cities](#okta-user-logins-from-multiple-cities) - [Detect New Login Attempts to Routers](#detect-new-login-attempts-to-routers) - [Email Attachments With Lots Of Spaces](#email-attachments-with-lots-of-spaces) -- [Phishing Email Detection by Machine Learning Method - SSA](#phishing-email-detection-by-machine-learning-method---ssa) -- [Suspicious Email - UBA Anomaly](#suspicious-email---uba-anomaly) -- [No Windows Updates in a time frame](#no-windows-updates-in-a-time-frame) -- [Suspicious Email Attachment Extensions](#suspicious-email-attachment-extensions) -- [Monitor Email For Brand Abuse](#monitor-email-for-brand-abuse) +- [Email files written outside of the Outlook directory](#email-files-written-outside-of-the-outlook-directory) - [Email servers sending high volume traffic to hosts](#email-servers-sending-high-volume-traffic-to-hosts) -- [Suspicious Java Classes](#suspicious-java-classes) +- [Monitor Email For Brand Abuse](#monitor-email-for-brand-abuse) +- [Multiple Okta Users With Invalid Credentials From The Same IP](#multiple-okta-users-with-invalid-credentials-from-the-same-ip) +- [No Windows Updates in a time frame](#no-windows-updates-in-a-time-frame) +- [Okta Account Lockout Events](#okta-account-lockout-events) +- [Okta Failed SSO Attempts](#okta-failed-sso-attempts) +- [Okta User Logins From Multiple Cities](#okta-user-logins-from-multiple-cities) +- [Phishing Email Detection by Machine Learning Method - SSA](#phishing-email-detection-by-machine-learning-method---ssa) - [Spectre and Meltdown Vulnerable Systems](#spectre-and-meltdown-vulnerable-systems) +- [Suspicious Email - UBA Anomaly](#suspicious-email---uba-anomaly) +- [Suspicious Email Attachment Extensions](#suspicious-email-attachment-extensions) +- [Suspicious Java Classes](#suspicious-java-classes) +- [Web Servers Executing Suspicious Processes](#web-servers-executing-suspicious-processes)
## Web @@ -335,88 +335,79 @@ All the detections shipped to different Splunk products. Below is a breakdown by View - [Detect F5 TMUI RCE CVE-2020-5902](#detect-f5-tmui-rce-cve-2020-5902) -- [Detect malicious requests to exploit JBoss servers](#detect-malicious-requests-to-exploit-jboss-servers) -- [Supernova Webshell](#supernova-webshell) - [Detect attackers scanning for vulnerable JBoss servers](#detect-attackers-scanning-for-vulnerable-jboss-servers) +- [Detect malicious requests to exploit JBoss servers](#detect-malicious-requests-to-exploit-jboss-servers) - [Monitor Web Traffic For Brand Abuse](#monitor-web-traffic-for-brand-abuse) - [SQL Injection with Long URLs](#sql-injection-with-long-urls) -- [Web Fraud - Anomalous User Clickspeed](#web-fraud---anomalous-user-clickspeed) +- [Supernova Webshell](#supernova-webshell) - [Web Fraud - Account Harvesting](#web-fraud---account-harvesting) +- [Web Fraud - Anomalous User Clickspeed](#web-fraud---anomalous-user-clickspeed) - [Web Fraud - Password Sharing Across Accounts](#web-fraud---password-sharing-across-accounts) -### Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules -This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary. +### AWS Cloud Provisioning From Previously Unseen City +This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. -- **Product**: UEBA for Security Cloud +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1543, T1055, T1574 -- **Last Updated**: 2020-11-05 +- **ATT&CK**: T1535 +- **Last Updated**: 2018-03-16
View #### Search ``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::detours/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_city_filter` ``` #### Associated Analytic Story #### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. #### Required fields -* _time -* process -* dest_device_id -* dest_user_id #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1543 | x | x | -| T1055 | x | x | -| T1574 | x | x | +| T1535 | x | x | #### Kill Chain Phases -* Actions on Objectives #### Known False Positives -None identified. +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References -* https://github.com/gentilkiwi/mimikatz -* https://en.wikipedia.org/wiki/Microsoft_Detours #### Test Dataset _version_: 1
---- -### Schtasks used for forcing a reboot -This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. + +===== +### AWS Cloud Provisioning From Previously Unseen Country +This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1053.005 -- **Last Updated**: 2020-12-07 +- **ATT&CK**: T1535 +- **Last Updated**: 2018-03-16
View #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_used_for_forcing_a_reboot_filter` +`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_country_filter` ``` #### Associated Analytic Story #### How To Implement -To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. #### Required fields @@ -424,129 +415,41 @@ To successfully implement this search you need to be ingesting logs with both th | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1053.005 | x | x | +| T1535 | x | x | #### Kill Chain Phases -* Actions on Objectives #### Known False Positives -Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References #### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_shutdown/windows-sysmon.log - -_version_: 4 -
---- -### Single Letter Process On Endpoint -This search looks for process names that consist only of a single letter. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1204.002 -- **Last Updated**: 2020-12-08 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name | `single_letter_process_on_endpoint_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1204.002 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/single_letter_exe/windows-sysmon.log - -_version_: 3 -
---- -### Suspicious Rundll32 no CommandLine Arguments -The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 -- **Last Updated**: 2021-02-09 - -
- View - -#### Search -``` -`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) | regex CommandLine="(rundll32\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_no_commandline_arguments_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. - -#### References -* https://attack.mitre.org/techniques/T1218/011/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md -* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 -* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log _version_: 1
---- -### Detect Rare Executables -This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. + +===== +### AWS Cloud Provisioning From Previously Unseen IP Address +This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: - **ATT&CK**: -- **Last Updated**: 2020-03-16 +- **Last Updated**: 2018-03-16
View #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | rex field=user "(?.*)\\\\(?.*)" | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_allow_list`| table process ] | `detect_rare_executables_filter` +`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_ip_address_filter` ``` #### Associated Analytic Story #### How To Implement -To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_allow_list` searches two lookup files for allowed processes. These consist of `rare_process_allow_list_default.csv` and `rare_process_allow_list_local.csv`. To add your own processes to the allow list, add them to `rare_process_allow_list_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the allow_list value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment. +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. #### Required fields @@ -556,279 +459,80 @@ To successfully implement this search, you must be ingesting data that records p | ----------- | ----------- |:-------------:| #### Kill Chain Phases -* Installation -* Command and Control -* Actions on Objectives #### Known False Positives -Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_allow_list_local.csv` to filter them out of your search results. +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References #### Test Dataset -_version_: 5 -
---- -### Dump LSASS via comsvcs DLL -Detect the usage of comsvcs.dll for dumping the lsass process. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.001 -- **Last Updated**: 2020-02-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_comsvcs_dll_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/ -* https://twitter.com/SBousseaden/status/1167417096374050817 - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Malicious PowerShell Process - Connect To Internet With Hidden Window -This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001 -- **Last Updated**: 2020-11-20 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.001 | x | x | - -#### Kill Chain Phases -* Command and Control -* Actions on Objectives - -#### Known False Positives -Legitimate process can have this combination of command-line options, but it's not common. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/hidden_powershell/windows-sysmon.log - -_version_: 5 -
---- -### Suspicious Rundll32 dllregisterserver -The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 -- **Last Updated**: 2021-02-09 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_dllregisterserver_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names. - -#### References -* https://attack.mitre.org/techniques/T1218/011/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md -* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 -* https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/seedworm-apt-iran-middle-east -* https://github.com/pan-unit42/tweets/blob/master/2020-12-10-IOCs-from-Ursnif-infection-with-Delf-variant.txt -* https://www.crowdstrike.com/blog/duck-hunting-with-falcon-complete-qakbot-zip-based-campaign/ -* https://msdn.microsoft.com/en-us/library/windows/desktop/ms682162(v=vs.85).aspx - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Illegal Access To User Content via PowerSploit modules -This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1021, T1113, T1123, T1563 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-HttpStatus/)=true OR match_regex(cmd_line, /(?i)Get-Keystrokes/)=true OR match_regex(cmd_line, /(?i)Get-MicrophoneAudio/)=true OR match_regex(cmd_line, /(?i)Get-NetRDPSession/)=true OR match_regex(cmd_line, /(?i)Get-TimedScreenshot/)=true OR match_regex(cmd_line, /(?i)Get-WebConfig/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021 | x | x | -| T1113 | x | x | -| T1123 | x | x | -| T1563 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - _version_: 1
---- -### Reconnaissance of Access and Persistence Opportunities via PowerSploit modules -This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised. -- **Product**: UEBA for Security Cloud +===== +### AWS Cloud Provisioning From Previously Unseen Region +This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1053, T1068, T1078, T1543, T1547, T1574 -- **Last Updated**: 2020-11-05 +- **ATT&CK**: T1535 +- **Last Updated**: 2018-03-16
View #### Search ``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-LocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainAcl/)=true OR match_regex(cmd_line, /(?i)Invoke-ACLScanner/)=true OR match_regex(cmd_line, /(?i)Find-PathDLLHijack/)=true OR match_regex(cmd_line, /(?i)Find-ProcessDLLHijack/)=true OR match_regex(cmd_line, /(?i)Get-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-DomainPolicy/)=true OR match_regex(cmd_line, /(?i)Get-ModifiablePath/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableRegistryAutoRun/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableScheduledTaskFile/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableService/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableServiceFile/)=true OR match_regex(cmd_line, /(?i)Get-PathAcl/)=true OR match_regex(cmd_line, /(?i)Get-UnattendedInstallFile/)=true OR match_regex(cmd_line, /(?i)Get-UnquotedService/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_region_filter` ``` #### Associated Analytic Story #### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. #### Required fields -* _time -* process -* dest_device_id -* dest_user_id #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1053 | x | x | -| T1068 | x | x | -| T1078 | x | x | -| T1543 | x | x | -| T1547 | x | x | -| T1574 | x | x | +| T1535 | x | x | #### Kill Chain Phases -* Actions on Objectives #### Known False Positives -None identified. +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References -* https://github.com/PowerShellMafia/PowerSploit #### Test Dataset _version_: 1
---- -### Shim Database File Creation -This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +===== +### AWS Cross Account Activity From Previously Unseen Account +This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. This search is deprecated and have been translated to use the latest Authentication Datamodel. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1546.011 -- **Last Updated**: 2020-12-08 +- **ATT&CK**: +- **Last Updated**: 2020-05-28
View #### Search ``` -| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter` +| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime | eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") | where status = "New Cross Account Activity" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_cross_account_activity_from_previously_unseen_account_filter` ``` #### Associated Analytic Story #### How To Implement -You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen AWS Cross Account Activity - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen AWS Cross Account Activity - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `aws_cross_account_activity_from_previously_unseen_account_filter` macro. #### Required fields @@ -836,134 +540,41 @@ You must be ingesting data that records the filesystem activity from your hosts | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1546.011 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. +Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. #### References #### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log - -_version_: 3 -
---- -### Processes launching netsh -This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.004 -- **Last Updated**: 2020-07-10 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`processes_launching_netsh_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/atomic_red_team/windows-sysmon.log - -_version_: 3 -
---- -### Credential Extraction indicative of use of DSInternals credential conversion modules -Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003 -- **Last Updated**: 2020-10-21 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)ConvertFrom-ADManagedPasswordBlob/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-UnicodePassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-KerberosKey/)=true OR match_regex(cmd_line, /(?i)ConvertTo-LMHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-NTHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-OrgIdHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-UnicodePassword/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* process_name -* parent_process_name -* _time -* process_path -* dest_user_id -* process - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/MichaelGrafnetter/DSInternals - -#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json _version_: 1
---- -### Schtasks scheduling job on remote system -This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. + +===== +### AWS Detect Users creating keys with encrypt policy without MFA +This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1053.005 -- **Last Updated**: 2020-07-21 +- **ATT&CK**: T1486 +- **Last Updated**: 2021-01-11
View #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter` +`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy | spath input=requestParameters.policy output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | spath input=key_policy_statements output=key_policy_action_2 path=Action{} | eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) | spath input=key_policy_statements output=key_policy_principal path=Principal.AWS | search key_policy_action="kms:Encrypt" AND key_policy_principal="*" | stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` ``` #### Associated Analytic Story #### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs #### Required fields @@ -971,41 +582,89 @@ You must be ingesting data that records process activity from your hosts to popu | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1053.005 | x | x | +| T1486 | x | x | #### Kill Chain Phases -* Actions on Objectives #### Known False Positives -Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. +unknown #### References +* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ +* https://github.com/d1vious/git-wild-hunt +* https://www.youtube.com/watch?v=PgzNib37g0M #### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json -_version_: 4 +_version_: 1
---- -### Certutil exe certificate extraction -This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS. + +===== +### AWS Detect Users with KMS keys performing encryption S3 +This search provides detection of users with KMS keys performing encryption specifically against S3 buckets. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1486 +- **Last Updated**: 2021-01-11 + +
+ View + +#### Search +``` +`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" | rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1486 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +bucket with S3 encryption + +#### References +* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ +* https://github.com/d1vious/git-wild-hunt +* https://www.youtube.com/watch?v=PgzNib37g0M + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json + +_version_: 1 +
+ +===== +### AWS EKS Kubernetes cluster sensitive object access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: - **ATT&CK**: -- **Last Updated**: 2021-01-26 +- **Last Updated**: 2020-06-23
View #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_filter` +`aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 |table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`aws_eks_kubernetes_cluster_sensitive_object_access_filter` ``` #### Associated Analytic Story #### How To Implement - +You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. This search works with cloudwatch logs. #### Required fields @@ -1015,56 +674,10 @@ This search looks for arguments to certutil.exe indicating the manipulation or e | ----------- | ----------- |:-------------:| #### Kill Chain Phases -* Installation +* Lateral Movement #### Known False Positives -Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log - -_version_: 1 -
---- -### Unusually Long Command Line -Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-10-06 - -
- View - -#### Search -``` - | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) | where cmd_line!=null and dest_user_id!=null | eval cmd_line_norm=replace(cast(cmd_line, "string"), /\s(--?\w+)|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) | select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input | adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 | where label AND quantile>0.99 | first_time_event input_columns=["dest_device_id", "cmd_line"] | where first_time_dest_device_id_cmd_line | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting sysmon endpoint data that monitors command lines. - -#### Required fields -* process_name -* _time -* dest_device_id -* dest_user_id -* process - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -This detection may flag suspiciously long command lines when there is not sufficient evidence (samples) for a given process that this detection is tracking; or when there is high variability in the length of the command line for the tracked process. Also, some legitimate applications may use long command lines. Such is the case of Ansible, that encodes Powershell scripts using long base64. Attackers may use this technique to obfuscate their payloads. +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. #### References @@ -1072,76 +685,27 @@ This detection may flag suspiciously long command lines when there is not suffic _version_: 1
---- -### Reconnaissance of Connectivity via PowerSploit modules -This detection identifies access to PowerSploit modules for reconnaissance of connectivity. -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1021.002, T1135, T1039 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainDNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DomainDNSZone/)=true OR match_regex(cmd_line, /(?i)Get-DNSZone/)=true OR match_regex(cmd_line, /(?i)Invoke-ReverseDnsLookup/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegCachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-CachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegProxy/)=true OR match_regex(cmd_line, /(?i)Get-Proxy/)=true OR match_regex(cmd_line, /(?i)Invoke-Portscan/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.002 | x | x | -| T1135 | x | x | -| T1039 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### Create Remote Thread into LSASS -Detect remote thread creation into LSASS consistent with credential dumping. +===== +### AWS Network Access Control List Created with All Open Ports +The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1003.001 -- **Last Updated**: 2019-12-06 +- **ATT&CK**: T1562.007 +- **Last Updated**: 2021-01-11
View #### Search ``` -`sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `create_remote_thread_into_lsass_filter` +`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 | eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' | where port_range>1024] | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter` ``` #### Associated Analytic Story #### How To Implement -This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs. #### Required fields @@ -1149,364 +713,29 @@ This search needs Sysmon Logs with a Sysmon configuration, which includes EventC | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1003.001 | x | x | +| T1562.007 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. - -#### References -* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Reconnaissance and Access to Shared Resources via PowerSploit modules -This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1021.002, T1135, T1039 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainShare/)=true OR match_regex(cmd_line, /(?i)Invoke-ShareFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainShareFile/)=true OR match_regex(cmd_line, /(?i)Invoke-FileFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingFile/)=true OR match_regex(cmd_line, /(?i)Get-DomainDFSShare/)=true OR match_regex(cmd_line, /(?i)Get-DFSshare/)=true OR match_regex(cmd_line, /(?i)Get-NetShare/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.002 | x | x | -| T1135 | x | x | -| T1039 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### Attempted Credential Dump From Registry via Reg exe -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.002 -- **Last Updated**: 2019-12-02 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_exe_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.002 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. +It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. #### References #### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### Detect processes used for System Network Configuration Discovery -This search looks for fast execution of processes used for system network configuration discovery on the endpoint. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1016 -- **Last Updated**: 2020-11-10 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime lastTime dest user process_name process parent_process eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1016 | x | x | - -#### Kill Chain Phases -* Installation -* Command and Control -* Actions on Objectives - -#### Known False Positives -It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/discovery_commands/windows-sysmon.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/aws_cloudtrail_events.json _version_: 2
---- -### Setting Credentials via DSInternals modules -This detection identifies illegal setting of credentials via DSInternals modules. -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1068, T1078, T1098 -- **Last Updated**: 2020-11-03 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-ADDBSidHistory/)=true OR match_regex(cmd_line, /(?i)Add-ADReplNgcKey/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPassword/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-ADDBBootKey/)=true OR match_regex(cmd_line, /(?i)Set-SamAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-AzureADUserEx/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* process_name -* parent_process_name -* _time -* process_path -* dest_user_id -* process - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | -| T1078 | x | x | -| T1098 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/MichaelGrafnetter/DSInternals - -#### Test Dataset - -_version_: 1 -
---- -### Common Ransomware Extensions -The search looks for file modifications with extensions commonly used by Ransomware +===== +### AWS Network Access Control List Deleted +Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1485 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` | `common_ransomware_extensions_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\ -This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ -1. \ -1. **Label:** File Extension, **Field:** file_extension\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1485 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_extensions/windows-sysmon.log - -_version_: 4 -
---- -### Assessment of Credential Strength via DSInternals modules -This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1078, T1098, T1087, T1201, T1552, T1555 -- **Last Updated**: 2020-11-03 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-PasswordQuality/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | -| T1098 | x | x | -| T1087 | x | x | -| T1201 | x | x | -| T1552 | x | x | -| T1555 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/MichaelGrafnetter/DSInternals - -#### Test Dataset - -_version_: 1 -
---- -### Detect Rundll32 Application Control Bypass - setupapi -The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 -- **Last Updated**: 2021-02-04 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___setupapi_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, some legitimate applications may use setupapi triggering a false positive. - -#### References -* https://attack.mitre.org/techniques/T1218/011/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md -* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 -* https://lolbas-project.github.io/lolbas/Libraries/Setupapi/ -* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Suspicious microsoft workflow compiler rename -The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1127, T1036.003 +- **ATT&CK**: T1562.007 - **Last Updated**: 2021-01-12
@@ -1514,12 +743,12 @@ The following analytic identifies a renamed instance of microsoft.workflow.compi #### Search ``` -`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_microsoft_workflow_compiler_rename_filter` +`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_deleted_filter` ``` #### Associated Analytic Story #### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. #### Required fields @@ -1527,135 +756,176 @@ To successfully implement this search, you need to be ingesting logs with the pr | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1127, T1036.003 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive. - -#### References -* https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log - -_version_: 1 -
---- -### Execution of File with Multiple Extensions -This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1036.003 -- **Last Updated**: 2020-11-18 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_multiple_extensions_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1036.003 | x | x | +| T1562.007 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -None identified. +It's possible that a user has legitimately deleted a network ACL. #### References #### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/aws_cloudtrail_events.json -_version_: 3 +_version_: 2
---- -### Illegal Management of Active Directory Elements and Policies via DSInternals modules -This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies. -- **Product**: UEBA for Security Cloud +===== +### AWS SAML Access by Provider User and Principal +This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1098, T1207, T1484 -- **Last Updated**: 2020-11-09 +- **ATT&CK**: T1078 +- **Last Updated**: 2021-01-26
View #### Search ``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Remove-ADDBObject/)=true OR match_regex(cmd_line, /(?i)Set-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Set-ADDBPrimaryGroup/)=true OR match_regex(cmd_line, /(?i)Set-LsaPolicyInformation/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +`cloudtrail` eventName=Assumerolewithsaml | stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_access_by_provider_user_and_principal_filter` ``` #### Associated Analytic Story #### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs #### Required fields -* dest_device_id -* dest_user_id -* process -* _time #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1098 | x | x | -| T1207 | x | x | -| T1484 | x | x | +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks. + +#### References +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf +* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/assume_role_with_saml/assume_role_with_saml.json + +_version_: 1 +
+ +===== +### AWS SAML Update identity provider +This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`cloudtrail` eventName=UpdateSAMLProvider | stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_update_identity_provider_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored. + +#### References +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf +* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json + +_version_: 1 +
+ +===== +### Abnormally High AWS Instances Launched by User +This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev | `abnormally_high_aws_instances_launched_by_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -None identified. +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. #### References -* https://github.com/MichaelGrafnetter/DSInternals #### Test Dataset -_version_: 1 +_version_: 2
---- -### Detect Prohibited Applications Spawning cmd exe -This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. + +===== +### Abnormally High AWS Instances Launched by User - MLTK +This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1059.003 -- **Last Updated**: 2020-11-10 +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21
View #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] | `detect_prohibited_applications_spawning_cmd_exe_filter` +`cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | apply ec2_excessive_runinstances_v1 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 ``` #### Associated Analytic Story #### How To Implement -You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. #### Required fields @@ -1663,92 +933,41 @@ You must be ingesting data that records process activity from your hosts and pop | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1059.003 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/powershell_spawn_cmd/windows-sysmon.log - -_version_: 5 -
---- -### Illegal Service and Process Control via PowerSploit modules -This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1055, T1106, T1569 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Install-SSP/)=true OR match_regex(cmd_line, /(?i)Set-CriticalProcess/)=true OR match_regex(cmd_line, /(?i)Install-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Restore-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Write-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Set-ServiceBinaryPath/)=true OR match_regex(cmd_line, /(?i)Invoke-ReflectivePEInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-DllInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-ServiceAbuse/)=true OR match_regex(cmd_line, /(?i)Invoke-Shellcode/)=true OR match_regex(cmd_line, /(?i)Invoke-WScriptUACBypass/)=true OR match_regex(cmd_line, /(?i)Invoke-WmiCommand/)=true OR match_regex(cmd_line, /(?i)Write-HijackDll/)=true OR match_regex(cmd_line, /(?i)Add-ServiceDacl/)=true ) - -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1055 | x | x | -| T1106 | x | x | -| T1569 | x | x | +| T1078.004 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -None identified. +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. #### References -* https://github.com/PowerShellMafia/PowerSploit #### Test Dataset -_version_: 1 +_version_: 2
---- -### Detect Computer Changed with Anonymous Account -This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. + +===== +### Abnormally High AWS Instances Terminated by User +This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1210 -- **Last Updated**: 2020-09-18 +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21
View #### Search ``` -`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 | stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter` +`cloudtrail` eventName=TerminateInstances errorCode=success | bucket span=10m _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev | `abnormally_high_aws_instances_terminated_by_user_filter` ``` #### Associated Analytic Story #### How To Implement -This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event Logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. #### Required fields @@ -1756,41 +975,41 @@ This search requires audit computer account management to be enabled on the syst | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1210 | x | x | +| T1078.004 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -None thus far found +Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. #### References -* https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/ #### Test Dataset -_version_: 1 +_version_: 2
---- -### Suspicious writes to windows Recycle Bin -This search detects writes to the recycle bin by a process other than explorer.exe. + +===== +### Abnormally High AWS Instances Terminated by User - MLTK +This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1036 -- **Last Updated**: 2020-07-22 +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21
View #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`| search [| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")` | table process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter` +`cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | apply ec2_excessive_terminateinstances_v1 | rename "IsOutlier(instances_terminated)" as isOutlier | where isOutlier=1 ``` #### Associated Analytic Story #### How To Implement -To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. #### Required fields @@ -1798,64 +1017,192 @@ To successfully implement this search you need to be ingesting information on fi | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1036 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/write_to_recycle_bin/windows-sysmon.log - -_version_: 4 -
---- -### BCDEdit Failure Recovery Modification -This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1490 -- **Last Updated**: 2020-12-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bcdedit_failure_recovery_modification_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1490 | x | x | +| T1078.004 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -Administrators may modify the boot configuration. +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. #### References -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair #### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + +_version_: 2 +
+ +===== +### Abnormally High Number Of Cloud Infrastructure API Calls +This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-09-07 + +
+ View + +#### Search +``` +| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_api_calls_v1 threshold=0.005 | rename "IsOutlier(api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold = api_calls - expected_upper_threshold | table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to create the probability density function. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives + + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json _version_: 1
---- + +===== +### Abnormally High Number Of Cloud Instances Destroyed +This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-08-21 + +
+ View + +#### Search +``` +| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] | where cardinality >=16 | apply cloud_excessive_instances_destroyed_v1 threshold=0.005 | rename "IsOutlier(instances_destroyed)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_destroyed - expected_upper_threshold | table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability density function. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Many service accounts configured within a cloud infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Abnormally High Number Of Cloud Instances Launched +This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-08-21 + +
+ View + +#### Search +``` +| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] | where cardinality >=16 | apply cloud_excessive_instances_created_v1 threshold=0.005 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_launched - expected_upper_threshold | table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_launched_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Launched` to create the probability density function. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Abnormally High Number Of Cloud Security Group API Calls +This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-09-07 + +
+ View + +#### Search +``` +| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 | rename "IsOutlier(security_group_api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where security_group_api_calls > expected_upper_threshold | eval distance_from_threshold = security_group_api_calls - expected_upper_threshold | table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to create the probability density function model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives + + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== ### Access LSASS Memory for Dump Creation Detect memory dumping of the LSASS process. @@ -1898,28 +1245,27 @@ Administrators can create memory dumps for debugging purposes, but memory dumps _version_: 2 ---- -### Ntdsutil export ntds -Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \ -ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ -This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. + +===== +### Amazon EKS Kubernetes Pod scan detection +This search provides detection information on unauthenticated requests against Kubernetes' Pods API - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1003.003 -- **Last Updated**: 2021-01-28 +- **ATT&CK**: T1526 +- **Last Updated**: 2020-04-15
View #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `ntdsutil_export_ntds_filter` +`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter` ``` #### Associated Analytic Story #### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. #### Required fields @@ -1927,653 +1273,13 @@ You must be ingesting endpoint data that tracks process activity, including pare | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1003.003 | x | x | +| T1526 | x | x | #### Kill Chain Phases -* Actions on Objectives +* Reconnaissance #### Known False Positives -Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives. - -#### References -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil -* https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11) -* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf -* https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Detect Regsvcs with No Command Line Arguments -The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.009 -- **Last Updated**: 2021-02-12 - -
- View - -#### Search -``` -`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) | regex CommandLine="(regsvcs\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_no_command_line_arguments_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. - -#### References -* https://attack.mitre.org/techniques/T1218/009/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Credential Extraction indicative of use of DSInternals modules -Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003 -- **Last Updated**: 2020-10-21 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ADDBBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Get-ADDBKdsRootKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBSchemaAttribute/)=true OR match_regex(cmd_line, /(?i)Get-ADKeyCredential/)=true OR match_regex(cmd_line, /(?i)Get-ADReplAccount/)=true OR match_regex(cmd_line, /(?i)Get-ADReplBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADSIAccount/)=true OR match_regex(cmd_line, /(?i)Get-AzureADUserEx/)=true OR match_regex(cmd_line, /(?i)Get-BootKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaPolicyInformation/)=true OR match_regex(cmd_line, /(?i)Get-SamPasswordPolicy/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* process_name -* parent_process_name -* _time -* process_path -* dest_user_id -* process - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/MichaelGrafnetter/DSInternals - -#### Test Dataset - -_version_: 1 -
---- -### Attempted Credential Dump From Registry via Reg exe -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003 -- **Last Updated**: 2020-6-04 - -
- View - -#### Search -``` - | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) | where process_name="cmd.exe" OR process_name="reg.exe" | where cmd_line != null AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting windows endpoint data that tracks process activity, including parent-child relationships from your endpoints. - -#### Required fields -* process_name -* _time -* dest_device_id -* dest_user_id -* process - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/splunk/security_content/blob/55a17c65f9f56c2220000b62701765422b46125d/detections/attempted_credential_dump_from_registry_via_reg_exe.yml - -#### Test Dataset - -_version_: 1 -
---- -### Reg exe Manipulating Windows Services Registry Keys -The search looks for reg.exe modifying registry keys that define Windows services and their configurations. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1574.011 -- **Last Updated**: 2020-11-26 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1574.011 | x | x | - -#### Kill Chain Phases -* Installation - -#### Known False Positives -It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log - -_version_: 5 -
---- -### Suspicious Rundll32 Rename -The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011, T1036.003 -- **Last Updated**: 2021-02-04 - -
- View - -#### Search -``` -`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_rundll32_rename_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | -| T1036.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. - -#### References -* https://attack.mitre.org/techniques/T1218/011/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md -* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Credential Dumping via Symlink to Shadow Copy -This search detects the creation of a symlink to a shadow copy. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.003 -- **Last Updated**: 2019-12-10 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_symlink_to_shadow_copy_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -unknown - -#### References -* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Malicious PowerShell Process - Encoded Command -This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1027 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `malicious_powershell_process___encoded_command_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1027 | x | x | - -#### Kill Chain Phases -* Command and Control -* Actions on Objectives - -#### Known False Positives -System administrators may use this option, but it's not common. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### Reconnaissance of Defensive Tools via PowerSploit modules -This detection identifies use of PowerSploit modules for assessment of presence of defensive tools. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1595.002, T1592.002 -- **Last Updated**: 2020-11-05 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-AVSignature/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1595.002 | x | x | -| T1592.002 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### File with Samsam Extension -The search looks for file writes with extensions consistent with a SamSam ransomware attack. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-12-14 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml | `file_with_samsam_extension_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Installation - -#### Known False Positives -Because these extensions are not typically used in normal operations, you should investigate all results. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/samsam_extension/windows-sysmon.log - -_version_: 1 -
---- -### Script Execution via WMI -This search looks for scripts launched via WMI. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1047 -- **Last Updated**: 2020-03-16 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `script_execution_via_wmi_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/execution_scrcons/windows-sysmon.log - -_version_: 3 -
---- -### Process Execution via WMI -This search looks for processes launched via WMI. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1047 -- **Last Updated**: 2020-03-16 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `process_execution_via_wmi_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, administrators may use wmi to execute commands for legitimate purposes. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log - -_version_: 3 -
---- -### Illegal Privilege Elevation via Mimikatz modules -This detection identifies use of Mimikatz modules for illegal privilege elevation. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1134, T1548 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)privilege::debug/)=true OR match_regex(cmd_line, /(?i)token::elevate/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1134 | x | x | -| T1548 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/gentilkiwi/mimikatz - -#### Test Dataset - -_version_: 1 -
---- -### Detect Regsvcs with Network Connection -The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.009 -- **Last Updated**: 2021-02-16 - -
- View - -#### Search -``` -`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_network_connection_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. - -#### References -* https://attack.mitre.org/techniques/T1218/009/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Credential Extraction indicative of FGDump and CacheDump with s option -Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003 -- **Last Updated**: 2020-10-18 - -
- View - -#### Search -``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND process_name != null AND parent_process_name != null AND match_regex(parent_process_name, /(?i)System32\\services.exe/)=true AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-s/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* process_name -* parent_process_name -* _time -* process_path -* dest_user_id -* process - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. +Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. #### References @@ -2581,26 +1287,27 @@ None identified. _version_: 1
---- -### Monitor Registry Keys for Print Monitors -This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. + +===== +### Amazon EKS Kubernetes cluster scan detection +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1547.010 -- **Last Updated**: 2020-11-23 +- **ATT&CK**: T1526 +- **Last Updated**: 2020-04-15
View #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `monitor_registry_keys_for_print_monitors_filter` +`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`amazon_eks_kubernetes_cluster_scan_detection_filter` ``` #### Associated Analytic Story #### How To Implement -To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. #### Required fields @@ -2608,291 +1315,13 @@ To successfully implement this search, you must be ingesting data that records r | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1547.010 | x | x | +| T1526 | x | x | #### Kill Chain Phases -* Actions on Objectives +* Reconnaissance #### Known False Positives -You will encounter noise from legitimate print-monitor registry entries. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.010/atomic_red_team/windows-sysmon.log - -_version_: 2 -
---- -### Shim Database Installation With Suspicious Parameters -This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1546.011 -- **Last Updated**: 2020-11-23 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.011 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### Suspicious Regsvr32 Register Suspicious Path -Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.010 -- **Last Updated**: 2021-01-28 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_regsvr32_register_suspicious_path_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.010 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues. - -#### References -* https://attack.mitre.org/techniques/T1218/010/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md -* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ -* https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 -* https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718 - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Detect Regsvr32 Application Control Bypass -Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ -Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.010 -- **Last Updated**: 2021-01-28 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_regsvr32_application_control_bypass_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.010 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Limited false positives related to third party software registering .DLL's. - -#### References -* https://attack.mitre.org/techniques/T1218/010/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md -* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ -* https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules -This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1199, T1482, T1590, T1591, T1595 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainSID/)=true OR match_regex(cmd_line, /(?i)Get-DomainSite/)=true OR match_regex(cmd_line, /(?i)Get-NetSite/)=true OR match_regex(cmd_line, /(?i)Get-DomainSubnet/)=true OR match_regex(cmd_line, /(?i)Get-NetSubnet/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrustMapping/)=true OR match_regex(cmd_line, /(?i)Invoke-MapDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-Forest/)=true OR match_regex(cmd_line, /(?i)Get-NetForest/)=true OR match_regex(cmd_line, /(?i)Get-ForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-NetForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-ForestGlobalCatalog/)=true OR match_regex(cmd_line, /(?i)Get-NetForestCatalog/)=true OR match_regex(cmd_line, /(?i)Get-ForestTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetForestTrust/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1199 | x | x | -| T1482 | x | x | -| T1590 | x | x | -| T1591 | x | x | -| T1595 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### Probing Access with Stolen Credentials via PowerSploit modules -This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1078, T1098 -- **Last Updated**: 2020-11-04 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-AdminAccess/)=true OR match_regex(cmd_line, /(?i)Invoke-CheckLocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Test-ServiceDaclPermission/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_user_id -* dest_device_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | -| T1098 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### Unusually Long Command Line - MLTK -Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2019-05-08 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime lastTime user dest process_name process processlen count | `unusually_long_command_line___mltk_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. +Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. #### References @@ -2900,3059 +1329,8 @@ Some legitimate applications use long command lines for installs or updates. You _version_: 1
---- -### Detect Credential Dumping through LSASS access -This search looks for reading lsass memory consistent with credential dumping. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.001 -- **Last Updated**: 2019-12-03 - -
- View - -#### Search -``` -`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_credential_dumping_through_lsass_access_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log - -_version_: 3 -
---- -### Detect Rundll32 Inline HTA Execution -The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.005 -- **Last Updated**: 2021-01-20 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_rundll32_inline_hta_execution_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.005 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. - -#### References -* https://github.com/redcanaryco/AtomicTestHarnesses -* https://redcanary.com/blog/introducing-atomictestharnesses/ -* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Illegal Service and Process Control via Mimikatz modules -This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1055, T1106, T1569 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::start/)=true OR match_regex(cmd_line, /(?i)service::\+/)=true OR match_regex(cmd_line, /(?i)service::\-/)=true OR match_regex(cmd_line, /(?i)service::start/)=true OR match_regex(cmd_line, /(?i)service::stop/)=true OR match_regex(cmd_line, /(?i)service::suspend/)=true OR match_regex(cmd_line, /(?i)misc::memssp/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1055 | x | x | -| T1106 | x | x | -| T1569 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/gentilkiwi/mimikatz - -#### Test Dataset - -_version_: 1 -
---- -### Registry Keys for Creating SHIM Databases -This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1546.011 -- **Last Updated**: 2020-11-26 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_for_creating_shim_databases_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response product, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.011 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log - -_version_: 3 -
---- -### Short Lived Windows Accounts -This search detects accounts that were created and deleted in a short time period. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1136.001 -- **Last Updated**: 2020-07-06 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 | transaction user connected=false maxspan=240m | table firstTime lastTime count user dest result_id | `short_lived_windows_accounts_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/ - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.001 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log - -_version_: 2 -
---- -### Credential Extraction indicative of use of PowerSploit modules -Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003 -- **Last Updated**: 2020-10-21 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ApplicationHost/)=true OR match_regex(cmd_line, /(?i)Get-CachedGPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-GPPAutologon/)=true OR match_regex(cmd_line, /(?i)Get-GPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-RegistryAutoLogon/)=true OR match_regex(cmd_line, /(?i)Get-SiteListPassword/)=true OR match_regex(cmd_line, /(?i)Get-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Request-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Get-VaultCredential/)=true OR match_regex(cmd_line, /(?i)Invoke-Kerberoast/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### Windows Event Log Cleared -This search looks for Windows events that indicate one of the Windows event logs has been purged. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1070.001 -- **Last Updated**: 2020-07-06 - -
- View - -#### Search -``` -(`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_log_cleared_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be ingesting Windows event logs from your hosts. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1070.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It is possible that these logs may be legitimately cleared by Administrators. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-security.log -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-system.log - -_version_: 4 -
---- -### Detect Prohibited Applications Spawning cmd exe -This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. This is a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe` by @bpatel. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1059 -- **Last Updated**: 2020-7-13 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) -| where process_name="cmd.exe" | rex field=parent_process "(?[^\\\\]+)$" | where field0="winword.exe" OR field0="excel.exe" OR field0="outlook.exe" OR field0="powerpnt.exe" OR field0="visio.exe" OR field0="mspub.exe" OR field0="acrobat.exe" OR field0="acrord32.exe" OR field0="chrome.exe" OR field0="iexplore.exe" OR field0="opera.exe" OR field0="firefox.exe" OR field0="java.exe" OR field0="powershell.exe" -| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body="TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting sysmon logs. This search has been modified to process raw sysmon data from attack_range's nxlogs on DSP. - -#### Required fields -* process_name -* parent_process_name -* _time -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Unload Sysmon Filter Driver -Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.001 -- **Last Updated**: 2020-07-22 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` |`unload_sysmon_filter_driver_filter`| table firstTime lastTime dest user count process_name process_id parent_process_name process -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives - - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log - -_version_: 3 -
---- -### Unusually Long Command Line -Command lines that are extremely long may be indicative of malicious activity on your hosts. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-12-08 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process | `unusually_long_command_line_filter` |eval threshold = 3 | where maxlen > ((threshold*stdevperhost) + avgperhost) -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the process field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Some legitimate applications start with long command lines. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log - -_version_: 5 -
---- -### Detect Rundll32 Application Control Bypass - advpack -The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 -- **Last Updated**: 2021-02-04 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___advpack_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive. - -#### References -* https://attack.mitre.org/techniques/T1218/011/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md -* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 -* https://lolbas-project.github.io/lolbas/Libraries/Advpack/ -* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules -This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1078, T1087, T1484 -- **Last Updated**: 2020-11-05 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Invoke-EnumerateLocalAdmin/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Invoke-EventHunter/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserLocation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserHunter/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignGroupMember/)=true OR match_regex(cmd_line, /(?i)Find-ForeignGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignUser/)=true OR match_regex(cmd_line, /(?i)Find-ForeignUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPO/)=true OR match_regex(cmd_line, /(?i)Get-NetGPO/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOComputerLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOComputerAdmin/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGPOGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOUserLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOLocation/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-DomainManagedSecurityGroup/)=true OR match_regex(cmd_line, /(?i)Find-ManagedSecurityGroups/)=true OR match_regex(cmd_line, /(?i)Get-DomainOU/)=true OR match_regex(cmd_line, /(?i)Get-NetOU/)=true OR match_regex(cmd_line, /(?i)Get-DomainUser/)=true OR match_regex(cmd_line, /(?i)Get-NetUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Get-UserEvent/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetLoggedon/)=true OR match_regex(cmd_line, /(?i)Get-RegLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegLastLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-LastLoggedOn/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | -| T1087 | x | x | -| T1484 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### Create local admin accounts using net exe -This search looks for the creation of local administrator accounts using net.exe. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1136.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`create_local_admin_accounts_using_net_exe_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Administrators often leverage net.exe to create admin accounts. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### Reconnaissance and Access to Computers via Mimikatz modules -This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1592 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::ServerInfo/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1592 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/gentilkiwi/mimikatz - -#### Test Dataset - -_version_: 1 -
---- -### Detect Regasm with Network Connection -The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.009 -- **Last Updated**: 2021-02-16 - -
- View - -#### Search -``` -`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_network_connection_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. - -#### References -* https://attack.mitre.org/techniques/T1218/009/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### System Process Running from Unexpected Location -An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1036 -- **Last Updated**: 2020-08-25 - -
- View - -#### Search -``` - $ssa_input = | from read_ssa_enriched_events() | eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), user=ucast(map_get(input_event, "dest_user_id"), "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=lower(ucast(map_get(input_event, "process_path"), "string", null)); -$cond_1 = | from $ssa_input | where process_name="arp.exe" OR process_name="adaptertroubleshooter.exe" OR process_name="applicationframehost.exe" OR process_name="atbroker.exe" OR process_name="authhost.exe" OR process_name="autoworkplace.exe" OR process_name="axinstui.exe" OR process_name="backgroundtransferhost.exe" OR process_name="bdehdcfg.exe" OR process_name="bdeuisrv.exe" OR process_name="bdeunlockwizard.exe" OR process_name="bitlockerdeviceencryption.exe" OR process_name="bitlockerwizard.exe" OR process_name="bitlockerwizardelev.exe" OR process_name="bytecodegenerator.exe" OR process_name="camerasettingsuihost.exe" OR process_name="castsrv.exe" OR process_name="certenrollctrl.exe" OR process_name="checknetisolation.exe" OR process_name="clipup.exe" OR process_name="cloudexperiencehostbroker.exe" OR process_name="cloudnotifications.exe" OR process_name="cloudstoragewizard.exe" OR process_name="compmgmtlauncher.exe" OR process_name="compattelrunner.exe" OR process_name="computerdefaults.exe" OR process_name="credentialuibroker.exe" OR process_name="dfdwiz.exe" OR process_name="dwwin.exe" OR process_name="dataexchangehost.exe" OR process_name="defrag.exe" OR process_name="devicedisplayobjectprovider.exe" OR process_name="deviceeject.exe" OR process_name="deviceenroller.exe" OR process_name="devicepairingwizard.exe" OR process_name="deviceproperties.exe" OR process_name="disksnapshot.exe" OR process_name="dism.exe" OR process_name="displayswitch.exe" OR process_name="dmnotificationbroker.exe" OR process_name="dmomacpmo.exe" OR process_name="dpiscaling.exe" OR process_name="dsmusertask.exe" OR process_name="dxpserver.exe" OR process_name="edpcleanup.exe" OR process_name="eosnotify.exe" OR process_name="eap3host.exe" OR process_name="easpoliciesbrokerhost.exe" OR process_name="easeofaccessdialog.exe" OR process_name="ehstorauthn.exe" OR process_name="fxscover.exe" OR process_name="fxssvc.exe" OR process_name="fxsunatd.exe" OR process_name="filehistory.exe" OR process_name="fondue.exe" OR process_name="gamepanel.exe" OR process_name="genvalobj.exe" OR process_name="gettingstarted.exe" OR process_name="hostname.exe" OR process_name="icsentitlementhost.exe" OR process_name="infdefaultinstall.exe" OR process_name="installagent.exe" OR process_name="languagecomponentsinstallercomhandler.exe" OR process_name="launchtm.exe" OR process_name="launchwinapp.exe" OR process_name="legacynetuxhost.exe" OR process_name="licensemanagershellext.exe" OR process_name="licensingui.exe" OR process_name="locationnotificationwindows.exe" OR process_name="locationnotifications.exe" OR process_name="locator.exe" OR process_name="lockapphost.exe" OR process_name="lockscreencontentserver.exe" OR process_name="logonui.exe" OR process_name="lsaiso.exe" OR process_name="mdeserver.exe" OR process_name="mdmagent.exe" OR process_name="mdmappinstaller.exe" OR process_name="mrinfo.exe" OR process_name="mrt.exe" OR process_name="mschedexe.exe" OR process_name="magnify.exe" OR process_name="mbaeparsertask.exe" OR process_name="mdres.exe" OR process_name="mdsched.exe" OR process_name="migautoplay.exe" OR process_name="mpsigstub.exe" OR process_name="msspellcheckinghost.exe" OR process_name="muiunattend.exe" OR process_name="multidigimon.exe" OR process_name="musnotification.exe" OR process_name="musnotificationux.exe" OR process_name="napstat.exe" OR process_name="netstat.exe" OR process_name="narrator.exe" OR process_name="netcfgnotifyobjecthost.exe" OR process_name="netevtfwdr.exe" OR process_name="netproj.exe" OR process_name="netplwiz.exe" OR process_name="networkuxbroker.exe"; -$cond_2 = | from $ssa_input | where process_name="openwith.exe" OR process_name="optionalfeatures.exe" OR process_name="pathping.exe" OR process_name="ping.exe" OR process_name="passwordonwakesettingflyout.exe" OR process_name="pickerhost.exe" OR process_name="pkgmgr.exe" OR process_name="pnpunattend.exe" OR process_name="pnputil.exe" OR process_name="presentationhost.exe" OR process_name="presentationsettings.exe" OR process_name="printbrmui.exe" OR process_name="printdialoghost.exe" OR process_name="printdialoghost3d.exe" OR process_name="printisolationhost.exe" OR process_name="proximityuxhost.exe" OR process_name="rdspnf.exe" OR process_name="rmactivate.exe" OR process_name="rmactivate_isv.exe" OR process_name="rmactivate_ssp.exe" OR process_name="rmactivate_ssp_isv.exe" OR process_name="route.exe" OR process_name="rdpsa.exe" OR process_name="rdpsaproxy.exe" OR process_name="rdpsauachelper.exe" OR process_name="reagentc.exe" OR process_name="recoverydrive.exe" OR process_name="register-cimprovider.exe" OR process_name="registeriepkeys.exe" OR process_name="relpost.exe" OR process_name="remoteposworker.exe" OR process_name="rmclient.exe" OR process_name="robocopy.exe" OR process_name="rpcping.exe" OR process_name="runlegacycplelevated.exe" OR process_name="runtimebroker.exe" OR process_name="sihclient.exe" OR process_name="searchfilterhost.exe" OR process_name="searchindexer.exe" OR process_name="searchprotocolhost.exe" OR process_name="secedit.exe" OR process_name="sensordataservice.exe" OR process_name="setieinstalleddate.exe" OR process_name="settingsynchost.exe" OR process_name="slidetoshutdown.exe" OR process_name="smartscreensettings.exe" OR process_name="sndvol.exe" OR process_name="snippingtool.exe" OR process_name="soundrecorder.exe" OR process_name="spaceagent.exe" OR process_name="sppextcomobj.exe" OR process_name="srtasks.exe" OR process_name="stikynot.exe" OR process_name="synchost.exe" OR process_name="sysreseterr.exe" OR process_name="systempropertiesadvanced.exe" OR process_name="systempropertiescomputername.exe" OR process_name="systempropertiesdataexecutionprevention.exe" OR process_name="systempropertieshardware.exe" OR process_name="systempropertiesperformance.exe" OR process_name="systempropertiesprotection.exe" OR process_name="systempropertiesremote.exe" OR process_name="systemsettingsadminflows.exe" OR process_name="systemsettingsbroker.exe" OR process_name="systemsettingsremovedevice.exe" OR process_name="tcpsvcs.exe" OR process_name="tracert.exe" OR process_name="tstheme.exe" OR process_name="tswbprxy.exe" OR process_name="tapiunattend.exe" OR process_name="taskmgr.exe" OR process_name="thumbnailextractionhost.exe" OR process_name="tokenbrokercookies.exe" OR process_name="tpminit.exe" OR process_name="tswpfwrp.exe" OR process_name="ui0detect.exe" OR process_name="upgraderesultsui.exe" OR process_name="useraccountbroker.exe" OR process_name="useraccountcontrolsettings.exe" OR process_name="usoclient.exe" OR process_name="utilman.exe" OR process_name="vssvc.exe" OR process_name="vaultcmd.exe" OR process_name="vaultsysui.exe" OR process_name="wfs.exe" OR process_name="wmpdmc.exe" OR process_name="wpdshextautoplay.exe" OR process_name="wscollect.exe" OR process_name="wsmanhttpconfig.exe" OR process_name="wsreset.exe" OR process_name="wudfhost.exe" OR process_name="wwahost.exe" OR process_name="wallpaperhost.exe" OR process_name="webcache.exe" OR process_name="werfault.exe" OR process_name="werfaultsecure.exe" OR process_name="winsat.exe" OR process_name="windows.media.backgroundplayback.exe" OR process_name="windowsactiondialog.exe" OR process_name="windowsanytimeupgrade.exe" OR process_name="windowsanytimeupgraderesults.exe"; -$cond_3 = | from $ssa_input | where process_name="windowsanytimeupgradeui.exe" OR process_name="windowsupdateelevatedinstaller.exe" OR process_name="workfolders.exe" OR process_name="wpcmon.exe" OR process_name="acu.exe" OR process_name="aitagent.exe" OR process_name="aitstatic.exe" OR process_name="alg.exe" OR process_name="appidcertstorecheck.exe" OR process_name="appidpolicyconverter.exe" OR process_name="at.exe" OR process_name="attrib.exe" OR process_name="audiodg.exe" OR process_name="auditpol.exe" OR process_name="autochk.exe" OR process_name="autoconv.exe" OR process_name="autofmt.exe" OR process_name="baaupdate.exe" OR process_name="backgroundtaskhost.exe" OR process_name="bcastdvr.exe" OR process_name="bcdboot.exe" OR process_name="bcdedit.exe" OR process_name="bdechangepin.exe" OR process_name="bdeunlock.exe" OR process_name="bitsadmin.exe" OR process_name="bootcfg.exe" OR process_name="bootim.exe" OR process_name="bootsect.exe" OR process_name="bridgeunattend.exe" OR process_name="browser_broker.exe" OR process_name="bthudtask.exe" OR process_name="cacls.exe" OR process_name="calc.exe" OR process_name="cdpreference.exe" OR process_name="certreq.exe" OR process_name="certutil.exe" OR process_name="change.exe" OR process_name="changepk.exe" OR process_name="charmap.exe" OR process_name="chglogon.exe" OR process_name="chgport.exe" OR process_name="chgusr.exe" OR process_name="chkdsk.exe" OR process_name="chkntfs.exe" OR process_name="choice.exe" OR process_name="cipher.exe" OR process_name="cleanmgr.exe" OR process_name="cliconfg.exe" OR process_name="clip.exe" OR process_name="cmd.exe" OR process_name="cmdkey.exe" OR process_name="cmdl32.exe" OR process_name="cmmon32.exe" OR process_name="cmstp.exe" OR process_name="cofire.exe" OR process_name="colorcpl.exe" OR process_name="comp.exe" OR process_name="compact.exe" OR process_name="conhost.exe" OR process_name="consent.exe" OR process_name="control.exe" OR process_name="convert.exe" OR process_name="credwiz.exe" OR process_name="cscript.exe" OR process_name="csrss.exe" OR process_name="ctfmon.exe" OR process_name="cttune.exe" OR process_name="cttunesvr.exe" OR process_name="dashost.exe" OR process_name="dccw.exe" OR process_name="dcomcnfg.exe" OR process_name="ddodiag.exe" OR process_name="dfrgui.exe" OR process_name="dialer.exe" OR process_name="diantz.exe" OR process_name="dinotify.exe" OR process_name="diskpart.exe" OR process_name="diskperf.exe" OR process_name="diskraid.exe" OR process_name="dispdiag.exe" OR process_name="djoin.exe" OR process_name="dllhost.exe" OR process_name="dllhst3g.exe" OR process_name="dmcertinst.exe" OR process_name="dmcfghost.exe" OR process_name="dmclient.exe" OR process_name="dnscacheugc.exe" OR process_name="doskey.exe" OR process_name="dpapimig.exe" OR process_name="dpnsvr.exe" OR process_name="driverquery.exe" OR process_name="drvcfg.exe" OR process_name="drvinst.exe" OR process_name="dsregcmd.exe" OR process_name="dstokenclean.exe" OR process_name="dvdplay.exe" OR process_name="dvdupgrd.exe" OR process_name="dwm.exe" OR process_name="dxdiag.exe" OR process_name="easinvoker.exe" OR process_name="efsui.exe"; -$cond_4 = | from $ssa_input | where process_name="embeddedapplauncher.exe" OR process_name="esentutl.exe" OR process_name="eudcedit.exe" OR process_name="eventcreate.exe" OR process_name="eventvwr.exe" OR process_name="expand.exe" OR process_name="extrac32.exe" OR process_name="fc.exe" OR process_name="fhmanagew.exe" OR process_name="find.exe" OR process_name="findstr.exe" OR process_name="finger.exe" OR process_name="fixmapi.exe" OR process_name="fltmc.exe" OR process_name="fodhelper.exe" OR process_name="fontdrvhost.exe" OR process_name="fontview.exe" OR process_name="forfiles.exe" OR process_name="fsavailux.exe" OR process_name="fsquirt.exe" OR process_name="fsutil.exe" OR process_name="ftp.exe" OR process_name="fvenotify.exe" OR process_name="fveprompt.exe" OR process_name="getmac.exe" OR process_name="gpresult.exe" OR process_name="gpscript.exe" OR process_name="gpupdate.exe" OR process_name="grpconv.exe" OR process_name="hdwwiz.exe" OR process_name="help.exe" OR process_name="hwrcomp.exe" OR process_name="hwrreg.exe" OR process_name="icacls.exe" OR process_name="icardagt.exe" OR process_name="icsunattend.exe" OR process_name="ie4uinit.exe" OR process_name="ieunatt.exe" OR process_name="ieetwcollector.exe" OR process_name="iexpress.exe" OR process_name="immersivetpmvscmgrsvr.exe" OR process_name="ipconfig.exe" OR process_name="irftp.exe" OR process_name="iscsicli.exe" OR process_name="iscsicpl.exe" OR process_name="isoburn.exe" OR process_name="klist.exe" OR process_name="ksetup.exe" OR process_name="ktmutil.exe" OR process_name="label.exe" OR process_name="licensingdiag.exe" OR process_name="lodctr.exe" OR process_name="logagent.exe" OR process_name="logman.exe" OR process_name="logoff.exe" OR process_name="lpkinstall.exe" OR process_name="lpksetup.exe" OR process_name="lpremove.exe" OR process_name="lsass.exe" OR process_name="lsm.exe" OR process_name="makecab.exe" OR process_name="manage-bde.exe" OR process_name="mblctr.exe" OR process_name="mcbuilder.exe" OR process_name="mctadmin.exe" OR process_name="mfpmp.exe" OR process_name="mmc.exe" OR process_name="mobsync.exe" OR process_name="mountvol.exe" OR process_name="mpnotify.exe" OR process_name="msconfig.exe" OR process_name="msdt.exe" OR process_name="msdtc.exe" OR process_name="msfeedssync.exe" OR process_name="msg.exe" OR process_name="mshta.exe" OR process_name="msiexec.exe" OR process_name="msinfo32.exe" OR process_name="mspaint.exe" OR process_name="msra.exe" OR process_name="mstsc.exe" OR process_name="mtstocom.exe" OR process_name="nbtstat.exe" OR process_name="ndadmin.exe" OR process_name="net.exe" OR process_name="net1.exe" OR process_name="netbtugc.exe" OR process_name="netcfg.exe" OR process_name="netiougc.exe" OR process_name="netsh.exe" OR process_name="newdev.exe" OR process_name="nltest.exe" OR process_name="notepad.exe" OR process_name="nslookup.exe" OR process_name="ntoskrnl.exe" OR process_name="ntprint.exe" OR process_name="ocsetup.exe" OR process_name="odbcad32.exe" OR process_name="odbcconf.exe" OR process_name="omadmclient.exe" OR process_name="omadmprc.exe"; -$cond_5 = | from $ssa_input | where process_name="openfiles.exe" OR process_name="osk.exe" OR process_name="p2phost.exe" OR process_name="pcalua.exe" OR process_name="pcaui.exe" OR process_name="pcawrk.exe" OR process_name="pcwrun.exe" OR process_name="perfmon.exe" OR process_name="phoneactivate.exe" OR process_name="plasrv.exe" OR process_name="poqexec.exe" OR process_name="powercfg.exe" OR process_name="prevhost.exe" OR process_name="print.exe" OR process_name="printfilterpipelinesvc.exe" OR process_name="printui.exe" OR process_name="proquota.exe" OR process_name="provtool.exe" OR process_name="psr.exe" OR process_name="pwlauncher.exe" OR process_name="qappsrv.exe" OR process_name="qprocess.exe" OR process_name="query.exe" OR process_name="quser.exe" OR process_name="qwinsta.exe" OR process_name="rasautou.exe" OR process_name="rasdial.exe" OR process_name="raserver.exe" OR process_name="rasphone.exe" OR process_name="rdpclip.exe" OR process_name="rdpinput.exe" OR process_name="rdrleakdiag.exe" OR process_name="recdisc.exe" OR process_name="recover.exe" OR process_name="reg.exe" OR process_name="regedt32.exe" OR process_name="regini.exe" OR process_name="regsvr32.exe" OR process_name="rekeywiz.exe" OR process_name="relog.exe" OR process_name="repair-bde.exe" OR process_name="replace.exe" OR process_name="reset.exe" OR process_name="resmon.exe" OR process_name="rmttpmvscmgrsvr.exe" OR process_name="rrinstaller.exe" OR process_name="rstrui.exe" OR process_name="runas.exe" OR process_name="rundll32.exe" OR process_name="runonce.exe" OR process_name="rwinsta.exe" OR process_name="sbunattend.exe" OR process_name="sc.exe" OR process_name="schtasks.exe" OR process_name="sdbinst.exe" OR process_name="sdchange.exe" OR process_name="sdclt.exe" OR process_name="sdiagnhost.exe" OR process_name="secinit.exe" OR process_name="services.exe" OR process_name="sessionmsg.exe" OR process_name="sethc.exe" OR process_name="setspn.exe" OR process_name="setupcl.exe" OR process_name="setupugc.exe" OR process_name="setx.exe" OR process_name="sfc.exe" OR process_name="shadow.exe" OR process_name="shrpubw.exe" OR process_name="shutdown.exe" OR process_name="sigverif.exe" OR process_name="sihost.exe" OR process_name="slui.exe" OR process_name="smss.exe" OR process_name="snmptrap.exe" OR process_name="sort.exe" OR process_name="spinstall.exe" OR process_name="spoolsv.exe" OR process_name="sppsvc.exe" OR process_name="spreview.exe" OR process_name="srdelayed.exe" OR process_name="subst.exe" OR process_name="svchost.exe" OR process_name="sxstrace.exe" OR process_name="syskey.exe" OR process_name="systeminfo.exe" OR process_name="systemreset.exe" OR process_name="systray.exe" OR process_name="tabcal.exe" OR process_name="takeown.exe" OR process_name="taskeng.exe" OR process_name="taskhost.exe" OR process_name="taskhostw.exe" OR process_name="taskkill.exe" OR process_name="tasklist.exe" OR process_name="taskmgr.exe" OR process_name="tcmsetup.exe" OR process_name="timeout.exe" OR process_name="tpmvscmgr.exe" OR process_name="tpmvscmgrsvr.exe"; -$cond_6 = | from $ssa_input | where process_name="tracerpt.exe" OR process_name="tscon.exe" OR process_name="tsdiscon.exe" OR process_name="tskill.exe" OR process_name="typeperf.exe" OR process_name="tzsync.exe" OR process_name="tzutil.exe" OR process_name="ucsvc.exe" OR process_name="unlodctr.exe" OR process_name="unregmp2.exe" OR process_name="upnpcont.exe" OR process_name="userinit.exe" OR process_name="vds.exe" OR process_name="vdsldr.exe" OR process_name="verclsid.exe" OR process_name="verifier.exe" OR process_name="verifiergui.exe" OR process_name="vmicsvc.exe" OR process_name="vssadmin.exe" OR process_name="w32tm.exe" OR process_name="waitfor.exe" OR process_name="wbadmin.exe" OR process_name="wbengine.exe" OR process_name="wecutil.exe" OR process_name="wermgr.exe" OR process_name="wevtutil.exe" OR process_name="wextract.exe" OR process_name="where.exe" OR process_name="whoami.exe" OR process_name="wiaacmgr.exe" OR process_name="wiawow64.exe" OR process_name="wifitask.exe" OR process_name="wimserv.exe" OR process_name="wininit.exe" OR process_name="winload.exe" OR process_name="winlogon.exe" OR process_name="winresume.exe" OR process_name="winrs.exe" OR process_name="winrshost.exe" OR process_name="winver.exe" OR process_name="wisptis.exe" OR process_name="wkspbroker.exe" OR process_name="wksprt.exe" OR process_name="wlanext.exe" OR process_name="wlrmdr.exe" OR process_name="wowreg32.exe" OR process_name="wpnpinst.exe" OR process_name="wpr.exe" OR process_name="write.exe" OR process_name="wscript.exe" OR process_name="wsmprovhost.exe" OR process_name="wsqmcons.exe" OR process_name="wuapihost.exe" OR process_name="wuapp.exe" OR process_name="wuauclt.exe" OR process_name="wusa.exe" OR process_name="xcopy.exe" OR process_name="xpsrchvw.exe" OR process_name="xwizard.exe"; -| from $cond_1 | union $cond_2 | union $cond_3 | union $cond_4 | union $cond_5 | union $cond_6 | where process_path!="c:\\windows\\system32" AND process_path!="c:\\windows\\syswow64" | eval start_time = timestamp, end_time = timestamp, entities = mvappend(device, user), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -Collect endpoint data such as sysmon or 4688 events. - -#### Required fields -* dest_device_id -* process_name -* _time -* dest_user_id -* process_path - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1036 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Rare Parent-Child Process Relationship -An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1203, T1059, T1053, T1072 -- **Last Updated**: 2020-08-13 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) | where parent_process_name!=null | select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id | conditional_anomaly conditional="parent_process_name" target="process_name" | rename output as input | where input < 1 | adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L | where label AND quantile<0.1 AND (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe") -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -Collect endpoint data such as sysmon or 4688 events. - -#### Required fields -* process_name -* parent_process_name -* _time -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1203 | x | x | -| T1059 | x | x | -| T1053 | x | x | -| T1072 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. - - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Setting Credentials via PowerSploit modules -This detection identifies illegal setting of credentials via PowerSploit modules. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1068, T1078, T1098 -- **Last Updated**: 2020-11-03 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainUserPassword/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | -| T1078 | x | x | -| T1098 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### Dump LSASS via procdump -Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\ -During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.001 -- **Last Updated**: 2021-02-01 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://attack.mitre.org/techniques/T1003/001/ -* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Disabling Remote User Account Control -The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1548.002 -- **Last Updated**: 2020-11-18 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1548.002 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### Reconnaissance of Credential Stores and Services via Mimikatz modules -This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1589.001, T1590.001, T1590.003, T1068, T1078, T1098 -- **Last Updated**: 2020-11-03 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)crypto::capi/)=true OR match_regex(cmd_line, /(?i)crypto::cng/)=true OR match_regex(cmd_line, /(?i)crypto::providers/)=true OR match_regex(cmd_line, /(?i)crypto::stores/)=true OR match_regex(cmd_line, /(?i)crypto::sc/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1589.001 | x | x | -| T1590.001 | x | x | -| T1590.003 | x | x | -| T1068 | x | x | -| T1078 | x | x | -| T1098 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/gentilkiwi/mimikatz - -#### Test Dataset - -_version_: 1 -
---- -### Creation of Shadow Copy with wmic and powershell -This search detects the use of wmic and Powershell to create a shadow copy. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.003 -- **Last Updated**: 2019-12-10 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_with_wmic_and_powershell_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Legtimate administrator usage of wmic to create a shadow copy. - -#### References -* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Detect HTML Help Renamed -The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.001 -- **Last Updated**: 2021-02-11 - -
- View - -#### Search -``` -`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_renamed_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed hh.exe may be used. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed. - -#### References -* https://attack.mitre.org/techniques/T1218/001/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md -* https://lolbas-project.github.io/lolbas/Binaries/Hh/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Windows Security Account Manager Stopped -The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1489 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_security_account_manager_stopped_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1489 | x | x | - -#### Kill Chain Phases -* Delivery - -#### Known False Positives -SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log - -_version_: 1 -
---- -### Reconnaissance and Access to Accounts and Groups via Mimikatz modules -This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1078, T1087, T1484 -- **Last Updated**: 2020-11-05 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::user/)=true OR match_regex(cmd_line, /(?i)net::group/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | -| T1087 | x | x | -| T1484 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/gentilkiwi/mimikatz - -#### Test Dataset - -_version_: 1 -
---- -### Detect Rundll32 Application Control Bypass - syssetup -The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 -- **Last Updated**: 2021-02-04 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___syssetup_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive. - -#### References -* https://attack.mitre.org/techniques/T1218/011/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md -* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 -* https://lolbas-project.github.io/lolbas/Libraries/Syssetup/ -* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### System Processes Run From Unexpected Locations -This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1036.003 -- **Last Updated**: 2020-12-08 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name Processes.process_hash| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file` | `system_processes_run_from_unexpected_locations_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1036.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log - -_version_: 5 -
---- -### USN Journal Deletion -The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1070 -- **Last Updated**: 2018-12-03 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1070 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/atomic_red_team/windows-sysmon.log - -_version_: 2 -
---- -### Detect Regasm Spawning a Process -The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.009 -- **Last Updated**: 2021-02-12 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_spawning_a_process_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. - -#### References -* https://attack.mitre.org/techniques/T1218/009/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ -* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Credential Extraction indicative of Lazagne command line options -Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003, T1555 -- **Last Updated**: 2020-10-18 - -
- View - -#### Search -``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)all\s+\-oA\s+\-output/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | -| T1555 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Credential Extraction indicative of FGDump and CacheDump with v option -Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003 -- **Last Updated**: 2020-10-18 - -
- View - -#### Search -``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null) | where cmd_line != null AND process_name != null AND process_path != null AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-v/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* process_name -* _time -* process_path -* dest_user_id -* process - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect HTML Help Using InfoTech Storage Handlers -The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.001 -- **Last Updated**: 2021-02-11 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_using_infotech_storage_handlers_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed. - -#### References -* https://attack.mitre.org/techniques/T1218/001/ -* https://www.kb.cert.org/vuls/id/851869 -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md -* https://lolbas-project.github.io/lolbas/Binaries/Hh/ -* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 -* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Common Ransomware Notes -The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1485 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_notes` | `common_ransomware_notes_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1485 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It's possible that a legitimate file could be created with the same name used by ransomware note files. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/windows-sysmon.log - -_version_: 4 -
---- -### Reconnaissance and Access to Processes and Services via Mimikatz modules -This detection identifies use of Mimikatz modules for discovery and access to services and processes. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1007, T1046, T1057 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::list/)=true OR match_regex(cmd_line, /(?i)service::list/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1007 | x | x | -| T1046 | x | x | -| T1057 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/gentilkiwi/mimikatz - -#### Test Dataset - -_version_: 1 -
---- -### Sc exe Manipulating Windows Services -This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1543.003 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1543.003 | x | x | - -#### Kill Chain Phases -* Installation - -#### Known False Positives -Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### Overwriting Accessibility Binaries -Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1546.008 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.008 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.008/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### Detect Regsvcs Spawning a Process -The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.009 -- **Last Updated**: 2021-02-12 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_spawning_a_process_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. - -#### References -* https://attack.mitre.org/techniques/T1218/009/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Detect MSHTA Url in Command Line -This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.005 -- **Last Updated**: 2021-01-20 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.005 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -It is possible legitimate applications may perform this behavior and will need to be filtered. - -#### References -* https://github.com/redcanaryco/AtomicTestHarnesses -* https://redcanary.com/blog/introducing-atomictestharnesses/ -* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### WBAdmin Delete System Backups -This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1490 -- **Last Updated**: 2021-01-22 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `wbadmin_delete_system_backups_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1490 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Administrators may modify the boot configuration. - -#### References -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md -* https://thedfirreport.com/2020/10/08/ryuks-return/ -* https://attack.mitre.org/techniques/T1490/ -* https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Illegal Management of Computers and Active Directory Elements via PowerSploit modules -This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1098, T1207, T1484 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainObject/)=true OR match_regex(cmd_line, /(?i)Set-ADObject/)=true OR match_regex(cmd_line, /(?i)Set-DomainObjectOwner/)=true OR match_regex(cmd_line, /(?i)Set-MasterBootRecord/)=true ) - -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1098 | x | x | -| T1207 | x | x | -| T1484 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### Suspicious Reg exe Process -This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1112 -- **Last Updated**: 2020-07-22 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup process_id| table process_id dest] | `suspicious_reg_exe_process_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1112 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. - -#### References -* https://car.mitre.org/wiki/CAR-2013-03-001 - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### Detect New Local Admin account -This search looks for newly created accounts that have been elevated to local administrators. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1136.001 -- **Last Updated**: 2020-07-08 - -
- View - -#### Search -``` -`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) | transaction member_id connected=false maxspan=180m | rename member_id as user | stats count min(_time) as firstTime max(_time) as lastTime by user dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_local_admin_account_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732 - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives -* Command and Control - -#### Known False Positives -The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log - -_version_: 2 -
---- -### Reconnaissance and Access to Computers and Domains via PowerSploit modules -This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1592, T1590, T1087 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ComputerDetail/)=true OR match_regex(cmd_line, /(?i)Get-Domain/)=true OR match_regex(cmd_line, /(?i)Get-NetDomain/)=true OR match_regex(cmd_line, /(?i)Get-DomainComputer/)=true OR match_regex(cmd_line, /(?i)Get-NetComputer/)=true OR match_regex(cmd_line, /(?i)Get-DomainController/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainController/)=true OR match_regex(cmd_line, /(?i)Get-DomainFileServer/)=true OR match_regex(cmd_line, /(?i)Get-NetFileServer/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1592 | x | x | -| T1590 | x | x | -| T1087 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### Suspicious msbuild path -The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1127.001, T1036.003 -- **Last Updated**: 2021-01-12 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `suspicious_msbuild_path_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1127.001 | x | x | -| T1036.003 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on. - -#### References -* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log - -_version_: 1 -
---- -### Credential Extraction native Microsoft debuggers peek into the kernel -Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003 -- **Last Updated**: 2020-10-18 - -
- View - -#### Search -``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND parent_process_name != null AND process_name != null AND ( match_regex(parent_process_name, /(?i)ntkd\.exe/)=true OR match_regex(parent_process_name, /(?i)livekd\.exe/)=true ) AND match_regex(process_name, /(?i)conhost\.exe/)=true AND match_regex(cmd_line, /(?i)0xffffffff/)=true AND match_regex(cmd_line, /(?i)\-ForceV1/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* process_name -* parent_process_name -* _time -* dest_device_id -* dest_user_id -* process - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. - -#### References -* https://medium.com/@clermont1050/covid-19-cyber-infection-c615ead7c29 - -#### Test Dataset - -_version_: 1 -
---- -### Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass -Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.001 | x | x | - -#### Kill Chain Phases -* Installation -* Actions on Objectives - -#### Known False Positives -Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log - -_version_: 6 -
---- -### Remote Process Instantiation via WMI -This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1047 -- **Last Updated**: 2020-11-30 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log - -_version_: 5 -
---- -### Detect mshta inline hta execution -The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.005 -- **Last Updated**: 2021-01-20 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_inline_hta_execution_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.005 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. - -#### References -* https://github.com/redcanaryco/AtomicTestHarnesses -* https://redcanary.com/blog/introducing-atomictestharnesses/ -* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log - -_version_: 5 -
---- -### Samsam Test File Write -The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1486 -- **Last Updated**: 2018-12-14 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1486 | x | x | - -#### Kill Chain Phases -* Delivery - -#### Known False Positives -No false positives have been identified. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/sam_sam_note/windows-sysmon.log - -_version_: 1 -
---- -### Suspicious microsoft workflow compiler usage -The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1127 -- **Last Updated**: 2021-01-12 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_usage_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1127 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM. - -#### References -* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log - -_version_: 1 -
---- -### Illegal Enabling or Disabling of Accounts via DSInternals modules -This detection identifies use of DSInternals modules that enable or disable accounts illegaly. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1078, T1098 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Disable-ADDBAccount/)=true OR match_regex(cmd_line, /(?i)Enable-ADDBAccount/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | -| T1098 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/MichaelGrafnetter/DSInternals - -#### Test Dataset - -_version_: 1 -
---- -### Deleting Shadow Copies -The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1490 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `deleting_shadow_copies_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1490 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### Reconnaissance and Access to Shared Resources via Mimikatz modules -This detection identifies use of Mimikatz modules for discovery and access to network shares. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1021.002, T1135, T1039 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::share/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.002 | x | x | -| T1135 | x | x | -| T1039 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/gentilkiwi/mimikatz - -#### Test Dataset - -_version_: 1 -
---- -### Attempt To Stop Security Service -This search looks for attempts to stop security-related services on the endpoint. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |lookup security_services_lookup service as process OUTPUTNEW category, description | search category=security | `attempt_to_stop_security_service_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.001 | x | x | - -#### Kill Chain Phases -* Installation -* Actions on Objectives - -#### Known False Positives -None identified. Attempts to disable security-related services should be identified and understood. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log - -_version_: 3 -
---- -### RunDLL Loading DLL By Ordinal -This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 -- **Last Updated**: 2020-11-30 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll_loading_dll_by_ordinal_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | - -#### Kill Chain Phases -* Installation - -#### Known False Positives -While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### Ryuk Test Files Detected -The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1486 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1486 | x | x | - -#### Kill Chain Phases -* Delivery - -#### Known False Positives -If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log - -_version_: 1 -
---- -### Credential Extraction indicative of use of Mimikatz modules -Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003 -- **Last Updated**: 2020-10-21 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)CRYPTO::Certificates/)=true OR match_regex(cmd_line, /(?i)CRYPTO::keys/)=true OR match_regex(cmd_line, /(?i)kerberos::list/)=true OR match_regex(cmd_line, /(?i)kerberos::tgt/)=true OR match_regex(cmd_line, /(?i)lsadump::sam/)=true OR match_regex(cmd_line, /(?i)lsadump::secrets/)=true OR match_regex(cmd_line, /(?i)lsadump::cache/)=true OR match_regex(cmd_line, /(?i)lsadump::lsa/)=true OR match_regex(cmd_line, /(?i)lsadump::trust/)=true OR match_regex(cmd_line, /(?i)lsadump::backupkeys/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/gentilkiwi/mimikatz - -#### Test Dataset - -_version_: 1 -
---- -### Credential Dumping via Copy Command from Shadow Copy -This search detects credential dumping using copy command from a shadow copy. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.003 -- **Last Updated**: 2019-12-10 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_copy_command_from_shadow_copy_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -unknown - -#### References -* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Scheduled Task Deleted Or Created via CMD -This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1053.005 -- **Last Updated**: 2020-12-17 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_deleted_or_created_via_cmd_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1053.005 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Tasks should not be manually created via CLI, this is rarely done by admins as well - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log - -_version_: 5 -
---- -### Suspicious wevtutil Usage -The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1070.001 -- **Last Updated**: 2020-07-22 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `suspicious_wevtutil_usage_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1070.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-sysmon.log - -_version_: 3 -
---- -### WMI Permanent Event Subscription - Sysmon -This search looks for the creation of WMI permanent event subscriptions. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1546.003 -- **Last Updated**: 2020-12-08 - -
- View - -#### Search -``` -`sysmon` EventCode=21 | rename host as dest | table _time, dest, user, Operation, EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, administrators may use event subscriptions for legitimate purposes. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log - -_version_: 2 -
---- -### Malicious PowerShell Process With Obfuscation Techniques -This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001 -- **Last Updated**: 2021-01-19 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) | `malicious_powershell_process_with_obfuscation_techniques_filter` | search num_obfuscation > 10 -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.001 | x | x | - -#### Kill Chain Phases -* Command and Control -* Actions on Objectives - -#### Known False Positives -These characters might be legitimately on the command-line, but it is not common. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/obfuscated_powershell/windows-sysmon.log - -_version_: 4 -
---- -### Detect mshta renamed -The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.005 -- **Last Updated**: 2021-01-20 - -
- View - -#### Search -``` -`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `detect_mshta_renamed_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.005 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive. - -#### References -* https://github.com/redcanaryco/AtomicTestHarnesses -* https://redcanary.com/blog/introducing-atomictestharnesses/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log - -_version_: 1 -
---- -### Suspicious Rundll32 StartW -The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 -- **Last Updated**: 2021-02-04 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_startw_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed. - -#### References -* https://attack.mitre.org/techniques/T1218/011/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md -* https://www.cobaltstrike.com/help-windows-executable -* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 -* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Detect HTML Help Spawn Child Process -The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.001 -- **Last Updated**: 2021-02-11 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_spawn_child_process_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed. - -#### References -* https://attack.mitre.org/techniques/T1218/001/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md -* https://lolbas-project.github.io/lolbas/Binaries/Hh/ -* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 -* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Detect Kerberoasting -This search detects a potential kerberoasting attack via service principal name requests - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1558.003 -- **Last Updated**: 2020-10-21 - -
- View - -#### Search -``` - | from read_ssa_enriched_events() | eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id") | where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" | first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] | where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID | eval start_time=_time, end_time=_time, body="TBD", entities="TBD" | select start_time, end_time, entities, body | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2 - -#### Required fields -* service_name -* _time -* event_code -* ticket_encryption_type -* service_id -* ticket_options - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1558.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Older systems that support kerberos RC4 by default NetApp may generate false positives - -#### References -* Initial ESCU implementation by Jose Hernandez and Patrick Bareiss - -#### Test Dataset - -_version_: 1 -
---- -### Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules -This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1068, T1078, T1098 -- **Last Updated**: 2020-11-05 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-PrivescAudit/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* _time -* process -* dest_device_id -* dest_user_id - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | -| T1078 | x | x | -| T1098 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- +===== ### Applying Stolen Credentials via Mimikatz modules This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack. @@ -6011,1224 +1389,8 @@ None identified. _version_: 1 ---- -### Detect HTML Help URL in Command Line -The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.001 -- **Last Updated**: 2021-02-11 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_url_in_command_line_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed. - -#### References -* https://attack.mitre.org/techniques/T1218/001/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md -* https://lolbas-project.github.io/lolbas/Binaries/Hh/ -* https://blog.sevagas.com/?Hacking-around-HTA-files -* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 -* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Detect Use of cmd exe to Launch Script Interpreters -This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.003 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.003 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Some legitimate applications may exhibit this behavior. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/cmd_spawns_cscript/windows-sysmon.log - -_version_: 4 -
---- -### Illegal Deletion of Logs via Mimikatz modules -This detection identifies access to PowerSploit modules that delete event logs. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1070 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)event::drop/)=true OR match_regex(cmd_line, /(?i)event::clear/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1070 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/gentilkiwi/mimikatz - -#### Test Dataset - -_version_: 1 -
---- -### Detect Excessive User Account Lockouts -This search detects user accounts that have been locked out a relatively high number of times in a short period. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.003 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_user_account_lockouts_filter` -``` -#### Associated Analytic Story - -#### How To Implement -ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.003 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log - -_version_: 3 -
---- -### Suspicious MSBuild Rename -The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1127.001, T1036.003 -- **Last Updated**: 2021-01-12 - -
- View - -#### Search -``` -`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_msbuild_rename_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1127.001 | x | x | -| T1036.003 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive. - -#### References -* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md -* https://github.com/infosecn1nja/MaliciousMacroMSBuild/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log - -_version_: 1 -
---- -### Attempt To Add Certificate To Untrusted Store -Attempt to add a certificate to the certificate store - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1553.004 -- **Last Updated**: 2020-11-03 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1553.004 | x | x | - -#### Kill Chain Phases -* Installation -* Actions on Objectives - -#### Known False Positives -There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log - -_version_: 6 -
---- -### Illegal Account Creation via PowerSploit modules -This detection identifies access to PowerSploit modules that create accounts illegaly. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1585 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)New-DomainUser/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1585 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### System Information Discovery Detection -Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1082 -- **Last Updated**: 2020-10-12 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest by dest | where dc_processes_by_dest > 2 | stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1082 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Administrators debugging servers - -#### References -* https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Illegal Privilege Elevation and Persistence via PowerSploit modules -This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1053, T1134, T1548 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Add-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Enable-Privilege/)=true OR match_regex(cmd_line, /(?i)New-ElevatedPersistenceOption/)=true OR match_regex(cmd_line, /(?i)New-UserPersistenceOption/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1053 | x | x | -| T1134 | x | x | -| T1548 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://github.com/PowerShellMafia/PowerSploit - -#### Test Dataset - -_version_: 1 -
---- -### Detect Activity Related to Pass the Hash Attacks -This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1550.002 -- **Last Updated**: 2020-10-15 - -
- View - -#### Search -``` -`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_activity_related_to_pass_the_hash_attacks_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1550.002 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.002/atomic_red_team/windows-security.log - -_version_: 5 -
---- -### Suspicious mshta child process -The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.005 -- **Last Updated**: 2021-01-12 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_child_process_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.005 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. - -#### References -* https://github.com/redcanaryco/AtomicTestHarnesses -* https://redcanary.com/blog/introducing-atomictestharnesses/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Detect Regasm with no Command Line Arguments -The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.009 -- **Last Updated**: 2021-02-12 - -
- View - -#### Search -``` -`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) | regex CommandLine="(regasm\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_no_command_line_arguments_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. - -#### References -* https://attack.mitre.org/techniques/T1218/009/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Malicious PowerShell Process - Execution Policy Bypass -This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.001 | x | x | - -#### Kill Chain Phases -* Command and Control -* Actions on Objectives - -#### Known False Positives -There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/windows-sysmon.log - -_version_: 4 -
---- -### Suspicious MSBuild Spawn -The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1127.001 -- **Last Updated**: 2021-01-12 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_msbuild_spawn_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1127.001 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. - -#### References -* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log - -_version_: 1 -
---- -### Process Creating LNK file in Suspicious Location -This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1566.002 -- **Last Updated**: 2021-01-28 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_id as lnk_pid | join lnk_pid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` | rename parent_process_id as lnk_pid | fields _time lnk_pid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `process_creating_lnk_file_in_suspicious_location_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1566.002 | x | x | - -#### Kill Chain Phases -* Installation -* Actions on Objectives - -#### Known False Positives -This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories. - -#### References -* https://attack.mitre.org/techniques/T1566/001/ -* https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log - -_version_: 4 -
---- -### Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals -Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003 -- **Last Updated**: 2020-10-18 - -
- View - -#### Search -``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)Get-ADDBAccount/)=true AND match_regex(cmd_line, /(?i)\-dbpath[\s;:\.\|]+/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* dest_device_id -* dest_user_id -* process -* _time - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Batch File Write to System32 -The search looks for a batch file (.bat) written to the Windows system directory tree. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1204.002 -- **Last Updated**: 2018-12-14 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat | `batch_file_write_to_system32_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1204.002 | x | x | - -#### Kill Chain Phases -* Delivery - -#### Known False Positives -It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/batch_file_in_system32/windows-sysmon.log - -_version_: 1 -
---- -### Detect Dump LSASS Memory using comsvcs -This search detects the memory of lsass.exe being dumped for offline credential theft attack. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003.003 -- **Last Updated**: 2020-09-15 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() | eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)) | where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true | eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including Windows command line logging. You can see how we test this with [Event Code 4688](https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4688a) on the [attack_range](https://github.com/splunk/attack_range/blob/develop/ansible/roles/windows_common/tasks/windows-enable-4688-cmd-line-audit.yml). - -#### Required fields -* process_name -* _tenant -* _time -* dest_device_id -* process - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References -* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -#### Test Dataset - -_version_: 1 -
---- -### Create or delete windows shares using net exe -This search looks for the creation or deletion of hidden shares using net.exe. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1070.005 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*share* | `create_or_delete_windows_shares_using_net_exe_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1070.005 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. - -#### References -* https://attack.mitre.org/techniques/T1070/005 - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log - -_version_: 5 -
---- -### NLTest Domain Trust Discovery -This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1482 -- **Last Updated**: 2021-01-25 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `nltest_domain_trust_discovery_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1482 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Administrators may use nltest for troubleshooting purposes, otherwise, rarely used. - -#### References -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md -* https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104 -* https://attack.mitre.org/techniques/T1482/ -* https://www.owasp.org/images/4/4b/Red_Team_Operating_in_a_Modern_Environment.pdf -* https://ss64.com/nt/nltest.html -* https://redcanary.com/threat-detection-report/techniques/domain-trust-discovery/ -* https://thedfirreport.com/2020/10/08/ryuks-return/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Creation of Shadow Copy -Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.003 -- **Last Updated**: 2019-12-10 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Legitimate administrator usage of Vssadmin or Wmic will create false positives. - -#### References -* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Registry Keys Used For Privilege Escalation -This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1546.012 -- **Last Updated**: 2020-11-27 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_privilege_escalation_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.012 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. - -#### References -* https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### Detect Excessive Account Lockouts From Endpoint -This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.002 -- **Last Updated**: 2020-11-09 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \ - **Splunk>Phantom Playbook Integration**\ -If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ -(Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\ - - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.002 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log - -_version_: 5 -
---- -### Credential Extraction native Microsoft debuggers via z command line option -Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1003 -- **Last Updated**: 2020-10-18 - -
- View - -#### Search -``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) | where cmd_line != null AND process_name != null AND ( match_regex(process_name, /^(?i)ntkd\.exe/)=true OR match_regex(process_name, /^(?i)kd\.exe/)=true ) AND match_regex(cmd_line, /(?i)\-z\s+/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. - -#### Required fields -* process_name -* _time -* dest_device_id -* dest_user_id -* process - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Creation of lsass Dump with Taskmgr -Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.001 -- **Last Updated**: 2020-02-03 - -
- View - -#### Search -``` -`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp | stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `creation_of_lsass_dump_with_taskmgr_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. - -#### References -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager -* https://attack.mitre.org/techniques/T1003/001/ -* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log - -_version_: 1 -
---- -### Hiding Files And Directories With Attrib exe -Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1222.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `hiding_files_and_directories_with_attrib_exe_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1222.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Some applications and users may legitimately use attrib.exe to interact with the files. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/atomic_red_team/windows-sysmon.log - -_version_: 4 -
---- -### First time seen command line argument -This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1059, T1117, T1202 -- **Last Updated**: 2021-2-1 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), cmd_line_norm=lower(cmd_line), cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/, "GUID"), cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/, "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N") | where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true | select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id | first_time_event input_columns=["cmd_line_norm"] | where first_time_cmd_line_norm | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -You must be populating the endpoint data model for SSA and specifically the process_name and the process fields - -#### Required fields -* process_name -* _time -* dest_device_id -* dest_user_id -* process - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059 | x | x | -| T1117 | x | x | -| T1202 | x | x | - -#### Kill Chain Phases -* Command and Control -* Actions on Objectives - -#### Known False Positives -Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name - -#### References - -#### Test Dataset - -_version_: 2 -
---- +===== ### Applying Stolen Credentials via PowerSploit modules Stolen credentials are applied by methods such as user impersonation, credential injection, spoofing of authentication processes or getting hold of critical accounts. This detection indicates such activities carried out by PowerSploit exploit kit APIs. @@ -7286,63 +1448,76 @@ None identified. _version_: 1 ---- -### Detect PsExec With accepteula Flag -This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +===== +### Assessment of Credential Strength via DSInternals modules +This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised. + +- **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1021.002 -- **Last Updated**: 2020-11-10 +- **ATT&CK**: T1078, T1098, T1087, T1201, T1552, T1555 +- **Last Updated**: 2020-11-03
View #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-PasswordQuality/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ``` #### Associated Analytic Story #### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields +* _time +* process +* dest_device_id +* dest_user_id #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1021.002 | x | x | +| T1078 | x | x | +| T1098 | x | x | +| T1087 | x | x | +| T1201 | x | x | +| T1552 | x | x | +| T1555 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine +None identified. #### References +* https://github.com/MichaelGrafnetter/DSInternals #### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/windows-sysmon.log -_version_: 3 +_version_: 1
---- -### Detect Path Interception By Creation Of program exe -The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. + +===== +### Attempt To Add Certificate To Untrusted Store +Attempt to add a certificate to the certificate store - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1574.009 -- **Last Updated**: 2020-07-03 +- **ATT&CK**: T1553.004 +- **Last Updated**: 2020-11-03
View #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest index | `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?[^\\\\]*\.(?:exe|bat|com|ps1))" | eval process_name = lower(process_name) | eval service_process = lower(service_process) | where process_name != service_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter` +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter` ``` #### Associated Analytic Story @@ -7355,7 +1530,1266 @@ You must be ingesting data that records process activity from your hosts to popu | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1574.009 | x | x | +| T1553.004 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log + +_version_: 6 +
+ +===== +### Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass +Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log + +_version_: 6 +
+ +===== +### Attempt To Stop Security Service +This search looks for attempts to stop security-related services on the endpoint. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |lookup security_services_lookup service as process OUTPUTNEW category, description | search category=security | `attempt_to_stop_security_service_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.001 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +None identified. Attempts to disable security-related services should be identified and understood. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+ +===== +### Attempted Credential Dump From Registry via Reg exe +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.002 +- **Last Updated**: 2019-12-02 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### Attempted Credential Dump From Registry via Reg exe +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-6-04 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) | where process_name="cmd.exe" OR process_name="reg.exe" | where cmd_line != null AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting windows endpoint data that tracks process activity, including parent-child relationships from your endpoints. + +#### Required fields +* process_name +* _time +* dest_device_id +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/splunk/security_content/blob/55a17c65f9f56c2220000b62701765422b46125d/detections/attempted_credential_dump_from_registry_via_reg_exe.yml + +#### Test Dataset + +_version_: 1 +
+ +===== +### BCDEdit Failure Recovery Modification +This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1490 +- **Last Updated**: 2020-12-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bcdedit_failure_recovery_modification_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1490 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators may modify the boot configuration. + +#### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Batch File Write to System32 +The search looks for a batch file (.bat) written to the Windows system directory tree. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1204.002 +- **Last Updated**: 2018-12-14 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat | `batch_file_write_to_system32_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1204.002 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/batch_file_in_system32/windows-sysmon.log + +_version_: 1 +
+ +===== +### Certutil exe certificate extraction +This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation + +#### Known False Positives +Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log + +_version_: 1 +
+ +===== +### Child Processes of Spoolsv exe +This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1068 +- **Last Updated**: 2020-03-16 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `child_processes_of_spoolsv_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### Clients Connecting to Multiple DNS Servers +This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 | `clients_connecting_to_multiple_dns_servers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ +This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048.003 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### Cloud API Calls From Previously Unseen User Roles +This search looks for new commands from each user role. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-09-04 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUserApiCall=min(firstTimeSeen) | where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") | table firstTime, user, object, command |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `cloud_api_calls_from_previously_unseen_user_roles_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Cloud Compute Instance Created By Previously Unseen User +This search looks for cloud compute instances created by users who have not created them before. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-08-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime, user, dest, count vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_by_previously_unseen_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Cloud Compute Instance Created In Previously Unused Region +This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2020-09-02 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") | table firstTime, user, dest, count , vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_in_previously_unused_region_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Cloud Compute Instance Created With Previously Unseen Image +This search looks for cloud compute instances being created with previously unseen image IDs. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-10-12 + +
+ View + +#### Search +``` +| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenImage=min(firstTimeSeen) | where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") | table firstTime, user, image_id, count, dest | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Cloud Compute Instance Created With Previously Unseen Instance Type +Find EC2 instances being created with previously unseen instance types. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-09-12 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where instance_type != "unknown" | lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenInstanceType=min(firstTimeSeen) | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") | table firstTime, user, dest, count, instance_type | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Cloud Instance Modified By Previously Unseen User +This search looks for cloud instances being modified by users who have not previously modified them. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-29 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime user command object_id count | `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Cloud Network Access Control List Deleted +Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-09-08 + +
+ View + +#### Search +``` +`cloudtrail` eventName=DeleteNetworkAcl|rename userIdentity.arn as arn | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `cloud_network_access_control_list_deleted_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the `cloud_network_access_control_list_deleted_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible that a user has legitimately deleted a network ACL. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Cloud Provisioning Activity From Previously Unseen City +This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-10-09 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) | lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, City, user, object, command | `cloud_provisioning_activity_from_previously_unseen_city_filter` | `security_content_ctime(firstTime)` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Cloud Provisioning Activity From Previously Unseen Country +This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-10-09 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | table firstTime, src, Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter` | `security_content_ctime(firstTime)` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Cloud Provisioning Activity From Previously Unseen IP Address +This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-08-16 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, user, object_id, command | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` | `security_content_ctime(firstTime)` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Cloud Provisioning Activity From Previously Unseen Region +This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-08-16 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region) | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, Region, user, object, command | `cloud_provisioning_activity_from_previously_unseen_region_filter` | `security_content_ctime(firstTime)` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Common Ransomware Extensions +The search looks for file modifications with extensions commonly used by Ransomware + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1485 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` | `common_ransomware_extensions_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\ +This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ +1. \ +1. **Label:** File Extension, **Field:** file_extension\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1485 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_extensions/windows-sysmon.log + +_version_: 4 +
+ +===== +### Common Ransomware Notes +The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1485 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_notes` | `common_ransomware_notes_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1485 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible that a legitimate file could be created with the same name used by ransomware note files. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/windows-sysmon.log + +_version_: 4 +
+ +===== +### Create Remote Thread into LSASS +Detect remote thread creation into LSASS consistent with credential dumping. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2019-12-06 + +
+ View + +#### Search +``` +`sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `create_remote_thread_into_lsass_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Create local admin accounts using net exe +This search looks for the creation of local administrator accounts using net.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`create_local_admin_accounts_using_net_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators often leverage net.exe to create admin accounts. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### Create or delete windows shares using net exe +This search looks for the creation or deletion of hidden shares using net.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1070.005 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*share* | `create_or_delete_windows_shares_using_net_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1070.005 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. + +#### References +* https://attack.mitre.org/techniques/T1070/005 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+ +===== +### Creation of Shadow Copy +Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2019-12-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Legitimate administrator usage of Vssadmin or Wmic will create false positives. + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Creation of Shadow Copy with wmic and powershell +This search detects the use of wmic and Powershell to create a shadow copy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2019-12-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_with_wmic_and_powershell_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Legtimate administrator usage of wmic to create a shadow copy. + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Creation of lsass Dump with Taskmgr +Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2020-02-03 + +
+ View + +#### Search +``` +`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp | stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `creation_of_lsass_dump_with_taskmgr_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. + +#### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager +* https://attack.mitre.org/techniques/T1003/001/ +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Credential Dumping via Copy Command from Shadow Copy +This search detects credential dumping using copy command from a shadow copy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2019-12-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_copy_command_from_shadow_copy_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | #### Kill Chain Phases * Actions on Objectives @@ -7364,16 +2798,2003 @@ You must be ingesting data that records process activity from your hosts to popu unknown #### References -* https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf #### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Credential Dumping via Symlink to Shadow Copy +This search detects the creation of a symlink to a shadow copy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2019-12-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_symlink_to_shadow_copy_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Credential Extraction indicative of FGDump and CacheDump with s option +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND process_name != null AND parent_process_name != null AND match_regex(parent_process_name, /(?i)System32\\services.exe/)=true AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-s/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* process_name +* parent_process_name +* _time +* process_path +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Credential Extraction indicative of FGDump and CacheDump with v option +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null) | where cmd_line != null AND process_name != null AND process_path != null AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-v/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* process_name +* _time +* process_path +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Credential Extraction indicative of Lazagne command line options +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003, T1555 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)all\s+\-oA\s+\-output/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | +| T1555 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Credential Extraction indicative of use of DSInternals credential conversion modules +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)ConvertFrom-ADManagedPasswordBlob/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-UnicodePassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-KerberosKey/)=true OR match_regex(cmd_line, /(?i)ConvertTo-LMHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-NTHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-OrgIdHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-UnicodePassword/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* process_name +* parent_process_name +* _time +* process_path +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/MichaelGrafnetter/DSInternals + +#### Test Dataset + +_version_: 1 +
+ +===== +### Credential Extraction indicative of use of DSInternals modules +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ADDBBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Get-ADDBKdsRootKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBSchemaAttribute/)=true OR match_regex(cmd_line, /(?i)Get-ADKeyCredential/)=true OR match_regex(cmd_line, /(?i)Get-ADReplAccount/)=true OR match_regex(cmd_line, /(?i)Get-ADReplBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADSIAccount/)=true OR match_regex(cmd_line, /(?i)Get-AzureADUserEx/)=true OR match_regex(cmd_line, /(?i)Get-BootKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaPolicyInformation/)=true OR match_regex(cmd_line, /(?i)Get-SamPasswordPolicy/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* process_name +* parent_process_name +* _time +* process_path +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/MichaelGrafnetter/DSInternals + +#### Test Dataset + +_version_: 1 +
+ +===== +### Credential Extraction indicative of use of Mimikatz modules +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)CRYPTO::Certificates/)=true OR match_regex(cmd_line, /(?i)CRYPTO::keys/)=true OR match_regex(cmd_line, /(?i)kerberos::list/)=true OR match_regex(cmd_line, /(?i)kerberos::tgt/)=true OR match_regex(cmd_line, /(?i)lsadump::sam/)=true OR match_regex(cmd_line, /(?i)lsadump::secrets/)=true OR match_regex(cmd_line, /(?i)lsadump::cache/)=true OR match_regex(cmd_line, /(?i)lsadump::lsa/)=true OR match_regex(cmd_line, /(?i)lsadump::trust/)=true OR match_regex(cmd_line, /(?i)lsadump::backupkeys/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+ +===== +### Credential Extraction indicative of use of PowerSploit modules +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ApplicationHost/)=true OR match_regex(cmd_line, /(?i)Get-CachedGPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-GPPAutologon/)=true OR match_regex(cmd_line, /(?i)Get-GPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-RegistryAutoLogon/)=true OR match_regex(cmd_line, /(?i)Get-SiteListPassword/)=true OR match_regex(cmd_line, /(?i)Get-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Request-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Get-VaultCredential/)=true OR match_regex(cmd_line, /(?i)Invoke-Kerberoast/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Credential Extraction native Microsoft debuggers peek into the kernel +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND parent_process_name != null AND process_name != null AND ( match_regex(parent_process_name, /(?i)ntkd\.exe/)=true OR match_regex(parent_process_name, /(?i)livekd\.exe/)=true ) AND match_regex(process_name, /(?i)conhost\.exe/)=true AND match_regex(cmd_line, /(?i)0xffffffff/)=true AND match_regex(cmd_line, /(?i)\-ForceV1/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* process_name +* parent_process_name +* _time +* dest_device_id +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. + +#### References +* https://medium.com/@clermont1050/covid-19-cyber-infection-c615ead7c29 + +#### Test Dataset + +_version_: 1 +
+ +===== +### Credential Extraction native Microsoft debuggers via z command line option +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) | where cmd_line != null AND process_name != null AND ( match_regex(process_name, /^(?i)ntkd\.exe/)=true OR match_regex(process_name, /^(?i)kd\.exe/)=true ) AND match_regex(cmd_line, /(?i)\-z\s+/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* process_name +* _time +* dest_device_id +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003 +- **Last Updated**: 2020-10-18 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)Get-ADDBAccount/)=true AND match_regex(cmd_line, /(?i)\-dbpath[\s;:\.\|]+/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### DNS Query Length Outliers - MLTK +This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.004 +- **Last Updated**: 2020-01-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +1. \ +1. **Label:** DNS Query Length, **Field:** query_length\ +1. \ +1. **Label:** Number of events, **Field:** count\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.004 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### DNS Query Length With High Standard Deviation +This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048.003 +- **Last Updated**: 2021-01-18 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query | `drop_dm_object_name("DNS")` | eval query_length = len(query) | table query query_length record_type count | eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2) | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048.003 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +It's possible there can be long domain names that are legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log _version_: 3
---- -### Suspicious mshta spawn -The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe. + +===== +### DNS Query Requests Resolved by Unauthorized DNS Servers +This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` | `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.004 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### DNS record changed +The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| inputlookup discovered_dns_records | rename answer as discovered_answer | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.004 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### Deleting Shadow Copies +The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1490 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `deleting_shadow_copies_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1490 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### Detect API activity from users without MFA +This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-05-17 + +
+ View + +#### Search +``` +`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity as user]| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_api_activity_from_users_without_mfa_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ +This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** AWS User ARN, **Field:** userIdentity.arn\ +1. \ +1. **Label:** AWS User Type, **Field:** userIdentity.type\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect ARP Poisoning +By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1200, T1498, T1557.002 +- **Last Updated**: 2020-08-11 + +
+ View + +#### Search +``` +`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_arp_poisoning_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1200 | x | x | +| T1498 | x | x | +| T1557.002 | x | x | + +#### Kill Chain Phases +* Reconnaissance +* Delivery +* Actions on Objectives + +#### Known False Positives +This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely). + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect AWS API Activities From Unapproved Accounts +This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` errorCode=success | rename userName as identity | search NOT [| inputlookup identity_lookup_expanded | fields identity] | search NOT [| inputlookup aws_service_accounts | fields identity] | rename identity as user | stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_aws_api_activities_from_unapproved_accounts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ +This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** First Time, **Field:** firstTime\ +1. \ +1. **Label:** Last Time, **Field:** lastTime\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Detect AWS Console Login by New User +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-28 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)` | inputlookup append=t previously_seen_users_console_logins | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") |where userStatus="First Time Logging into AWS Console" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `detect_aws_console_login_by_new_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Detect AWS Console Login by User from New City +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2020-10-07 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user City | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user City | fields earliestseen user City] | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCity = "New City" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user City userStatus userCity | `detect_aws_console_login_by_user_from_new_city_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Detect AWS Console Login by User from New Country +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2020-10-07 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Country | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Country | fields earliestseen user Country] | eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") | eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCountry = "New Country" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Country userStatus userCountry | `detect_aws_console_login_by_user_from_new_country_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Detect AWS Console Login by User from New Region +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2020-10-07 + +
+ View + +#### Search +``` +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Region | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Region | fields earliestseen user Region] | eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userRegion = "New Region" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Region userStatus userRegion | `detect_aws_console_login_by_user_from_new_region_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + +_version_: 1 +
+ +===== +### Detect Activity Related to Pass the Hash Attacks +This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1550.002 +- **Last Updated**: 2020-10-15 + +
+ View + +#### Search +``` +`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_activity_related_to_pass_the_hash_attacks_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1550.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.002/atomic_red_team/windows-security.log + +_version_: 5 +
+ +===== +### Detect Baron Samedit CVE-2021-3156 +This search detects the heap-based buffer overflow of sudoedit + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1068 +- **Last Updated**: 2021-01-27 + +
+ View + +#### Search +``` +`linux_hosts` | search "sudoedit -s \\" | `detect_baron_samedit_cve_2021_3156_filter` +``` +#### Associated Analytic Story + +#### How To Implement +Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Baron Samedit CVE-2021-3156 Segfault +This search detects the heap-based buffer overflow of sudoedit + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1068 +- **Last Updated**: 2021-01-29 + +
+ View + +#### Search +``` +`linux_hosts` | search sudoedit segfault | stats count min(_time) as firstTime max(_time) as lastTime by host | search count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter` +``` +#### Associated Analytic Story + +#### How To Implement +Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged. This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +If sudoedit is throwing segfaults for other reasons this will pick those up too. + +#### References +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Baron Samedit CVE-2021-3156 via OSQuery +This search detects the heap-based buffer overflow of sudoedit + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1068 +- **Last Updated**: 2021-01-28 + +
+ View + +#### Search +``` +`osquery_process` | search "columns.cmdline"="sudoedit -s \\*" | `detect_baron_samedit_cve_2021_3156_via_osquery_filter` +``` +#### Associated Analytic Story + +#### How To Implement +OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Computer Changed with Anonymous Account +This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1210 +- **Last Updated**: 2020-09-18 + +
+ View + +#### Search +``` +`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 | stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event Logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1210 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None thus far found + +#### References +* https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/ + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Credential Dumping through LSASS access +This search looks for reading lsass memory consistent with credential dumping. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2019-12-03 + +
+ View + +#### Search +``` +`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_credential_dumping_through_lsass_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+ +===== +### Detect DNS requests to Phishing Sites leveraging EvilGinx2 +This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1566.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\ + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1566.003 | x | x | + +#### Kill Chain Phases +* Delivery +* Command and Control + +#### Known False Positives +If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Detect Dump LSASS Memory using comsvcs +This search detects the memory of lsass.exe being dumped for offline credential theft attack. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2020-09-15 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() | eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)) | where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true | eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including Windows command line logging. You can see how we test this with [Event Code 4688](https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4688a) on the [attack_range](https://github.com/splunk/attack_range/blob/develop/ansible/roles/windows_common/tasks/windows-enable-4688-cmd-line-audit.yml). + +#### Required fields +* process_name +* _tenant +* _time +* dest_device_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Excessive Account Lockouts From Endpoint +This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.002 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +(Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\ + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.002 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log + +_version_: 5 +
+ +===== +### Detect Excessive User Account Lockouts +This search detects user accounts that have been locked out a relatively high number of times in a short period. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_user_account_lockouts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.003 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log + +_version_: 3 +
+ +===== +### Detect F5 TMUI RCE CVE-2020-5902 +This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1190 +- **Last Updated**: 2020-08-02 + +
+ View + +#### Search +``` +`f5_bigip_rogue` | regex _raw="(hsqldb;|.*\\.\\.;.*)" | search `detect_f5_tmui_rce_cve_2020_5902_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog. As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it. I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;). + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1190 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/ +* https://support.f5.com/csp/article/K52145254 +* https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/ + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect GCP Storage access from a new IP +This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2020-08-10 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` | multikv | rename sc_status_ as status | rename cs_object_ as bucket_name | rename c_ip_ as remote_ip | rename cs_uri_ as request_uri | rename cs_method_ as operation | search status="\"200\"" | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri | table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri | inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri | outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) | where newIP=1 | eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") | table first_time last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour. It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect HTML Help Renamed +The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.001 +- **Last Updated**: 2021-02-11 + +
+ View + +#### Search +``` +`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_renamed_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed hh.exe may be used. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed. + +#### References +* https://attack.mitre.org/techniques/T1218/001/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect HTML Help Spawn Child Process +The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.001 +- **Last Updated**: 2021-02-11 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_spawn_child_process_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed. + +#### References +* https://attack.mitre.org/techniques/T1218/001/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ +* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 +* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect HTML Help URL in Command Line +The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.001 +- **Last Updated**: 2021-02-11 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_url_in_command_line_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed. + +#### References +* https://attack.mitre.org/techniques/T1218/001/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ +* https://blog.sevagas.com/?Hacking-around-HTA-files +* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 +* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect HTML Help Using InfoTech Storage Handlers +The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.001 +- **Last Updated**: 2021-02-11 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_using_infotech_storage_handlers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed. + +#### References +* https://attack.mitre.org/techniques/T1218/001/ +* https://www.kb.cert.org/vuls/id/851869 +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ +* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 +* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect IPv6 Network Infrastructure Threats +By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1200, T1498, T1557.002 +- **Last Updated**: 2020-10-28 + +
+ View + +#### Search +``` +`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") | eval src_interface=src_int_prefix_long+src_int_suffix | eval dest_interface=dest_int_prefix_long+dest_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface | table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detect_ipv6_network_infrastructure_threats_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1200 | x | x | +| T1498 | x | x | +| T1557.002 | x | x | + +#### Kill Chain Phases +* Reconnaissance +* Delivery +* Actions on Objectives + +#### Known False Positives +None currently known + +#### References +* https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Kerberoasting +This search detects a potential kerberoasting attack via service principal name requests + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1558.003 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() | eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id") | where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" | first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] | where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID | eval start_time=_time, end_time=_time, body="TBD", entities="TBD" | select start_time, end_time, entities, body | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2 + +#### Required fields +* service_name +* _time +* event_code +* ticket_encryption_type +* service_id +* ticket_options + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1558.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Older systems that support kerberos RC4 by default NetApp may generate false positives + +#### References +* Initial ESCU implementation by Jose Hernandez and Patrick Bareiss + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Large Outbound ICMP Packets +This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1095 +- **Last Updated**: 2018-06-01 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1095 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Detect Long DNS TXT Record Response +This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" | `detect_long_dns_txt_record_response_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048.003 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Detect MSHTA Url in Command Line +This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: @@ -7385,7 +4806,7 @@ The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_spawn_filter` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter` ``` #### Associated Analytic Story @@ -7404,19 +4825,406 @@ To successfully implement this search you need to be ingesting information on pr * Exploitation #### Known False Positives -Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. +It is possible legitimate applications may perform this behavior and will need to be filtered. #### References -* https://codewhitesec.blogspot.com/2018/07/lethalhta.html * https://github.com/redcanaryco/AtomicTestHarnesses * https://redcanary.com/blog/introducing-atomictestharnesses/ +* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing #### Test Dataset * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log _version_: 1 ---- + +===== +### Detect Mimikatz Using Loaded Images +This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2019-12-03 + +
+ View + +#### Search +``` +`sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_using_loaded_images_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Other tools can import the same DLLs. These tools should be part of a whitelist. + +#### References +* https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Mimikatz Via PowerShell And EventCode 4703 +This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2019-02-27 + +
+ View + +#### Search +``` +`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_via_powershell_and_eventcode_4703_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Detect New Local Admin account +This search looks for newly created accounts that have been elevated to local administrators. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136.001 +- **Last Updated**: 2020-07-08 + +
+ View + +#### Search +``` +`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) | transaction member_id connected=false maxspan=180m | rename member_id as user | stats count min(_time) as firstTime max(_time) as lastTime by user dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_local_admin_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732 + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives +* Command and Control + +#### Known False Positives +The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + +_version_: 2 +
+ +===== +### Detect New Login Attempts to Routers +The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Legitimate router connections may appear as new connections + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect New Open GCP Storage Buckets +This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2020-08-05 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions | spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action | spath output=user path=data.protoPayload.authenticationInfo.principalEmail | spath output=location path=data.protoPayload.resourceLocation.currentLocations{} | spath output=src path=data.protoPayload.requestMetadata.callerIp | spath output=bucketName path=data.protoPayload.resourceName | spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role | spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member | search (member=allUsers AND action=ADD) | table _time, bucketName, src, user, location, action, role, member | search `detect_new_open_gcp_storage_buckets_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect New Open S3 Buckets over AWS CLI +This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") | rename requestParameters.bucketName AS bucketName | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_over_aws_cli_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + +_version_: 1 +
+ +===== +### Detect New Open S3 buckets +This search looks for CloudTrail events where a user has created an open/public S3 bucket. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex field=_raw "(?{.+})" | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} | search grantees=* | mvexpand grantees | spath input=grantees output=uri path=Grantee.URI | spath input=grantees output=permission path=Permission | search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") | search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") | rename requestParameters.bucketName AS bucketName | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + +_version_: 2 +
+ +===== +### Detect Oulook exe writing a zip file +This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1566.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| rename parent_process_id as outlook_id| join malicious_id type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] | table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path | where file_name != "" | `detect_oulook_exe_writing_a__zip_file_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1566.001 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +It is not uncommon for outlook to write legitimate zip files to the disk. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### Detect Outbound SMB Traffic +This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.002 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(start_time)` | `security_content_ctime(end_time)` | `detect_outbound_smb_traffic_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives +* Command and Control + +#### Known False Positives +It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== ### Detect Pass the Hash This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique. @@ -7464,7 +5272,6975 @@ Legitimate logon activity by authorized NTLM systems may be detected by this sea _version_: 1 ---- + +===== +### Detect Path Interception By Creation Of program exe +The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1574.009 +- **Last Updated**: 2020-07-03 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest index | `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?[^\\\\]*\.(?:exe|bat|com|ps1))" | eval process_name = lower(process_name) | eval service_process = lower(service_process) | where process_name != service_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1574.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References +* https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+ +===== +### Detect Port Security Violation +By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1200, T1498, T1557.002 +- **Last Updated**: 2020-10-28 + +
+ View + +#### Search +``` +`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_port_security_violation_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1200 | x | x | +| T1498 | x | x | +| T1557.002 | x | x | + +#### Kill Chain Phases +* Reconnaissance +* Delivery +* Exploitation +* Actions on Objectives + +#### Known False Positives +This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Prohibited Applications Spawning cmd exe +This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.003 +- **Last Updated**: 2020-11-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] | `detect_prohibited_applications_spawning_cmd_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.003 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/powershell_spawn_cmd/windows-sysmon.log + +_version_: 5 +
+ +===== +### Detect Prohibited Applications Spawning cmd exe +This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. This is a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe` by @bpatel. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1059 +- **Last Updated**: 2020-7-13 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) +| where process_name="cmd.exe" | rex field=parent_process "(?[^\\\\]+)$" | where field0="winword.exe" OR field0="excel.exe" OR field0="outlook.exe" OR field0="powerpnt.exe" OR field0="visio.exe" OR field0="mspub.exe" OR field0="acrobat.exe" OR field0="acrord32.exe" OR field0="chrome.exe" OR field0="iexplore.exe" OR field0="opera.exe" OR field0="firefox.exe" OR field0="java.exe" OR field0="powershell.exe" +| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body="TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting sysmon logs. This search has been modified to process raw sysmon data from attack_range's nxlogs on DSP. + +#### Required fields +* process_name +* parent_process_name +* _time +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect PsExec With accepteula Flag +This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.002 +- **Last Updated**: 2020-11-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+ +===== +### Detect Rare Executables +This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-03-16 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | rex field=user "(?.*)\\\\(?.*)" | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_allow_list`| table process ] | `detect_rare_executables_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_allow_list` searches two lookup files for allowed processes. These consist of `rare_process_allow_list_default.csv` and `rare_process_allow_list_local.csv`. To add your own processes to the allow list, add them to `rare_process_allow_list_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the allow_list value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation +* Command and Control +* Actions on Objectives + +#### Known False Positives +Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_allow_list_local.csv` to filter them out of your search results. + +#### References + +#### Test Dataset + +_version_: 5 +
+ +===== +### Detect Regasm Spawning a Process +The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_spawning_a_process_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ +* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect Regasm with Network Connection +The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-16 + +
+ View + +#### Search +``` +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_network_connection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect Regasm with no Command Line Arguments +The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-12 + +
+ View + +#### Search +``` +`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) | regex CommandLine="(regasm\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_no_command_line_arguments_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect Regsvcs Spawning a Process +The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_spawning_a_process_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect Regsvcs with Network Connection +The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-16 + +
+ View + +#### Search +``` +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_network_connection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect Regsvcs with No Command Line Arguments +The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.009 +- **Last Updated**: 2021-02-12 + +
+ View + +#### Search +``` +`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) | regex CommandLine="(regsvcs\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_no_command_line_arguments_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.009 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +#### References +* https://attack.mitre.org/techniques/T1218/009/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect Regsvr32 Application Control Bypass +Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ +Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.010 +- **Last Updated**: 2021-01-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_regsvr32_application_control_bypass_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.010 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Limited false positives related to third party software registering .DLL's. + +#### References +* https://attack.mitre.org/techniques/T1218/010/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ +* https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect Rogue DHCP Server +By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1200, T1498, T1557 +- **Last Updated**: 2020-08-11 + +
+ View + +#### Search +``` +`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_rogue_dhcp_server_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1200 | x | x | +| T1498 | x | x | +| T1557 | x | x | + +#### Kill Chain Phases +* Reconnaissance +* Delivery +* Actions on Objectives + +#### Known False Positives +This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Rundll32 Application Control Bypass - advpack +The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-04 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___advpack_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://lolbas-project.github.io/lolbas/Libraries/Advpack/ +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect Rundll32 Application Control Bypass - setupapi +The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-04 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___setupapi_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use setupapi triggering a false positive. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://lolbas-project.github.io/lolbas/Libraries/Setupapi/ +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect Rundll32 Application Control Bypass - syssetup +The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-04 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___syssetup_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://lolbas-project.github.io/lolbas/Libraries/Syssetup/ +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect Rundll32 Inline HTA Execution +The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.005 +- **Last Updated**: 2021-01-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_rundll32_inline_hta_execution_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.005 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +#### References +* https://github.com/redcanaryco/AtomicTestHarnesses +* https://redcanary.com/blog/introducing-atomictestharnesses/ +* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect S3 access from a new IP +This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2018-06-28 + +
+ View + +#### Search +``` +`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect SNICat SNI Exfiltration +This search looks for commands that the SNICat tool uses in the TLS SNI field. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1041 +- **Last Updated**: 2020-10-21 + +
+ View + +#### Search +``` +`zeek_ssl` | rex field=server_name "(?(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)" | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1041 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Unknown + +#### References +* https://www.mnemonic.no/blog/introducing-snicat/ +* https://github.com/mnemonic-no/SNIcat +* https://attack.mitre.org/techniques/T1041/ + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Software Download To Network Device +Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1542.005 +- **Last Updated**: 2020-10-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1542.005 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Spike in AWS API Activity +This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_aws_api_activity_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ +This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\ +1. \ +1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives + + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Detect Spike in AWS Security Hub Alerts for EC2 Instance +This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket span=4h _time | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest | eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev | eval threshold_value = 3 | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +None + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json + +_version_: 3 +
+ +===== +### Detect Spike in AWS Security Hub Alerts for User +This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename findings{}.Resources{}.Id as user | bucket span=4h _time | stats count AS alerts by _time user | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev | eval threshold_value = 2 | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +None + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### Detect Spike in Network ACL Activity +This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.007 +- **Last Updated**: 2018-05-21 + +
+ View + +#### Search +``` +`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup network_acl_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_network_acl_activity_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.007 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Spike in S3 Bucket deletion +This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1530 +- **Last Updated**: 2018-11-27 + +
+ View + +#### Search +``` +`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1530 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Spike in Security Group Activity +This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2018-04-18 + +
+ View + +#### Search +``` +`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup security_group_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_security_group_activity_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Spike in blocked Outbound Traffic from your AWS +This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-05-07 + +
+ View + +#### Search +``` +`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives +* Command and Control + +#### Known False Positives +The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Traffic Mirroring +Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1200, T1498, T1020.001 +- **Last Updated**: 2020-10-28 + +
+ View + +#### Search +``` +`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_traffic_mirroring_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1200 | x | x | +| T1498 | x | x | +| T1020.001 | x | x | + +#### Kill Chain Phases +* Delivery +* Actions on Objectives + +#### Known False Positives +This search will return false positives for any legitimate traffic captures by network administrators. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect USB device insertion +The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-11-27 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `detect_usb_device_insertion_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +Legitimate USB activity will also be detected. Please verify and investigate as appropriate. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Unauthorized Assets by MAC address +By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-13 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac | dedup All_Sessions.dest_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` | search NOT [| inputlookup asset_lookup_by_str |rename mac as dest_mac | fields + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Reconnaissance +* Delivery +* Actions on Objectives + +#### Known False Positives +This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Use of cmd exe to Launch Script Interpreters +This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.003 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Some legitimate applications may exhibit this behavior. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/cmd_spawns_cscript/windows-sysmon.log + +_version_: 4 +
+ +===== +### Detect Windows DNS SIGRed via Splunk Stream +This search detects SIGRed via Splunk Stream. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1203 +- **Last Updated**: 2020-07-28 + +
+ View + +#### Search +``` +`stream_dns` | spath "query_type{}" | search "query_type{}" IN (SIG,KEY) | spath protocol_stack | search protocol_stack="ip:tcp:dns" | append [search `stream_tcp` bytes_out>65000] | `detect_windows_dns_sigred_via_splunk_stream_filter` | stats count by flow_id | where count>1 | fields - count +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1203 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Windows DNS SIGRed via Zeek +This search detects SIGRed via Zeek DNS and Zeek Conn data. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1203 +- **Last Updated**: 2020-07-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id | rename DNS.flow_id as flow_id | append [| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id | rename All_Traffic.flow_id as flow_id] | `detect_windows_dns_sigred_via_zeek_filter` | stats count by flow_id | where count>1 | fields - count +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The Network Resolution and Network Traffic datamodels are in use for this search. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1203 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect Zerologon via Zeek +This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1190 +- **Last Updated**: 2020-09-15 + +
+ View + +#### Search +``` +`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) | bin span=5m _time | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip | search opscount=3 authcount>4 passcount>0 | search `detect_zerologon_via_zeek_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json. These three operations are then correlated on the Zeek UID field. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1190 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +unknown + +#### References +* https://www.secura.com/blog/zero-logon +* https://github.com/SecuraBV/CVE-2020-1472 +* https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472 + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect attackers scanning for vulnerable JBoss servers +This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1082 +- **Last Updated**: 2017-09-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1082 | x | x | + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect hosts connecting to dynamic domain providers +Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1189 +- **Last Updated**: 2021-01-14 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ +This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +1. \ +1. **Label:** DNS Answer, **Field:** answer\ +1. \ +1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1189 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log + +_version_: 3 +
+ +===== +### Detect malicious requests to exploit JBoss servers +This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +No known false positives for this detection. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect mshta inline hta execution +The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.005 +- **Last Updated**: 2021-01-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_inline_hta_execution_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.005 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +#### References +* https://github.com/redcanaryco/AtomicTestHarnesses +* https://redcanary.com/blog/introducing-atomictestharnesses/ +* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+ +===== +### Detect mshta renamed +The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.005 +- **Last Updated**: 2021-01-20 + +
+ View + +#### Search +``` +`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `detect_mshta_renamed_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.005 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive. + +#### References +* https://github.com/redcanaryco/AtomicTestHarnesses +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + +_version_: 1 +
+ +===== +### Detect new API calls from user roles +This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2018-04-16 + +
+ View + +#### Search +``` +`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | table eventName userName] |rename userName as user| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | `detect_new_api_calls_from_user_roles_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Detect new user AWS Console Login +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | stats earliest(_time) as firstTime latest(_time) as lastTime by user | inputlookup append=t previously_seen_users_console_logins_cloudtrail | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| where userStatus ="First Time Logging into AWS Console" | `detect_new_user_aws_console_login_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Detect processes used for System Network Configuration Discovery +This search looks for fast execution of processes used for system network configuration discovery on the endpoint. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1016 +- **Last Updated**: 2020-11-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime lastTime dest user process_name process parent_process eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1016 | x | x | + +#### Kill Chain Phases +* Installation +* Command and Control +* Actions on Objectives + +#### Known False Positives +It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/discovery_commands/windows-sysmon.log + +_version_: 2 +
+ +===== +### Detect web traffic to dynamic domain providers +This search looks for web connections to dynamic DNS providers. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic` | `detect_web_traffic_to_dynamic_domain_providers_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ +This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` Deprecated because duplicate. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.001 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Detection of DNS Tunnels +This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048.003 +- **Last Updated**: 2017-09-18 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" | rename "DNS.src" as src "DNS.query" as message | eval length=len(message) | stats sum(length) as length by src | append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" | rename "DNS.src" as src "DNS.answer" as message | eval message=if(message=="unknown","", message) | eval length=len(message) | stats sum(length) as length by src ] | stats sum(length) as length by src | where length > 10000 | `detection_of_dns_tunnels_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048.003 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Detection of tools built by NirSoft +This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1072 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1072 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### Disabling Remote User Account Control +The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1548.002 +- **Last Updated**: 2020-11-18 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1548.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### Dump LSASS via comsvcs DLL +Detect the usage of comsvcs.dll for dumping the lsass process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2020-02-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_comsvcs_dll_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/ +* https://twitter.com/SBousseaden/status/1167417096374050817 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Dump LSASS via procdump +Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\ +During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2021-02-01 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://attack.mitre.org/techniques/T1003/001/ +* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Dump LSASS via procdump Rename +Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\ +During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.001 +- **Last Updated**: 2021-02-01 + +
+ View + +#### Search +``` +`sysmon` OriginalFileName=procdump process_name!=procdump*.exe EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_rename_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://attack.mitre.org/techniques/T1003/001/ +* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### EC2 Instance Modified With Previously Unseen User +This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest | `ec2_instance_modified_with_previously_unseen_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### EC2 Instance Started In Previously Unseen Region +This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1535 +- **Last Updated**: 2018-02-23 + +
+ View + +#### Search +``` +`cloudtrail` earliest=-1h StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | inputlookup append=t previously_seen_aws_regions.csv | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where regionStatus="Instance Started in a New Region" | `ec2_instance_started_in_previously_unseen_region_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1535 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### EC2 Instance Started With Previously Unseen AMI +This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-03-12 + +
+ View + +#### Search +``` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID | table firstTime, lastTime, arn, amiID, dest, instanceType | `ec2_instance_started_with_previously_unseen_ami_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### EC2 Instance Started With Previously Unseen Instance Type +This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-02-07 + +
+ View + +#### Search +``` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_instance_type_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### EC2 Instance Started With Previously Unseen User +This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.004 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Email Attachments With Lots Of Spaces +Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-19 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address "(?.*)@" | `email_attachments_with_lots_of_spaces_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ + **Splunk Phantom Playbook Integration**\ +If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +None at this time + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Email files written outside of the Outlook directory +The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_written_outside_of_the_outlook_directory_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### Email servers sending high volume traffic to hosts +This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.002 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Excessive DNS Failures +This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1071.004 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query"| `drop_dm_object_name("DNS")`| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain| where isnull(domain)| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank| where isnull(rank)| stats sum(count) as count mode(queries) as queries by src| `get_asset(src)`| where count>50 | `excessive_dns_failures_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1071.004 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Execution of File With Spaces Before Extension +This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1036.003 +- **Last Updated**: 2020-11-19 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_spaces_before_extension_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### Execution of File with Multiple Extensions +This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1036.003 +- **Last Updated**: 2020-11-18 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_multiple_extensions_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+ +===== +### Extended Period Without Successful Netbackup Backups +This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-12 + +
+ View + +#### Search +``` +`netbackup` MESSAGE="Disk/Partition backup completed successfully." | stats latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest | `extended_period_without_successful_netbackup_backups_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### File with Samsam Extension +The search looks for file writes with extensions consistent with a SamSam ransomware attack. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-12-14 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml | `file_with_samsam_extension_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation + +#### Known False Positives +Because these extensions are not typically used in normal operations, you should investigate all results. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/samsam_extension/windows-sysmon.log + +_version_: 1 +
+ +===== +### First Time Seen Child Process of Zoom +This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1068 +- **Last Updated**: 2020-05-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest | `drop_dm_object_name(Processes)` | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/zoom_child_process/windows-sysmon.log + +_version_: 1 +
+ +===== +### First Time Seen Running Windows Service +This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1569.002 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`wineventlog_system` EventCode=7036 | rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | where state="running" | lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) | table _time dest service | `first_time_seen_running_windows_service_filter` +``` +#### Associated Analytic Story + +#### How To Implement +While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1569.002 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. + +#### References + +#### Test Dataset + +_version_: 4 +
+ +===== +### First time seen command line argument +This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1059, T1117, T1202 +- **Last Updated**: 2021-2-1 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), cmd_line_norm=lower(cmd_line), cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/, "GUID"), cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/, "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N") | where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true | select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id | first_time_event input_columns=["cmd_line_norm"] | where first_time_cmd_line_norm | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be populating the endpoint data model for SSA and specifically the process_name and the process fields + +#### Required fields +* process_name +* _time +* dest_device_id +* dest_user_id +* process + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059 | x | x | +| T1117 | x | x | +| T1202 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### First time seen command line argument +This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001, T1059.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest| `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table process] | `first_time_seen_command_line_argument_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | +| T1059.003 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name + +#### References + +#### Test Dataset + +_version_: 5 +
+ +===== +### GCP Detect accounts with high risk roles by project +This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-10-09 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}.role=roles/owner OR roles/editor OR roles/iam.serviceAccountUser OR roles/iam.serviceAccountAdmin OR roles/iam.serviceAccountTokenCreator OR roles/dataflow.developer OR roles/dataflow.admin OR roles/composer.admin OR roles/dataproc.admin OR roles/dataproc.editor | table data.resource.type data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.authorizationInfo{}.resource data.protoPayload.response.bindings{}.role data.protoPayload.response.bindings{}.members{} | `gcp_detect_accounts_with_high_risk_roles_by_project_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Accounts with high risk roles should be reduced to the minimum number needed, however specific tasks and setups may be simply expected behavior within organization + +#### References +* https://github.com/dxa4481/gcploit +* https://www.youtube.com/watch?v=Ml09R38jpok +* https://cloud.google.com/iam/docs/understanding-roles + +#### Test Dataset + +_version_: 1 +
+ +===== +### GCP Detect gcploit framework +This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-10-08 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s | table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent | `gcp_detect_gcploit_framework_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects + +#### References +* https://github.com/dxa4481/gcploit +* https://www.youtube.com/watch?v=Ml09R38jpok + +#### Test Dataset + +_version_: 1 +
+ +===== +### GCP Detect high risk permissions by resource and account +This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-10-09 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.permission=iam.serviceAccounts.getaccesstoken OR iam.serviceAccounts.setIamPolicy OR iam.serviceAccounts.actas OR dataflow.jobs.create OR composer.environments.create OR dataproc.clusters.create |table data.protoPayload.requestMetadata.callerIp data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.response.bindings{}.members{} data.resource.labels.project_id | `gcp_detect_high_risk_permissions_by_resource_and_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +High risk permissions are part of any GCP environment, however it is important to track resource and accounts usage, this search may produce false positives. + +#### References +* https://github.com/dxa4481/gcploit +* https://www.youtube.com/watch?v=Ml09R38jpok +* https://cloud.google.com/iam/docs/permissions-reference + +#### Test Dataset + +_version_: 1 +
+ +===== +### GCP GCR container uploaded +This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1525 +- **Last Updated**: 2020-02-20 + +
+ View + +#### Search +``` +|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path | `drop_dm_object_name("Storage")` | `gcp_gcr_container_uploaded_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1525 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### GCP Kubernetes cluster pod scan detection +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1526 +- **Last Updated**: 2020-07-17 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` category=kube-audit |spath input=properties.log |search responseStatus.code=401 |table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod | `gcp_kubernetes_cluster_pod_scan_detection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1526 | x | x | + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### GCP Kubernetes cluster scan detection +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1526 +- **Last Updated**: 2020-04-15 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `gcp_kubernetes_cluster_scan_detection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1526 | x | x | + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Hiding Files And Directories With Attrib exe +Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1222.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `hiding_files_and_directories_with_attrib_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1222.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Some applications and users may legitimately use attrib.exe to interact with the files. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### High Number of Login Failures from a single source +This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1110.001 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory | stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app | search accounts_locked >= 5| `high_number_of_login_failures_from_a_single_source_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1110.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Hosts receiving high volume of network traffic from email server +This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.002 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Identify New User Accounts +This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.002 +- **Last Updated**: 2017-09-12 + +
+ View + +#### Search +``` +| from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity empStatus endDate startDate | `identify_new_user_accounts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.002 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Illegal Access To User Content via PowerSploit modules +This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1021, T1113, T1123, T1563 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-HttpStatus/)=true OR match_regex(cmd_line, /(?i)Get-Keystrokes/)=true OR match_regex(cmd_line, /(?i)Get-MicrophoneAudio/)=true OR match_regex(cmd_line, /(?i)Get-NetRDPSession/)=true OR match_regex(cmd_line, /(?i)Get-TimedScreenshot/)=true OR match_regex(cmd_line, /(?i)Get-WebConfig/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021 | x | x | +| T1113 | x | x | +| T1123 | x | x | +| T1563 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Illegal Account Creation via PowerSploit modules +This detection identifies access to PowerSploit modules that create accounts illegaly. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1585 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)New-DomainUser/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1585 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Illegal Deletion of Logs via Mimikatz modules +This detection identifies access to PowerSploit modules that delete event logs. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1070 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)event::drop/)=true OR match_regex(cmd_line, /(?i)event::clear/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1070 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+ +===== +### Illegal Enabling or Disabling of Accounts via DSInternals modules +This detection identifies use of DSInternals modules that enable or disable accounts illegaly. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1078, T1098 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Disable-ADDBAccount/)=true OR match_regex(cmd_line, /(?i)Enable-ADDBAccount/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | +| T1098 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/MichaelGrafnetter/DSInternals + +#### Test Dataset + +_version_: 1 +
+ +===== +### Illegal Management of Active Directory Elements and Policies via DSInternals modules +This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1098, T1207, T1484 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Remove-ADDBObject/)=true OR match_regex(cmd_line, /(?i)Set-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Set-ADDBPrimaryGroup/)=true OR match_regex(cmd_line, /(?i)Set-LsaPolicyInformation/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1098 | x | x | +| T1207 | x | x | +| T1484 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/MichaelGrafnetter/DSInternals + +#### Test Dataset + +_version_: 1 +
+ +===== +### Illegal Management of Computers and Active Directory Elements via PowerSploit modules +This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1098, T1207, T1484 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainObject/)=true OR match_regex(cmd_line, /(?i)Set-ADObject/)=true OR match_regex(cmd_line, /(?i)Set-DomainObjectOwner/)=true OR match_regex(cmd_line, /(?i)Set-MasterBootRecord/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1098 | x | x | +| T1207 | x | x | +| T1484 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Illegal Privilege Elevation and Persistence via PowerSploit modules +This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1053, T1134, T1548 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Add-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Enable-Privilege/)=true OR match_regex(cmd_line, /(?i)New-ElevatedPersistenceOption/)=true OR match_regex(cmd_line, /(?i)New-UserPersistenceOption/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053 | x | x | +| T1134 | x | x | +| T1548 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Illegal Privilege Elevation via Mimikatz modules +This detection identifies use of Mimikatz modules for illegal privilege elevation. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1134, T1548 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)privilege::debug/)=true OR match_regex(cmd_line, /(?i)token::elevate/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1134 | x | x | +| T1548 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+ +===== +### Illegal Service and Process Control via Mimikatz modules +This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1055, T1106, T1569 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::start/)=true OR match_regex(cmd_line, /(?i)service::\+/)=true OR match_regex(cmd_line, /(?i)service::\-/)=true OR match_regex(cmd_line, /(?i)service::start/)=true OR match_regex(cmd_line, /(?i)service::stop/)=true OR match_regex(cmd_line, /(?i)service::suspend/)=true OR match_regex(cmd_line, /(?i)misc::memssp/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1055 | x | x | +| T1106 | x | x | +| T1569 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+ +===== +### Illegal Service and Process Control via PowerSploit modules +This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1055, T1106, T1569 +- **Last Updated**: 2020-11-09 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Install-SSP/)=true OR match_regex(cmd_line, /(?i)Set-CriticalProcess/)=true OR match_regex(cmd_line, /(?i)Install-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Restore-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Write-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Set-ServiceBinaryPath/)=true OR match_regex(cmd_line, /(?i)Invoke-ReflectivePEInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-DllInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-ServiceAbuse/)=true OR match_regex(cmd_line, /(?i)Invoke-Shellcode/)=true OR match_regex(cmd_line, /(?i)Invoke-WScriptUACBypass/)=true OR match_regex(cmd_line, /(?i)Invoke-WmiCommand/)=true OR match_regex(cmd_line, /(?i)Write-HijackDll/)=true OR match_regex(cmd_line, /(?i)Add-ServiceDacl/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* dest_device_id +* dest_user_id +* process +* _time + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1055 | x | x | +| T1106 | x | x | +| T1569 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kerberoasting spn request with RC4 encryption +This search detects a potential kerberoasting attack via service principal name requests + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1558.003 +- **Last Updated**: 2020-10-16 + +
+ View + +#### Search +``` +`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 | stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kerberoasting_spn_request_with_rc4_encryption_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1558.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Older systems that support kerberos RC4 by default NetApp may generate false positives + +#### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md +* https://www.trimarcsecurity.com/post/trimarcresearch-detecting-kerberoasting-activity + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-security.log + +_version_: 3 +
+ +===== +### Kubernetes AWS detect RBAC authorization by account +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason | stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_aws_detect_rbac_authorization_by_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes AWS detect most active service accounts by pod +This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts objectRef.resource=pods | table sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision | top sourceIPs{} user.username verb annotations.authorization.k8s.io/decision |`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes AWS detect sensitive role access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_aws_detect_sensitive_role_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes AWS detect service accounts forbidden failure access +This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure | table sourceIPs{} user.username userAgent verb responseStatus.status requestURI | `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes AWS detect suspicious kubectl calls +This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous | table src_ip src_user verb userAgent requestURI | stats count by src_ip src_user verb userAgent requestURI |`kubernetes_aws_detect_suspicious_kubectl_calls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes Azure detect RBAC authorization by account +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-26 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | search annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason |stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_azure_detect_rbac_authorization_by_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes Azure detect most active service accounts by pod namespace +This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-26 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace | top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes Azure detect sensitive object access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_object_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes Azure detect sensitive role access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=clusterroles OR clusterrolebindings | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes Azure detect service accounts forbidden failure access +This search provides information on Kubernetes service accounts with failure or forbidden access status + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes Azure detect suspicious kubectl calls +This search provides information on rare Kubectl calls with IP, verb namespace and object access context + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-26 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration | search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 | table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI | rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI |`kubernetes_azure_detect_suspicious_kubectl_calls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes Azure pod scan fingerprint +This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod |`kubernetes_azure_pod_scan_fingerprint_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes Azure scan fingerprint +This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1526 +- **Last Updated**: 2020-05-19 + +
+ View + +#### Search +``` +`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason |`kubernetes_azure_scan_fingerprint_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1526 | x | x | + +#### Kill Chain Phases +* Reconnaissance + +#### Known False Positives +Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes GCP detect RBAC authorizations by account +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole | table src_ip src_user data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason | rare src_user data.labels.authorization.k8s.io/reason |`kubernetes_gcp_detect_rbac_authorizations_by_account_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on for GCP. This search works with pubsub messaging service logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes GCP detect most active service accounts by pod +This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-07-10 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.request.spec.group{}=system:serviceaccounts | table src_ip src_user http_user_agent data.protoPayload.request.spec.nonResourceAttributes.verb data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource | top src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource |`kubernetes_gcp_detect_most_active_service_accounts_by_pod_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk GCP add on. This search works with pubsub messaging service logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes GCP detect sensitive object access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.resource=configmaps OR secrets | table data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name data.protoPayload.request.metadata.namespace data.labels.authorization.k8s.io/decision | dedup data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name |`kubernetes_gcp_detect_sensitive_object_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk add on for GCP . This search works with pubsub messaging service logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes GCP detect sensitive role access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole dest=apis/rbac.authorization.k8s.io/v1 src_ip!=::1 | table src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason | dedup src_ip src_user |`kubernetes_gcp_detect_sensitive_role_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk add on for GCP. This search works with pubsub messaging servicelogs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sensitive role resource access is necessary for cluster operation, however source IP, user agent, decision and reason may indicate possible malicious use. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes GCP detect service accounts forbidden failure access +This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` system:serviceaccounts data.protoPayload.response.status.allowed!=* | table src_ip src_user http_user_agent data.protoPayload.response.spec.resourceAttributes.namespace data.resource.labels.cluster_name data.protoPayload.response.spec.resourceAttributes.verb data.protoPayload.request.status.allowed data.protoPayload.response.status.reason data.labels.authorization.k8s.io/decision | dedup src_ip src_user | `kubernetes_gcp_detect_service_accounts_forbidden_failure_access_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk add on for GCP. This search works with pubsub messaging service logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Kubernetes GCP detect suspicious kubectl calls +This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous | table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path |dedup src_ip src_user |`kubernetes_gcp_detect_suspicious_kubectl_calls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk add on for GCP. This search works with pubsub messaging logs. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Large Volume of DNS ANY Queries +The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1498.002 +- **Last Updated**: 2017-09-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1498.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### MacOS - Re-opened Applications +This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-02-07 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `macos___re_opened_applications_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation +* Command and Control + +#### Known False Positives +At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Malicious PowerShell Process - Connect To Internet With Hidden Window +This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001 +- **Last Updated**: 2020-11-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +Legitimate process can have this combination of command-line options, but it's not common. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/hidden_powershell/windows-sysmon.log + +_version_: 5 +
+ +===== +### Malicious PowerShell Process - Encoded Command +This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1027 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `malicious_powershell_process___encoded_command_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1027 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +System administrators may use this option, but it's not common. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### Malicious PowerShell Process - Execution Policy Bypass +This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/windows-sysmon.log + +_version_: 4 +
+ +===== +### Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments +This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001 +- **Last Updated**: 2021-01-19 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* | `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +Legitimate process can have this combination of command-line options, but it's not common. + +#### References + +#### Test Dataset + +_version_: 6 +
+ +===== +### Malicious PowerShell Process With Obfuscation Techniques +This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.001 +- **Last Updated**: 2021-01-19 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) | `malicious_powershell_process_with_obfuscation_techniques_filter` | search num_obfuscation > 10 +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.001 | x | x | + +#### Kill Chain Phases +* Command and Control +* Actions on Objectives + +#### Known False Positives +These characters might be legitimately on the command-line, but it is not common. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/obfuscated_powershell/windows-sysmon.log + +_version_: 4 +
+ +===== +### Monitor DNS For Brand Abuse +This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)`| `brand_abuse_dns` | `monitor_dns_for_brand_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery +* Actions on Objectives + +#### Known False Positives +None at this time + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Monitor Email For Brand Abuse +This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-01-05 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +None at this time + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Monitor Registry Keys for Print Monitors +This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1547.010 +- **Last Updated**: 2020-11-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `monitor_registry_keys_for_print_monitors_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1547.010 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +You will encounter noise from legitimate print-monitor registry entries. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.010/atomic_red_team/windows-sysmon.log + +_version_: 2 +
+ +===== +### Monitor Web Traffic For Brand Abuse +This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +None at this time + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### More than usual number of LOLBAS applications in short time period +Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1059, T1053 +- **Last Updated**: 2020-08-25 + +
+ View + +#### Search +``` + | from read_ssa_enriched_events() | eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" | stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) | eval lolbas_counter=lolbas_counter*1.0 | rename window_end as timestamp | adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L | where label AND quantile>0.99 | eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +Collect endpoint data such as sysmon or 4688 events. + +#### Required fields +* dest_device_id +* _time +* process_name + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059 | x | x | +| T1053 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Some administrative tasks may involve multiple use of LOLBAS applications in a short period of time. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. + + +#### References +* https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries + +#### Test Dataset + +_version_: 1 +
+ +===== +### Multiple Okta Users With Invalid Credentials From The Same IP +This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`okta` outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search distinct_users > 5| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.001 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### NLTest Domain Trust Discovery +This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1482 +- **Last Updated**: 2021-01-25 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `nltest_domain_trust_discovery_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1482 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Administrators may use nltest for troubleshooting purposes, otherwise, rarely used. + +#### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md +* https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104 +* https://attack.mitre.org/techniques/T1482/ +* https://www.owasp.org/images/4/4b/Red_Team_Operating_in_a_Modern_Environment.pdf +* https://ss64.com/nt/nltest.html +* https://redcanary.com/threat-detection-report/techniques/domain-trust-discovery/ +* https://thedfirreport.com/2020/10/08/ryuks-return/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### New container uploaded to AWS ECR +This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1525 +- **Last Updated**: 2020-02-20 + +
+ View + +#### Search +``` +| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")` | `new_container_uploaded_to_aws_ecr_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1525 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Uploading container is a normal behavior from developers or users with access to container registry. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### No Windows Updates in a time frame +This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-15 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) | `security_content_ctime(lastTime)` | search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Ntdsutil export ntds +Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \ +ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ +This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1003.003 +- **Last Updated**: 2021-01-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `ntdsutil_export_ntds_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives. + +#### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil +* https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11) +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf +* https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### O365 Add App Role Assignment Grant User +This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136.003 +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." | stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_add_app_role_assignment_grant_user_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136.003 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. + +#### References +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json + +_version_: 1 +
+ +===== +### O365 Added Service Principal +This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136.003 +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." | stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_added_service_principal_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136.003 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. + +#### References +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html +* https://www.sygnia.co/golden-saml-advisory + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json + +_version_: 1 +
+ +===== +### O365 Bypass MFA via Trusted IP +This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.007 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy | rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") | mvexpand ip_addresses_new_added | where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) |stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account status user_id action | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `o365_bypass_mfa_via_trusted_ip_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.007 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration. + +#### References +* https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf +* https://attack.mitre.org/techniques/T1562/007/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json + +_version_: 1 +
+ +===== +### O365 Disable MFA +This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1556 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Operation="Disable Strong Authentication." | stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_disable_mfa_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1556 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +Unless it is a special case, it is uncommon to disable MFA or Strong Authentication + +#### References +* https://attack.mitre.org/techniques/T1556/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json + +_version_: 1 +
+ +===== +### O365 Excessive Authentication Failures Alert +This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1110 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed | stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user | where count > 10 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_excessive_authentication_failures_alert_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1110 | x | x | + +#### Kill Chain Phases +* Not Applicable + +#### Known False Positives +The threshold for alert is above 10 attempts and this should reduce the number of false positives. + +#### References +* https://attack.mitre.org/techniques/T1110/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json + +_version_: 1 +
+ +===== +### O365 Excessive SSO logon errors +This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1556 +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired | stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId | where count > 5 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_excessive_sso_logon_errors_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1556 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack. + +#### References +* https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json + +_version_: 1 +
+ +===== +### O365 New Federated Domain Added +This search detects the addition of a new Federated domain. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136.003 +- **Last Updated**: 2021-01-26 + +
+ View + +#### Search +``` +`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" | stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_federated_domain_added_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136.003 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider. + +#### References +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html +* https://www.sygnia.co/golden-saml-advisory +* https://o365blog.com/post/aadbackdoor/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json + +_version_: 1 +
+ +===== +### O365 PST export alert +This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" | stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_pst_export_alert_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114 | x | x | + +#### Kill Chain Phases +* Actions on Objective + +#### Known False Positives +PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored. + +#### References +* https://attack.mitre.org/techniques/T1114/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json + +_version_: 1 +
+ +===== +### O365 Suspicious Admin Email Forwarding +This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.003 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_admin_email_forwarding_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json + +_version_: 1 +
+ +===== +### O365 Suspicious Rights Delegation +This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.002 +- **Last Updated**: 2020-12-15 + +
+ View + +#### Search +``` +`o365_management_activity` Operation=Add-MailboxPermission | spath input=Parameters | rename User AS src_user, Identity AS dest_user | search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf | stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_rights_delegation_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Service Accounts + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json + +_version_: 1 +
+ +===== +### O365 Suspicious User Email Forwarding +This search detects when multiple user configured a forwarding rule to the same destination. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1114.003 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` +`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingSmtpAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_user_email_forwarding_filter` +``` +#### Associated Analytic Story + +#### How To Implement + + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1114.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +unknown + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json + +_version_: 1 +
+ +===== +### Okta Account Lockout Events +Detect Okta user lockout events + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`okta` displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, country, state, city, src_ip | `okta_account_lockout_events_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.001 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Okta Failed SSO Attempts +Detect failed Okta SSO events + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`okta` displayMessage="User attempted unauthorized access to app" | stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_failed_sso_attempts_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.001 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +There may be a faulty config preventing legitmate users from accessing apps they should have access to. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Okta User Logins From Multiple Cities +This search detects logins from the same user from different cities in a 24 hour period. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `okta_user_logins_from_multiple_cities_filter` | search locations > 1 +``` +#### Associated Analytic Story + +#### How To Implement +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078.001 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Open Redirect in Splunk Web +This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-09-19 + +
+ View + +#### Search +``` +index=_internal sourcetype=splunk_web_access return_to="/%09/*" | `open_redirect_in_splunk_web_filter` +``` +#### Associated Analytic Story + +#### How To Implement +No extra steps needed to implement this search. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Osquery pack - ColdRoot detection +This search looks for ColdRoot events from the osx-attacks osquery pack. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2019-01-29 + +
+ View + +#### Search +``` +| from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path | bucket _time span=30s | stats count(path) by _time, host, user, path | `osquery_pack___coldroot_detection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation +* Command and Control + +#### Known False Positives +There are no known false positives. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Overwriting Accessibility Binaries +Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1546.008 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1546.008 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.008/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### Phishing Email Detection by Machine Learning Method - SSA +Malicious mails can conduct phishing that induces readers to open attachment, click links or trigger third party service. This detect uses Natural Language Processing (NLP) approach to analyze an email message's content (Sender, Subject and Body) and judge whether it is a phishing email. The detection adopts a deep learning (neural network) model that employs character level embeddings plus LSTM layers to perform classification. The model is pre-trained and then published as ONNX format. Current sample model is trained using the dataset published at https://github.com/splunk/attack_data/tree/master/datasets/T1566_Phishing_Email/splunk_train.json User are expected to re-train the model by combining with their own training data for better accuracy using the provided model file (SMLE notebook). DSP pipeline then processes the email message and passes it as an event to Apply ML Models function, which returns the probability of a phishing email. Current implementation assumes the email is fed to DSP in JSON format contains at least email's sender, subject and its message body, including reply content, if any. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1566 +- **Last Updated**: 2020-08-25 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() | eval eventLine=concat(ucast(map_get(input_event, "From"), "string", " "), " ", ucast(map_get(input_event, "Subject"), "string", " "), " ", ucast(map_get(input_event, "Content"), "string", " "), " "), _time=map_get(input_event, "_time") | where eventLine IS NOT NULL | eval mapC={" ": 32, "!": 33, "\"": 34, "#": 35, "$": 36, "%": 37, "&": 38, "`": 39, "(": 40, ")": 41, "*": 42, "+": 43, ",": 44, "-": 45, ".": 46, "/": 47, "0": 48, "1": 49, "2": 50, "3": 51, "4": 52, "5": 53, "6": 54, "7": 55, "8": 56, "9": 57, ":": 58, ";": 59, "<": 60, "=": 61, ">": 62, "?": 63, "@": 64, "A": 65, "B": 66, "C": 67, "D": 68, "E": 69, "F": 70, "G": 71, "H": 72, "I": 73, "J": 74, "K": 75, "L": 76, "M": 77, "N": 78, "O": 79, "P": 80, "Q": 81, "R": 82, "S": 83, "T": 84, "U": 85, "V": 86, "W": 87, "X": 88, "Y": 89, "Z": 90, "[": 91, "\\": 92, "]": 93, "^": 94, "_": 95, "`": 96, "a": 97, "b": 98, "c": 99, "d": 100, "e": 101, "f": 102, "g": 103, "h": 104, "i": 105, "j": 106, "k": 107, "l": 108, "m": 109, "n": 110, "o": 111, "p": 112, "q": 113, "r": 114, "s": 115, "t": 116, "u": 117, "v": 118, "w": 119, "x": 120, "y": 121, "z": 122, "{": 123, "|": 124, "}": 125, "~": 126}, ml_in = for_each(iterator(mvrange(1,129), "i"), cast(map_get(mapC, substr(eventLine, i, 1)), "float") ) | apply_model connection_id="YOUR_S3_ONNX_CONNECTOR_ID" name="phishing_email_v8" path="s3://smle-experiments/models/phishing_email" | eval probability = mvindex(ml_out, 0) | where probability > 0.5 | eval start_time=_time, end_time=_time, entities="TBD", body="TBD" | select probability, body, entities, start_time, end_time | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +Events are fed to DSP contains at least email's sender, subject and its message body. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1566 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Because of imbalance of anomaly data in training, the model will less likely report false positive. Instead, the model is more prone to false negative. Current best recall score is ~85% + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Probing Access with Stolen Credentials via PowerSploit modules +This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1078, T1098 +- **Last Updated**: 2020-11-04 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-AdminAccess/)=true OR match_regex(cmd_line, /(?i)Invoke-CheckLocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Test-ServiceDaclPermission/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_user_id +* dest_device_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | +| T1098 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Process Creating LNK file in Suspicious Location +This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1566.002 +- **Last Updated**: 2021-01-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_id as lnk_pid | join lnk_pid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` | rename parent_process_id as lnk_pid | fields _time lnk_pid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `process_creating_lnk_file_in_suspicious_location_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1566.002 | x | x | + +#### Kill Chain Phases +* Installation +* Actions on Objectives + +#### Known False Positives +This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories. + +#### References +* https://attack.mitre.org/techniques/T1566/001/ +* https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log + +_version_: 4 +
+ +===== +### Process Execution via WMI +This search looks for processes launched via WMI. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1047 +- **Last Updated**: 2020-03-16 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `process_execution_via_wmi_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1047 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, administrators may use wmi to execute commands for legitimate purposes. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+ +===== +### Processes Tapping Keyboard Events +This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2019-01-25 + +
+ View + +#### Search +``` +| from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id| dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Processes created by netsh +This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.004 +- **Last Updated**: 2020-11-23 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `processes_created_by_netsh_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft. + +#### References + +#### Test Dataset + +_version_: 5 +
+ +===== +### Processes launching netsh +This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.004 +- **Last Updated**: 2020-07-10 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`processes_launching_netsh_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.004 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+ +===== +### Prohibited Network Traffic Allowed +This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048 | x | x | + +#### Kill Chain Phases +* Delivery +* Command and Control + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Prohibited Software On Endpoint +This search looks for applications on the endpoint that you have marked as prohibited. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2019-10-11 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `prohibited_softwares` | `prohibited_software_on_endpoint_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Installation +* Command and Control +* Actions on Objectives + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Protocol or Port Mismatch +This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1048.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter` +``` +#### Associated Analytic Story + +#### How To Implement +Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1048.003 | x | x | + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +None identified + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Protocols passing authentication in cleartext +This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-11-04 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocols_passing_authentication_in_cleartext_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Reconnaissance +* Actions on Objectives + +#### Known False Positives +Some networks may use kerberized FTP or telnet servers, however, this is rare. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Rare Parent-Child Process Relationship +An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1203, T1059, T1053, T1072 +- **Last Updated**: 2020-08-13 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) | where parent_process_name!=null | select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id | conditional_anomaly conditional="parent_process_name" target="process_name" | rename output as input | where input < 1 | adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L | where label AND quantile<0.1 AND (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe") +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +Collect endpoint data such as sysmon or 4688 events. + +#### Required fields +* process_name +* parent_process_name +* _time +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1203 | x | x | +| T1059 | x | x | +| T1053 | x | x | +| T1072 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. + + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules +This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1078, T1087, T1484 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Invoke-EnumerateLocalAdmin/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Invoke-EventHunter/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserLocation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserHunter/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignGroupMember/)=true OR match_regex(cmd_line, /(?i)Find-ForeignGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignUser/)=true OR match_regex(cmd_line, /(?i)Find-ForeignUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPO/)=true OR match_regex(cmd_line, /(?i)Get-NetGPO/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOComputerLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOComputerAdmin/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGPOGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOUserLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOLocation/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-DomainManagedSecurityGroup/)=true OR match_regex(cmd_line, /(?i)Find-ManagedSecurityGroups/)=true OR match_regex(cmd_line, /(?i)Get-DomainOU/)=true OR match_regex(cmd_line, /(?i)Get-NetOU/)=true OR match_regex(cmd_line, /(?i)Get-DomainUser/)=true OR match_regex(cmd_line, /(?i)Get-NetUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Get-UserEvent/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetLoggedon/)=true OR match_regex(cmd_line, /(?i)Get-RegLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegLastLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-LastLoggedOn/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | +| T1087 | x | x | +| T1484 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance and Access to Accounts and Groups via Mimikatz modules +This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1078, T1087, T1484 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::user/)=true OR match_regex(cmd_line, /(?i)net::group/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | +| T1087 | x | x | +| T1484 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules +This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1199, T1482, T1590, T1591, T1595 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainSID/)=true OR match_regex(cmd_line, /(?i)Get-DomainSite/)=true OR match_regex(cmd_line, /(?i)Get-NetSite/)=true OR match_regex(cmd_line, /(?i)Get-DomainSubnet/)=true OR match_regex(cmd_line, /(?i)Get-NetSubnet/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrustMapping/)=true OR match_regex(cmd_line, /(?i)Invoke-MapDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-Forest/)=true OR match_regex(cmd_line, /(?i)Get-NetForest/)=true OR match_regex(cmd_line, /(?i)Get-ForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-NetForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-ForestGlobalCatalog/)=true OR match_regex(cmd_line, /(?i)Get-NetForestCatalog/)=true OR match_regex(cmd_line, /(?i)Get-ForestTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetForestTrust/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1199 | x | x | +| T1482 | x | x | +| T1590 | x | x | +| T1591 | x | x | +| T1595 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance and Access to Computers and Domains via PowerSploit modules +This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1592, T1590, T1087 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ComputerDetail/)=true OR match_regex(cmd_line, /(?i)Get-Domain/)=true OR match_regex(cmd_line, /(?i)Get-NetDomain/)=true OR match_regex(cmd_line, /(?i)Get-DomainComputer/)=true OR match_regex(cmd_line, /(?i)Get-NetComputer/)=true OR match_regex(cmd_line, /(?i)Get-DomainController/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainController/)=true OR match_regex(cmd_line, /(?i)Get-DomainFileServer/)=true OR match_regex(cmd_line, /(?i)Get-NetFileServer/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1592 | x | x | +| T1590 | x | x | +| T1087 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance and Access to Computers via Mimikatz modules +This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1592 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::ServerInfo/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1592 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+ +===== ### Reconnaissance and Access to Operating System Elements via PowerSploit modules This detection identifies access to PowerSploit modules that discover and access operating system elements, such as processes, services, registry locations, security packages and files. @@ -7519,7 +12295,558 @@ None identified. _version_: 1 ---- + +===== +### Reconnaissance and Access to Processes and Services via Mimikatz modules +This detection identifies use of Mimikatz modules for discovery and access to services and processes. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1007, T1046, T1057 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::list/)=true OR match_regex(cmd_line, /(?i)service::list/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1007 | x | x | +| T1046 | x | x | +| T1057 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance and Access to Shared Resources via Mimikatz modules +This detection identifies use of Mimikatz modules for discovery and access to network shares. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1021.002, T1135, T1039 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::share/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | +| T1135 | x | x | +| T1039 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance and Access to Shared Resources via PowerSploit modules +This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1021.002, T1135, T1039 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainShare/)=true OR match_regex(cmd_line, /(?i)Invoke-ShareFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainShareFile/)=true OR match_regex(cmd_line, /(?i)Invoke-FileFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingFile/)=true OR match_regex(cmd_line, /(?i)Get-DomainDFSShare/)=true OR match_regex(cmd_line, /(?i)Get-DFSshare/)=true OR match_regex(cmd_line, /(?i)Get-NetShare/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | +| T1135 | x | x | +| T1039 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance of Access and Persistence Opportunities via PowerSploit modules +This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1053, T1068, T1078, T1543, T1547, T1574 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-LocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainAcl/)=true OR match_regex(cmd_line, /(?i)Invoke-ACLScanner/)=true OR match_regex(cmd_line, /(?i)Find-PathDLLHijack/)=true OR match_regex(cmd_line, /(?i)Find-ProcessDLLHijack/)=true OR match_regex(cmd_line, /(?i)Get-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-DomainPolicy/)=true OR match_regex(cmd_line, /(?i)Get-ModifiablePath/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableRegistryAutoRun/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableScheduledTaskFile/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableService/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableServiceFile/)=true OR match_regex(cmd_line, /(?i)Get-PathAcl/)=true OR match_regex(cmd_line, /(?i)Get-UnattendedInstallFile/)=true OR match_regex(cmd_line, /(?i)Get-UnquotedService/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053 | x | x | +| T1068 | x | x | +| T1078 | x | x | +| T1543 | x | x | +| T1547 | x | x | +| T1574 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance of Connectivity via PowerSploit modules +This detection identifies access to PowerSploit modules for reconnaissance of connectivity. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1021.002, T1135, T1039 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainDNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DomainDNSZone/)=true OR match_regex(cmd_line, /(?i)Get-DNSZone/)=true OR match_regex(cmd_line, /(?i)Invoke-ReverseDnsLookup/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegCachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-CachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegProxy/)=true OR match_regex(cmd_line, /(?i)Get-Proxy/)=true OR match_regex(cmd_line, /(?i)Invoke-Portscan/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | +| T1135 | x | x | +| T1039 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance of Credential Stores and Services via Mimikatz modules +This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1589.001, T1590.001, T1590.003, T1068, T1078, T1098 +- **Last Updated**: 2020-11-03 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)crypto::capi/)=true OR match_regex(cmd_line, /(?i)crypto::cng/)=true OR match_regex(cmd_line, /(?i)crypto::providers/)=true OR match_regex(cmd_line, /(?i)crypto::stores/)=true OR match_regex(cmd_line, /(?i)crypto::sc/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1589.001 | x | x | +| T1590.001 | x | x | +| T1590.003 | x | x | +| T1068 | x | x | +| T1078 | x | x | +| T1098 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance of Defensive Tools via PowerSploit modules +This detection identifies use of PowerSploit modules for assessment of presence of defensive tools. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1595.002, T1592.002 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-AVSignature/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1595.002 | x | x | +| T1592.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules +This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1068, T1078, T1098 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-PrivescAudit/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1068 | x | x | +| T1078 | x | x | +| T1098 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/PowerShellMafia/PowerSploit + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules +This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary. + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1543, T1055, T1574 +- **Last Updated**: 2020-11-05 + +
+ View + +#### Search +``` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::detours/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +#### Required fields +* _time +* process +* dest_device_id +* dest_user_id + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1543 | x | x | +| T1055 | x | x | +| T1574 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified. + +#### References +* https://github.com/gentilkiwi/mimikatz +* https://en.wikipedia.org/wiki/Microsoft_Detours + +#### Test Dataset + +_version_: 1 +
+ +===== +### Reg exe Manipulating Windows Services Registry Keys +The search looks for reg.exe modifying registry keys that define Windows services and their configurations. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1574.011 +- **Last Updated**: 2020-11-26 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1574.011 | x | x | + +#### Kill Chain Phases +* Installation + +#### Known False Positives +It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log + +_version_: 5 +
+ +===== +### Reg exe used to hide files directories via registry keys +The search looks for command-line arguments used to hide a file or directory using the reg add command. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1564.001 +- **Last Updated**: 2019-02-27 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)" | `reg_exe_used_to_hide_files_directories_via_registry_keys_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1564.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None at the moment + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== ### Registry Keys Used For Persistence The search looks for modifications to registry keys that can be used to launch an application or service at system startup. @@ -7561,26 +12888,27 @@ There are many legitimate applications that must execute on system startup and w _version_: 5 ---- -### Windows AdFind Exe -This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. + +===== +### Registry Keys Used For Privilege Escalation +This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1018 -- **Last Updated**: 2020-12-16 +- **ATT&CK**: T1546.012 +- **Last Updated**: 2020-11-27
View #### Search ``` - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_adfind_exe_filter` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_privilege_escalation_filter` ``` #### Associated Analytic Story #### How To Implement -To successfully implement this search, you need to be ingesting logs with the process name, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. #### Required fields @@ -7588,71 +12916,889 @@ To successfully implement this search, you need to be ingesting logs with the pr | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1018 | x | x | +| T1546.012 | x | x | #### Kill Chain Phases -* Exploitation +* Actions on Objectives #### Known False Positives -administrators rarely use adfind, usually not used for legitimate reasons +There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. #### References -* https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/ -* https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html +* https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/ #### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### Registry Keys for Creating SHIM Databases +This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1546.011 +- **Last Updated**: 2020-11-26 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_for_creating_shim_databases_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response product, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1546.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+ +===== +### Remote Desktop Network Bruteforce +This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev | `remote_desktop_network_bruteforce_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must ensure that your network traffic data is populating the Network_Traffic data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.001 | x | x | + +#### Kill Chain Phases +* Reconnaissance +* Delivery + +#### Known False Positives +RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Remote Desktop Network Traffic +This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.001 +- **Last Updated**: 2020-07-07 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Remote Desktop may be used legitimately by users on the network. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### Remote Desktop Process Running On System +This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.001 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Remote Desktop may be used legitimately by users on the network. + +#### References + +#### Test Dataset + +_version_: 5 +
+ +===== +### Remote Process Instantiation via WMI +This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1047 +- **Last Updated**: 2020-11-30 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1047 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+ +===== +### Remote Registry Key modifications +This search monitors for remote modifications to registry keys. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-03-02 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `remote_registry_key_modifications_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### Remote WMI Command Attempt +This search looks for wmic.exe being launched with parameters to operate on remote systems. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1047 +- **Last Updated**: 2018-12-03 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_wmi_command_attempt_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1047 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators may use this legitimately to gather info from remote systems. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### RunDLL Loading DLL By Ordinal +This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2020-11-30 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll_loading_dll_by_ordinal_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Installation + +#### Known False Positives +While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### Ryuk Test Files Detected +The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1486 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1486 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log _version_: 1
---- -### More than usual number of LOLBAS applications in short time period -Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine. + +===== +### SMB Traffic Spike +This search looks for spikes in the number of Server Message Block (SMB) traffic connections. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.002 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter` +``` +#### Associated Analytic Story + +#### How To Implement +This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +A file server may experience high-demand loads that could cause this analytic to trigger. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### SMB Traffic Spike - MLTK +This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1021.002 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count | `smb_traffic_spike___mltk_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ +1. **Label:** Number of events, **Field:** count\ +Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1021.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### SQL Injection with Long URLs +This search looks for long URLs that have several SQL commands visible within them. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1190 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1190 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. + +#### References + +#### Test Dataset + +_version_: 2 +
+ +===== +### Samsam Test File Write +The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1486 +- **Last Updated**: 2018-12-14 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1486 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +No false positives have been identified. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/sam_sam_note/windows-sysmon.log + +_version_: 1 +
+ +===== +### Sc exe Manipulating Windows Services +This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1543.003 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1543.003 | x | x | + +#### Kill Chain Phases +* Installation + +#### Known False Positives +Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### Scheduled Task Deleted Or Created via CMD +This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1053.005 +- **Last Updated**: 2020-12-17 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_deleted_or_created_via_cmd_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053.005 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Tasks should not be manually created via CLI, this is rarely done by admins as well + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+ +===== +### Scheduled tasks used in BadRabbit ransomware +This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1053.005 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) | `scheduled_tasks_used_in_badrabbit_ransomware_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053.005 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +No known false positives + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== +### Schtasks scheduling job on remote system +This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1053.005 +- **Last Updated**: 2020-07-21 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053.005 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### Schtasks used for forcing a reboot +This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1053.005 +- **Last Updated**: 2020-12-07 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_used_for_forcing_a_reboot_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1053.005 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_shutdown/windows-sysmon.log + +_version_: 4 +
+ +===== +### Script Execution via WMI +This search looks for scripts launched via WMI. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1047 +- **Last Updated**: 2020-03-16 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `script_execution_via_wmi_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1047 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/execution_scrcons/windows-sysmon.log + +_version_: 3 +
+ +===== +### Setting Credentials via DSInternals modules +This detection identifies illegal setting of credentials via DSInternals modules. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1059, T1053 -- **Last Updated**: 2020-08-25 +- **ATT&CK**: T1068, T1078, T1098 +- **Last Updated**: 2020-11-03
View #### Search ``` - | from read_ssa_enriched_events() | eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" | stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) | eval lolbas_counter=lolbas_counter*1.0 | rename window_end as timestamp | adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L | where label AND quantile>0.99 | eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body = "TBD" | into write_ssa_detected_events(); +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-ADDBSidHistory/)=true OR match_regex(cmd_line, /(?i)Add-ADReplNgcKey/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPassword/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-ADDBBootKey/)=true OR match_regex(cmd_line, /(?i)Set-SamAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-AzureADUserEx/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ``` #### Associated Analytic Story #### How To Implement -Collect endpoint data such as sysmon or 4688 events. +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields * dest_device_id -* _time * process_name +* parent_process_name +* _time +* process_path +* dest_user_id +* process #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1059 | x | x | -| T1053 | x | x | +| T1068 | x | x | +| T1078 | x | x | +| T1098 | x | x | #### Kill Chain Phases -* Exploitation +* Actions on Objectives #### Known False Positives -Some administrative tasks may involve multiple use of LOLBAS applications in a short period of time. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. - +None identified. #### References -* https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries +* https://github.com/MichaelGrafnetter/DSInternals #### Test Dataset _version_: 1
---- + +===== ### Setting Credentials via Mimikatz modules This detection identifies illegal setting of credentials via Mimikatz modules. @@ -7702,35 +13848,43 @@ None identified. _version_: 1 ---- -### Dump LSASS via procdump Rename -Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\ -During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +===== +### Setting Credentials via PowerSploit modules +This detection identifies illegal setting of credentials via PowerSploit modules. + +- **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003.001 -- **Last Updated**: 2021-02-01 +- **ATT&CK**: T1068, T1078, T1098 +- **Last Updated**: 2020-11-03
View #### Search ``` -`sysmon` OriginalFileName=procdump process_name!=procdump*.exe EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_rename_filter` +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainUserPassword/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ``` #### Associated Analytic Story #### How To Implement -To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields +* dest_device_id +* dest_user_id +* process +* _time #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1003.001 | x | x | +| T1068 | x | x | +| T1078 | x | x | +| T1098 | x | x | #### Kill Chain Phases * Actions on Objectives @@ -7739,35 +13893,33 @@ To successfully implement this search you need to be ingesting information on pr None identified. #### References -* https://attack.mitre.org/techniques/T1003/001/ -* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump +* https://github.com/PowerShellMafia/PowerSploit #### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log _version_: 1
---- -### First Time Seen Child Process of Zoom -This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. + +===== +### Shim Database File Creation +This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1068 -- **Last Updated**: 2020-05-20 +- **ATT&CK**: T1546.011 +- **Last Updated**: 2020-12-08
View #### Search ``` -| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest | `drop_dm_object_name(Processes)` | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter` +| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter` ``` #### Associated Analytic Story #### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. #### Required fields @@ -7775,2383 +13927,37 @@ You must be ingesting data that records process activity from your hosts to popu | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1068 | x | x | +| T1546.011 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. +Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. #### References #### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/zoom_child_process/windows-sysmon.log - -_version_: 1 -
---- -### Kerberoasting spn request with RC4 encryption -This search detects a potential kerberoasting attack via service principal name requests - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1558.003 -- **Last Updated**: 2020-10-16 - -
- View - -#### Search -``` -`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 | stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kerberoasting_spn_request_with_rc4_encryption_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1558.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Older systems that support kerberos RC4 by default NetApp may generate false positives - -#### References -* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md -* https://www.trimarcsecurity.com/post/trimarcresearch-detecting-kerberoasting-activity - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log _version_: 3
---- -### Email files written outside of the Outlook directory -The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. + +===== +### Shim Database Installation With Suspicious Parameters +This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1114.001 -- **Last Updated**: 2020-07-21 +- **ATT&CK**: T1546.011 +- **Last Updated**: 2020-11-23
View #### Search ``` -| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_written_outside_of_the_outlook_directory_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### Web Servers Executing Suspicious Processes -This search looks for suspicious processes on all systems labeled as web servers. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1082 -- **Last Updated**: 2019-04-01 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1082 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Multiple Okta Users With Invalid Credentials From The Same IP -This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`okta` outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search distinct_users > 5| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.001 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Okta Failed SSO Attempts -Detect failed Okta SSO events - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`okta` displayMessage="User attempted unauthorized access to app" | stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_failed_sso_attempts_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.001 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -There may be a faulty config preventing legitmate users from accessing apps they should have access to. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Okta Account Lockout Events -Detect Okta user lockout events - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`okta` displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, country, state, city, src_ip | `okta_account_lockout_events_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.001 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Okta User Logins From Multiple Cities -This search detects logins from the same user from different cities in a 24 hour period. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `okta_user_logins_from_multiple_cities_filter` | search locations > 1 -``` -#### Associated Analytic Story - -#### How To Implement -This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.001 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### O365 Add App Role Assignment Grant User -This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1136.003 -- **Last Updated**: 2021-01-26 - -
- View - -#### Search -``` -`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." | stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_add_app_role_assignment_grant_user_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.003 | x | x | - -#### Kill Chain Phases -* Actions on Objective - -#### Known False Positives -The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. - -#### References -* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf -* https://us-cert.cisa.gov/ncas/alerts/aa21-008a - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json - -_version_: 1 -
---- -### AWS SAML Access by Provider User and Principal -This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2021-01-26 - -
- View - -#### Search -``` -`cloudtrail` eventName=Assumerolewithsaml | stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_access_by_provider_user_and_principal_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks. - -#### References -* https://us-cert.cisa.gov/ncas/alerts/aa21-008a -* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html -* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf -* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/assume_role_with_saml/assume_role_with_saml.json - -_version_: 1 -
---- -### Cloud Compute Instance Created In Previously Unused Region -This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 -- **Last Updated**: 2020-09-02 - -
- View - -#### Search -``` -| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") | table firstTime, user, dest, count , vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_in_previously_unused_region_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### O365 Excessive SSO logon errors -This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1556 -- **Last Updated**: 2021-01-26 - -
- View - -#### Search -``` -`o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired | stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId | where count > 5 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_excessive_sso_logon_errors_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1556 | x | x | - -#### Kill Chain Phases -* Actions on Objective - -#### Known False Positives -Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack. - -#### References -* https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json - -_version_: 1 -
---- -### O365 Suspicious Admin Email Forwarding -This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1114.003 -- **Last Updated**: 2020-12-16 - -
- View - -#### Search -``` -`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_admin_email_forwarding_filter` -``` -#### Associated Analytic Story - -#### How To Implement - - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -unknown - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json - -_version_: 1 -
---- -### Cloud Provisioning Activity From Previously Unseen City -This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-10-09 - -
- View - -#### Search -``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) | lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, City, user, object, command | `cloud_provisioning_activity_from_previously_unseen_city_filter` | `security_content_ctime(firstTime)` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### Detect New Open GCP Storage Buckets -This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1530 -- **Last Updated**: 2020-08-05 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions | spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action | spath output=user path=data.protoPayload.authenticationInfo.principalEmail | spath output=location path=data.protoPayload.resourceLocation.currentLocations{} | spath output=src path=data.protoPayload.requestMetadata.callerIp | spath output=bucketName path=data.protoPayload.resourceName | spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role | spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member | search (member=allUsers AND action=ADD) | table _time, bucketName, src, user, location, action, role, member | search `detect_new_open_gcp_storage_buckets_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Abnormally High Number Of Cloud Instances Destroyed -This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-08-21 - -
- View - -#### Search -``` -| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] | where cardinality >=16 | apply cloud_excessive_instances_destroyed_v1 threshold=0.005 | rename "IsOutlier(instances_destroyed)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_destroyed - expected_upper_threshold | table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability density function. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Many service accounts configured within a cloud infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### High Number of Login Failures from a single source -This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1110.001 -- **Last Updated**: 2020-12-16 - -
- View - -#### Search -``` -`o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory | stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app | search accounts_locked >= 5| `high_number_of_login_failures_from_a_single_source_filter` -``` -#### Associated Analytic Story - -#### How To Implement - - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1110.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -unknown - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect AWS Console Login by User from New City -This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 -- **Last Updated**: 2020-10-07 - -
- View - -#### Search -``` -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user City | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user City | fields earliestseen user City] | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCity = "New City" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user City userStatus userCity | `detect_aws_console_login_by_user_from_new_city_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### Detect AWS Console Login by User from New Country -This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 -- **Last Updated**: 2020-10-07 - -
- View - -#### Search -``` -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Country | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Country | fields earliestseen user Country] | eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") | eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCountry = "New Country" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Country userStatus userCountry | `detect_aws_console_login_by_user_from_new_country_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### Cloud Instance Modified By Previously Unseen User -This search looks for cloud instances being modified by users who have not previously modified them. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-07-29 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime user command object_id count | `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### Detect Spike in S3 Bucket deletion -This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1530 -- **Last Updated**: 2018-11-27 - -
- View - -#### Search -``` -`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### AWS Network Access Control List Deleted -Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.007 -- **Last Updated**: 2021-01-12 - -
- View - -#### Search -``` -`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_deleted_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.007 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It's possible that a user has legitimately deleted a network ACL. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/aws_cloudtrail_events.json - -_version_: 2 -
---- -### Cloud Compute Instance Created By Previously Unseen User -This search looks for cloud compute instances created by users who have not created them before. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-08-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime, user, dest, count vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_by_previously_unseen_user_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### AWS Detect Users with KMS keys performing encryption S3 -This search provides detection of users with KMS keys performing encryption specifically against S3 buckets. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1486 -- **Last Updated**: 2021-01-11 - -
- View - -#### Search -``` -`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" | rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1486 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -bucket with S3 encryption - -#### References -* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ -* https://github.com/d1vious/git-wild-hunt -* https://www.youtube.com/watch?v=PgzNib37g0M - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json - -_version_: 1 -
---- -### O365 Suspicious User Email Forwarding -This search detects when multiple user configured a forwarding rule to the same destination. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1114.003 -- **Last Updated**: 2020-12-16 - -
- View - -#### Search -``` -`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingSmtpAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_user_email_forwarding_filter` -``` -#### Associated Analytic Story - -#### How To Implement - - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.003 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -unknown - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json - -_version_: 1 -
---- -### Cloud API Calls From Previously Unseen User Roles -This search looks for new commands from each user role. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-09-04 - -
- View - -#### Search -``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUserApiCall=min(firstTimeSeen) | where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") | table firstTime, user, object, command |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `cloud_api_calls_from_previously_unseen_user_roles_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter` - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### O365 PST export alert -This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1114 -- **Last Updated**: 2020-12-16 - -
- View - -#### Search -``` -`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" | stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_pst_export_alert_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114 | x | x | - -#### Kill Chain Phases -* Actions on Objective - -#### Known False Positives -PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored. - -#### References -* https://attack.mitre.org/techniques/T1114/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json - -_version_: 1 -
---- -### AWS Network Access Control List Created with All Open Ports -The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.007 -- **Last Updated**: 2021-01-11 - -
- View - -#### Search -``` -`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 | eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' | where port_range>1024] | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.007 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/aws_cloudtrail_events.json - -_version_: 2 -
---- -### Abnormally High Number Of Cloud Instances Launched -This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-08-21 - -
- View - -#### Search -``` -| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] | where cardinality >=16 | apply cloud_excessive_instances_created_v1 threshold=0.005 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_launched - expected_upper_threshold | table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_launched_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Launched` to create the probability density function. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Cloud Provisioning Activity From Previously Unseen Country -This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-10-09 - -
- View - -#### Search -``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | table firstTime, src, Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter` | `security_content_ctime(firstTime)` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### Cloud Compute Instance Created With Previously Unseen Instance Type -Find EC2 instances being created with previously unseen instance types. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-09-12 - -
- View - -#### Search -``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where instance_type != "unknown" | lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenInstanceType=min(firstTimeSeen) | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") | table firstTime, user, dest, count, instance_type | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -#### Known False Positives -It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### AWS Detect Users creating keys with encrypt policy without MFA -This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1486 -- **Last Updated**: 2021-01-11 - -
- View - -#### Search -``` -`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy | spath input=requestParameters.policy output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | spath input=key_policy_statements output=key_policy_action_2 path=Action{} | eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) | spath input=key_policy_statements output=key_policy_principal path=Principal.AWS | search key_policy_action="kms:Encrypt" AND key_policy_principal="*" | stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1486 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -unknown - -#### References -* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ -* https://github.com/d1vious/git-wild-hunt -* https://www.youtube.com/watch?v=PgzNib37g0M - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json - -_version_: 1 -
---- -### O365 Suspicious Rights Delegation -This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1114.002 -- **Last Updated**: 2020-12-15 - -
- View - -#### Search -``` -`o365_management_activity` Operation=Add-MailboxPermission | spath input=Parameters | rename User AS src_user, Identity AS dest_user | search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf | stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_rights_delegation_filter` -``` -#### Associated Analytic Story - -#### How To Implement - - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.002 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Service Accounts - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json - -_version_: 1 -
---- -### New container uploaded to AWS ECR -This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1525 -- **Last Updated**: 2020-02-20 - -
- View - -#### Search -``` -| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")` | `new_container_uploaded_to_aws_ecr_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1525 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -Uploading container is a normal behavior from developers or users with access to container registry. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect Spike in blocked Outbound Traffic from your AWS -This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-05-07 - -
- View - -#### Search -``` -`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Actions on Objectives -* Command and Control - -#### Known False Positives -The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect New Open S3 buckets -This search looks for CloudTrail events where a user has created an open/public S3 bucket. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1530 -- **Last Updated**: 2021-01-12 - -
- View - -#### Search -``` -`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex field=_raw "(?{.+})" | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} | search grantees=* | mvexpand grantees | spath input=grantees output=uri path=Grantee.URI | spath input=grantees output=permission path=Permission | search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") | search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") | rename requestParameters.bucketName AS bucketName | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json - -_version_: 2 -
---- -### Detect AWS Console Login by User from New Region -This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 -- **Last Updated**: 2020-10-07 - -
- View - -#### Search -``` -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Region | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Region | fields earliestseen user Region] | eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userRegion = "New Region" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Region userStatus userRegion | `detect_aws_console_login_by_user_from_new_region_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### Detect AWS Console Login by New User -This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-28 - -
- View - -#### Search -``` -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)` | inputlookup append=t previously_seen_users_console_logins | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") |where userStatus="First Time Logging into AWS Console" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `detect_aws_console_login_by_new_user_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### Detect Spike in AWS Security Hub Alerts for User -This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2021-01-26 - -
- View - -#### Search -``` -`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename findings{}.Resources{}.Id as user | bucket span=4h _time | stats count AS alerts by _time user | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev | eval threshold_value = 2 | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -#### Known False Positives -None - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### Cloud Provisioning Activity From Previously Unseen IP Address -This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-08-16 - -
- View - -#### Search -``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, user, object_id, command | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` | `security_content_ctime(firstTime)` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### O365 Disable MFA -This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1556 -- **Last Updated**: 2020-12-16 - -
- View - -#### Search -``` -`o365_management_activity` Operation="Disable Strong Authentication." | stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_disable_mfa_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1556 | x | x | - -#### Kill Chain Phases -* Actions on Objective - -#### Known False Positives -Unless it is a special case, it is uncommon to disable MFA or Strong Authentication - -#### References -* https://attack.mitre.org/techniques/T1556/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json - -_version_: 1 -
---- -### Detect New Open S3 Buckets over AWS CLI -This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1530 -- **Last Updated**: 2021-01-12 - -
- View - -#### Search -``` -`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") | rename requestParameters.bucketName AS bucketName | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_over_aws_cli_filter` -``` -#### Associated Analytic Story - -#### How To Implement - - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json - -_version_: 1 -
---- -### O365 Excessive Authentication Failures Alert -This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1110 -- **Last Updated**: 2020-12-16 - -
- View - -#### Search -``` -`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed | stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user | where count > 10 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_excessive_authentication_failures_alert_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1110 | x | x | - -#### Kill Chain Phases -* Not Applicable - -#### Known False Positives -The threshold for alert is above 10 attempts and this should reduce the number of false positives. - -#### References -* https://attack.mitre.org/techniques/T1110/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json - -_version_: 1 -
---- -### O365 Added Service Principal -This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1136.003 -- **Last Updated**: 2021-01-26 - -
- View - -#### Search -``` -`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." | stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_added_service_principal_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.003 | x | x | - -#### Kill Chain Phases -* Actions on Objective - -#### Known False Positives -The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. - -#### References -* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf -* https://us-cert.cisa.gov/ncas/alerts/aa21-008a -* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html -* https://www.sygnia.co/golden-saml-advisory - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json - -_version_: 1 -
---- -### Detect Spike in AWS Security Hub Alerts for EC2 Instance -This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2021-01-26 - -
- View - -#### Search -``` -`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket span=4h _time | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest | eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev | eval threshold_value = 3 | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -#### Known False Positives -None - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json - -_version_: 3 -
---- -### Cloud Compute Instance Created With Previously Unseen Image -This search looks for cloud compute instances being created with previously unseen image IDs. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-10-12 - -
- View - -#### Search -``` -| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenImage=min(firstTimeSeen) | where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") | table firstTime, user, image_id, count, dest | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -#### Known False Positives -After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### O365 Bypass MFA via Trusted IP -This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.007 -- **Last Updated**: 2021-01-12 - -
- View - -#### Search -``` -`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy | rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") | mvexpand ip_addresses_new_added | where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) |stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account status user_id action | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `o365_bypass_mfa_via_trusted_ip_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.007 | x | x | - -#### Kill Chain Phases -* Actions on Objective - -#### Known False Positives -Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration. - -#### References -* https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf -* https://attack.mitre.org/techniques/T1562/007/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json - -_version_: 1 -
---- -### Abnormally High Number Of Cloud Infrastructure API Calls -This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-09-07 - -
- View - -#### Search -``` -| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_api_calls_v1 threshold=0.005 | rename "IsOutlier(api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold = api_calls - expected_upper_threshold | table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to create the probability density function. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives - - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### Detect GCP Storage access from a new IP -This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1530 -- **Last Updated**: 2020-08-10 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` | multikv | rename sc_status_ as status | rename cs_object_ as bucket_name | rename c_ip_ as remote_ip | rename cs_uri_ as request_uri | rename cs_method_ as operation | search status="\"200\"" | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri | table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri | inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri | outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) | where newIP=1 | eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") | table first_time last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour. It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### AWS Cross Account Activity From Previously Unseen Account -This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. This search is deprecated and have been translated to use the latest Authentication Datamodel. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-28 - -
- View - -#### Search -``` -| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime | eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") | where status = "New Cross Account Activity" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_cross_account_activity_from_previously_unseen_account_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen AWS Cross Account Activity - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen AWS Cross Account Activity - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `aws_cross_account_activity_from_previously_unseen_account_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### O365 New Federated Domain Added -This search detects the addition of a new Federated domain. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1136.003 -- **Last Updated**: 2021-01-26 - -
- View - -#### Search -``` -`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" | stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_federated_domain_added_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.003 | x | x | - -#### Kill Chain Phases -* Actions on Objective - -#### Known False Positives -The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider. - -#### References -* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf -* https://us-cert.cisa.gov/ncas/alerts/aa21-008a -* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html -* https://www.sygnia.co/golden-saml-advisory -* https://o365blog.com/post/aadbackdoor/ - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json - -_version_: 1 -
---- -### Detect S3 access from a new IP -This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1530 -- **Last Updated**: 2018-06-28 - -
- View - -#### Search -``` -`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Cloud Provisioning Activity From Previously Unseen Region -This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-08-16 - -
- View - -#### Search -``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region) | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, Region, user, object, command | `cloud_provisioning_activity_from_previously_unseen_region_filter` | `security_content_ctime(firstTime)` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### Abnormally High Number Of Cloud Security Group API Calls -This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user. - -- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-09-07 - -
- View - -#### Search -``` -| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 | rename "IsOutlier(security_group_api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where security_group_api_calls > expected_upper_threshold | eval distance_from_threshold = security_group_api_calls - expected_upper_threshold | table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to create the probability density function model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives - - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - -_version_: 1 -
---- -### AWS SAML Update identity provider -This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2021-01-26 - -
- View - -#### Search -``` -`cloudtrail` eventName=UpdateSAMLProvider | stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_update_identity_provider_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored. - -#### References -* https://us-cert.cisa.gov/ncas/alerts/aa21-008a -* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html -* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf -* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json - -_version_: 1 -
---- -### AWS Cloud Provisioning From Previously Unseen Region -This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 -- **Last Updated**: 2018-03-16 - -
- View - -#### Search -``` -`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_region_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### First time seen command line argument -This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001, T1059.003 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest| `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table process] | `first_time_seen_command_line_argument_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.001 | x | x | -| T1059.003 | x | x | - -#### Kill Chain Phases -* Command and Control -* Actions on Objectives - -#### Known False Positives -Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name - -#### References - -#### Test Dataset - -_version_: 5 -
---- -### Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments -This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001 -- **Last Updated**: 2021-01-19 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* | `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter` ``` #### Associated Analytic Story @@ -10164,83 +13970,42 @@ You must be ingesting data that records process activity from your hosts to popu | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1059.001 | x | x | +| T1546.011 | x | x | #### Kill Chain Phases -* Command and Control * Actions on Objectives #### Known False Positives -Legitimate process can have this combination of command-line options, but it's not common. +None identified #### References #### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log -_version_: 6 +_version_: 4
---- -### Detect Mimikatz Using Loaded Images -This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code. + +===== +### Short Lived Windows Accounts +This search detects accounts that were created and deleted in a short time period. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1003.001 -- **Last Updated**: 2019-12-03 +- **ATT&CK**: T1136.001 +- **Last Updated**: 2020-07-06
View #### Search ``` -`sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_using_loaded_images_filter` +| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 | transaction user connected=false maxspan=240m | table firstTime lastTime count user dest result_id | `short_lived_windows_accounts_filter` ``` #### Associated Analytic Story #### How To Implement -This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Other tools can import the same DLLs. These tools should be part of a whitelist. - -#### References -* https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html - -#### Test Dataset - -_version_: 1 -
---- -### EC2 Instance Started With Previously Unseen Instance Type -This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-02-07 - -
- View - -#### Search -``` -`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_instance_type_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. +This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/ #### Required fields @@ -10248,38 +14013,86 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | ID | technique | Tactic | | ----------- | ----------- |:-------------:| +| T1136.001 | x | x | #### Kill Chain Phases #### Known False Positives -It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. +It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. #### References #### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log _version_: 2
---- -### EC2 Instance Started With Previously Unseen AMI -This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel. + +===== +### Single Letter Process On Endpoint +This search looks for process names that consist only of a single letter. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-03-12 +- **ATT&CK**: T1204.002 +- **Last Updated**: 2020-12-08
View #### Search ``` -`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID | table firstTime, lastTime, arn, amiID, dest, instanceType | `ec2_instance_started_with_previously_unseen_ami_filter` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name | `single_letter_process_on_endpoint_filter` ``` #### Associated Analytic Story #### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1204.002 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/single_letter_exe/windows-sysmon.log + +_version_: 3 +
+ +===== +### Spectre and Meltdown Vulnerable Systems +The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2017-01-07 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest | `drop_dm_object_name(Vulnerabilities)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spectre_and_meltdown_vulnerable_systems_filter` +``` +#### Associated Analytic Story + +#### How To Implement +The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. #### Required fields @@ -10291,7 +14104,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases #### Known False Positives -After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. +It is possible that your vulnerability scanner is not detecting that the patches have been applied. #### References @@ -10299,7 +14112,49 @@ After a new AMI is created, the first systems created with that AMI will cause t _version_: 1
---- + +===== +### Spike in File Writes +The search looks for a sharp increase in the number of files written to a particular host + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-03-16 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter` +``` +#### Associated Analytic Story + +#### How To Implement +In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. + +#### References + +#### Test Dataset + +_version_: 3 +
+ +===== ### Splunk Enterprise Information Disclosure This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. @@ -10339,26 +14194,27 @@ Retrieving server information may be a legitimate API request. Verify that the a _version_: 1 ---- -### Execution of File With Spaces Before Extension -This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. + +===== +### Sunburst Correlation DLL and Network Event +The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1036.003 -- **Last Updated**: 2020-11-19 +- **ATT&CK**: T1203 +- **Last Updated**: 2020-12-14
View #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_spaces_before_extension_filter` +(`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) | eventstats dc(EventCode) AS dc_events | where dc_events=2 | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host | rename host as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `sunburst_correlation_dll_and_network_event_filter` ``` #### Associated Analytic Story #### How To Implement -To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days. #### Required fields @@ -10366,2045 +14222,23 @@ To successfully implement this search, you must be ingesting data that records p | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1036.003 | x | x | +| T1203 | x | x | #### Kill Chain Phases * Actions on Objectives -#### Known False Positives -None identified. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### EC2 Instance Started In Previously Unseen Region -This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 -- **Last Updated**: 2018-02-23 - -
- View - -#### Search -``` -`cloudtrail` earliest=-1h StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | inputlookup append=t previously_seen_aws_regions.csv | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where regionStatus="Instance Started in a New Region" | `ec2_instance_started_in_previously_unseen_region_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect new API calls from user roles -This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2018-04-16 - -
- View - -#### Search -``` -`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | table eventName userName] |rename userName as user| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | `detect_new_api_calls_from_user_roles_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect AWS API Activities From Unapproved Accounts -This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` errorCode=success | rename userName as identity | search NOT [| inputlookup identity_lookup_expanded | fields identity] | search NOT [| inputlookup aws_service_accounts | fields identity] | rename identity as user | stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_aws_api_activities_from_unapproved_accounts_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ -This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ -1. \ -1. **Label:** First Time, **Field:** firstTime\ -1. \ -1. **Label:** Last Time, **Field:** lastTime\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Monitor DNS For Brand Abuse -This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-23 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)`| `brand_abuse_dns` | `monitor_dns_for_brand_abuse_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Delivery -* Actions on Objectives - -#### Known False Positives -None at this time - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### AWS Cloud Provisioning From Previously Unseen City -This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 -- **Last Updated**: 2018-03-16 - -
- View - -#### Search -``` -`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_city_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Osquery pack - ColdRoot detection -This search looks for ColdRoot events from the osx-attacks osquery pack. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2019-01-29 - -
- View - -#### Search -``` -| from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path | bucket _time span=30s | stats count(path) by _time, host, user, path | `osquery_pack___coldroot_detection_filter` -``` -#### Associated Analytic Story - -#### How To Implement -In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Installation -* Command and Control - -#### Known False Positives -There are no known false positives. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Scheduled tasks used in BadRabbit ransomware -This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1053.005 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) | `scheduled_tasks_used_in_badrabbit_ransomware_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1053.005 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -No known false positives - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### DNS Query Requests Resolved by Unauthorized DNS Servers -This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1071.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` | `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1071.004 | x | x | - -#### Kill Chain Phases -* Command and Control - -#### Known False Positives -Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### Suspicious Changes to File Associations -This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1546.001 -- **Last Updated**: 2020-07-22 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table process_id dest registry_path]| `suspicious_changes_to_file_associations_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. - -#### References - -#### Test Dataset - -_version_: 4 -
---- -### Identify New User Accounts -This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.002 -- **Last Updated**: 2017-09-12 - -
- View - -#### Search -``` -| from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity empStatus endDate startDate | `identify_new_user_accounts_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.002 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect Spike in Security Group Activity -This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2018-04-18 - -
- View - -#### Search -``` -`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup security_group_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_security_group_activity_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Abnormally High AWS Instances Terminated by User - MLTK -This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | apply ec2_excessive_terminateinstances_v1 | rename "IsOutlier(instances_terminated)" as isOutlier | where isOutlier=1 -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Detect Spike in AWS API Activity -This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_aws_api_activity_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ -This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ -1. \ -1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\ -1. \ -1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives - - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Prohibited Software On Endpoint -This search looks for applications on the endpoint that you have marked as prohibited. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2019-10-11 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `prohibited_softwares` | `prohibited_software_on_endpoint_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Installation -* Command and Control -* Actions on Objectives - -#### Known False Positives -None identified - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### AWS Cloud Provisioning From Previously Unseen Country -This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 -- **Last Updated**: 2018-03-16 - -
- View - -#### Search -``` -`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_country_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect web traffic to dynamic domain providers -This search looks for web connections to dynamic DNS providers. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1071.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic` | `detect_web_traffic_to_dynamic_domain_providers_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ -This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` Deprecated because duplicate. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1071.001 | x | x | - -#### Kill Chain Phases -* Command and Control -* Actions on Objectives - -#### Known False Positives -It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Remote WMI Command Attempt -This search looks for wmic.exe being launched with parameters to operate on remote systems. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1047 -- **Last Updated**: 2018-12-03 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_wmi_command_attempt_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Administrators may use this legitimately to gather info from remote systems. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Windows DisableAntiSpyware Registry -The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.001 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.001 | x | x | - -#### Kill Chain Phases -* Delivery - -#### Known False Positives -It is unusual to turn this feature on a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Remote Registry Key modifications -This search monitors for remote modifications to registry keys. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-03-02 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `remote_registry_key_modifications_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### GCP GCR container uploaded -This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1525 -- **Last Updated**: 2020-02-20 - -
- View - -#### Search -``` -|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path | `drop_dm_object_name("Storage")` | `gcp_gcr_container_uploaded_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1525 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Clients Connecting to Multiple DNS Servers -This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048.003 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 | `clients_connecting_to_multiple_dns_servers_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ -This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048.003 | x | x | - -#### Kill Chain Phases -* Command and Control - -#### Known False Positives -It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### Suspicious writes to System Volume Information -This search detects writes to the 'System Volume Information' folder by something other than the System process. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1036 -- **Last Updated**: 2020-07-22 - -
- View - -#### Search -``` -(`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* | stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_writes_to_system_volume_information_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1036 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Detect new user AWS Console Login -This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | stats earliest(_time) as firstTime latest(_time) as lastTime by user | inputlookup append=t previously_seen_users_console_logins_cloudtrail | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| where userStatus ="First Time Logging into AWS Console" | `detect_new_user_aws_console_login_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Detection of DNS Tunnels -This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048.003 -- **Last Updated**: 2017-09-18 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" | rename "DNS.src" as src "DNS.query" as message | eval length=len(message) | stats sum(length) as length by src | append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" | rename "DNS.src" as src "DNS.answer" as message | eval message=if(message=="unknown","", message) | eval length=len(message) | stats sum(length) as length by src ] | stats sum(length) as length by src | where length > 10000 | `detection_of_dns_tunnels_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048.003 | x | x | - -#### Kill Chain Phases -* Command and Control -* Actions on Objectives - -#### Known False Positives -It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Detect DNS requests to Phishing Sites leveraging EvilGinx2 -This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1566.003 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ - **Splunk>Phantom Playbook Integration**\ -If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ -(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\ - - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1566.003 | x | x | - -#### Kill Chain Phases -* Delivery -* Command and Control - -#### Known False Positives -If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Unsuccessful Netbackup backups -This search gives you the hosts where a backup was attempted and then failed. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-12 - -
- View - -#### Search -``` -`netbackup` | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature | `unsuccessful_netbackup_backups_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -#### Known False Positives -None identified - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect Mimikatz Via PowerShell And EventCode 4703 -This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.001 -- **Last Updated**: 2019-02-27 - -
- View - -#### Search -``` -`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_via_powershell_and_eventcode_4703_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Processes created by netsh -This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.004 -- **Last Updated**: 2020-11-23 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `processes_created_by_netsh_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft. - -#### References - -#### Test Dataset - -_version_: 5 -
---- -### Open Redirect in Splunk Web -This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-19 - -
- View - -#### Search -``` -index=_internal sourcetype=splunk_web_access return_to="/%09/*" | `open_redirect_in_splunk_web_filter` -``` -#### Associated Analytic Story - -#### How To Implement -No extra steps needed to implement this search. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Delivery - -#### Known False Positives -None identified - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Cloud Network Access Control List Deleted -Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-09-08 - -
- View - -#### Search -``` -`cloudtrail` eventName=DeleteNetworkAcl|rename userIdentity.arn as arn | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `cloud_network_access_control_list_deleted_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the `cloud_network_access_control_list_deleted_filter` macro. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It's possible that a user has legitimately deleted a network ACL. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Abnormally High AWS Instances Launched by User - MLTK -This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | apply ec2_excessive_runinstances_v1 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Reg exe used to hide files directories via registry keys -The search looks for command-line arguments used to hide a file or directory using the reg add command. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1564.001 -- **Last Updated**: 2019-02-27 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)" | `reg_exe_used_to_hide_files_directories_via_registry_keys_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1564.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None at the moment - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Detect Long DNS TXT Record Response -This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048.003 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" | `detect_long_dns_txt_record_response_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048.003 | x | x | - -#### Kill Chain Phases -* Command and Control - -#### Known False Positives -It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### AWS Cloud Provisioning From Previously Unseen IP Address -This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-03-16 - -
- View - -#### Search -``` -`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_ip_address_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Unsigned Image Loaded by LSASS -This search detects loading of unsigned images by LSASS. Deprecated because too noisy. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.001 -- **Last Updated**: 2019-12-06 - -
- View - -#### Search -``` -`sysmon` EventID=7 Image=*lsass.exe Signed=false | stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `unsigned_image_loaded_by_lsass_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. - -#### References -* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -#### Test Dataset - -_version_: 1 -
---- -### Detect USB device insertion -The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-11-27 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `detect_usb_device_insertion_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Installation -* Actions on Objectives - -#### Known False Positives -Legitimate USB activity will also be detected. Please verify and investigate as appropriate. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Abnormally High AWS Instances Launched by User -This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev | `abnormally_high_aws_instances_launched_by_user_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### EC2 Instance Modified With Previously Unseen User -This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest | `ec2_instance_modified_with_previously_unseen_user_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### Windows hosts file modification -The search looks for modifications to the hosts file on all Windows endpoints across your environment. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-11-02 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* | `drop_dm_object_name(Filesystem)` | `windows_hosts_file_modification_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Command and Control - -#### Known False Positives -There may be legitimate reasons for system administrators to add entries to this file. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Abnormally High AWS Instances Terminated by User -This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=TerminateInstances errorCode=success | bucket span=10m _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev | `abnormally_high_aws_instances_terminated_by_user_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Windows connhost exe started forcefully -The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.003 -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_connhost_exe_started_forcefully_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.003 | x | x | - -#### Kill Chain Phases -* Delivery - -#### Known False Positives -This process should not be ran forcefully, we have not see any false positives for this detection - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Suspicious File Write -The search looks for files created with names that have been linked to malicious activity. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2019-04-25 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` | `suspicious_writes` | `suspicious_file_write_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### EC2 Instance Started With Previously Unseen User -This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_user_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -#### Known False Positives -It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Detect Spike in Network ACL Activity -This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.007 -- **Last Updated**: 2018-05-21 - -
- View - -#### Search -``` -`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup network_acl_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_network_acl_activity_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.007 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect API activity from users without MFA -This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-05-17 - -
- View - -#### Search -``` -`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity as user]| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_api_activity_from_users_without_mfa_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ -This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ -1. \ -1. **Label:** AWS User ARN, **Field:** userIdentity.arn\ -1. \ -1. **Label:** AWS User Type, **Field:** userIdentity.type\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -#### Known False Positives -Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Extended Period Without Successful Netbackup Backups -This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-12 - -
- View - -#### Search -``` -`netbackup` MESSAGE="Disk/Partition backup completed successfully." | stats latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest | `extended_period_without_successful_netbackup_backups_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -#### Known False Positives -None identified - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Uncommon Processes On Endpoint -This search looks for applications on the endpoint that you have marked as uncommon. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1204.002 -- **Last Updated**: 2020-07-22 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_on_endpoint_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1204.002 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified - -#### References - -#### Test Dataset - -_version_: 4 -
---- -### Detect F5 TMUI RCE CVE-2020-5902 -This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1190 -- **Last Updated**: 2020-08-02 - -
- View - -#### Search -``` -`f5_bigip_rogue` | regex _raw="(hsqldb;|.*\\.\\.;.*)" | search `detect_f5_tmui_rce_cve_2020_5902_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog. As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it. I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;). - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1190 | x | x | - -#### Kill Chain Phases -* Exploitation - #### Known False Positives unknown #### References -* https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/ -* https://support.f5.com/csp/article/K52145254 -* https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/ +* https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html #### Test Dataset _version_: 1
---- -### Detect malicious requests to exploit JBoss servers -This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-23 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Delivery - -#### Known False Positives -No known false positives for this detection. - -#### References - -#### Test Dataset - -_version_: 1 -
---- +===== ### Supernova Webshell This search aims to detect the Supernova webshell used in the SUNBURST attack. @@ -12447,26 +14281,27 @@ There might be false positives associted with this detection since items like ar _version_: 1 ---- -### Detect attackers scanning for vulnerable JBoss servers -This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. + +===== +### Suspicious Changes to File Associations +This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1082 -- **Last Updated**: 2017-09-23 +- **ATT&CK**: T1546.001 +- **Last Updated**: 2020-07-22
View #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table process_id dest registry_path]| `suspicious_changes_to_file_associations_filter` ``` #### Associated Analytic Story #### How To Implement -You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. +To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. #### Required fields @@ -12474,803 +14309,13 @@ You must be ingesting data from the web server or network traffic that contains | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1082 | x | x | - -#### Kill Chain Phases -* Reconnaissance - -#### Known False Positives -It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Monitor Web Traffic For Brand Abuse -This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-23 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Delivery - -#### Known False Positives -None at this time - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### SQL Injection with Long URLs -This search looks for long URLs that have several SQL commands visible within them. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1190 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1190 | x | x | - -#### Kill Chain Phases -* Delivery - -#### Known False Positives -It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Protocols passing authentication in cleartext -This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-11-04 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocols_passing_authentication_in_cleartext_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Reconnaissance -* Actions on Objectives - -#### Known False Positives -Some networks may use kerberized FTP or telnet servers, however, this is rare. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Large Volume of DNS ANY Queries -The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1498.002 -- **Last Updated**: 2017-09-20 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1498.002 | x | x | +| T1546.001 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Hosts receiving high volume of network traffic from email server -This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1114.002 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.002 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Detect ARP Poisoning -By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1200, T1498, T1557.002 -- **Last Updated**: 2020-08-11 - -
- View - -#### Search -``` -`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_arp_poisoning_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1200 | x | x | -| T1498 | x | x | -| T1557.002 | x | x | - -#### Kill Chain Phases -* Reconnaissance -* Delivery -* Actions on Objectives - -#### Known False Positives -This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely). - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Prohibited Network Traffic Allowed -This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter` -``` -#### Associated Analytic Story - -#### How To Implement -In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048 | x | x | - -#### Kill Chain Phases -* Delivery -* Command and Control - -#### Known False Positives -None identified - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### DNS record changed -The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1071.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| inputlookup discovered_dns_records | rename answer as discovered_answer | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ - **Splunk>Phantom Playbook Integration**\ -If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ -(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ - - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1071.004 | x | x | - -#### Kill Chain Phases -* Command and Control - -#### Known False Positives -Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### Protocol or Port Mismatch -This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048.003 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter` -``` -#### Associated Analytic Story - -#### How To Implement -Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048.003 | x | x | - -#### Kill Chain Phases -* Command and Control - -#### Known False Positives -None identified - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Excessive DNS Failures -This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1071.004 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query"| `drop_dm_object_name("DNS")`| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain| where isnull(domain)| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank| where isnull(rank)| stats sum(count) as count mode(queries) as queries by src| `get_asset(src)`| where count>50 | `excessive_dns_failures_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1071.004 | x | x | - -#### Kill Chain Phases -* Command and Control - -#### Known False Positives -It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Processes Tapping Keyboard Events -This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2019-01-25 - -
- View - -#### Search -``` -| from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id| dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter` -``` -#### Associated Analytic Story - -#### How To Implement -In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Command and Control - -#### Known False Positives -There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Child Processes of Spoolsv exe -This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1068 -- **Last Updated**: 2020-03-16 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `child_processes_of_spoolsv_exe_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### WMI Permanent Event Subscription -This search looks for the creation of WMI permanent event subscriptions. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1047 -- **Last Updated**: 2018-10-23 - -
- View - -#### Search -``` -`wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | rename ComputerName as dest | `wmi_permanent_event_subscription_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Although unlikely, administrators may use event subscriptions for legitimate purposes. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect Baron Samedit CVE-2021-3156 Segfault -This search detects the heap-based buffer overflow of sudoedit - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1068 -- **Last Updated**: 2021-01-29 - -
- View - -#### Search -``` -`linux_hosts` | search sudoedit segfault | stats count min(_time) as firstTime max(_time) as lastTime by host | search count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter` -``` -#### Associated Analytic Story - -#### How To Implement -Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged. This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -If sudoedit is throwing segfaults for other reasons this will pick those up too. - -#### References -* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit - -#### Test Dataset - -_version_: 1 -
---- -### Spike in File Writes -The search looks for a sharp increase in the number of files written to a particular host - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-03-16 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter` -``` -#### Associated Analytic Story - -#### How To Implement -In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### Detect Baron Samedit CVE-2021-3156 via OSQuery -This search detects the heap-based buffer overflow of sudoedit - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1068 -- **Last Updated**: 2021-01-28 - -
- View - -#### Search -``` -`osquery_process` | search "columns.cmdline"="sudoedit -s \\*" | `detect_baron_samedit_cve_2021_3156_via_osquery_filter` -``` -#### Associated Analytic Story - -#### How To Implement -OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -unknown - -#### References -* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit - -#### Test Dataset - -_version_: 1 -
---- -### Detection of tools built by NirSoft -This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1072 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1072 | x | x | - -#### Kill Chain Phases -* Installation -* Actions on Objectives - -#### Known False Positives -While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### Detect Oulook exe writing a zip file -This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1566.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| rename parent_process_id as outlook_id| join malicious_id type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] | table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path | where file_name != "" | `detect_oulook_exe_writing_a__zip_file_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1566.001 | x | x | - -#### Kill Chain Phases -* Installation -* Actions on Objectives - -#### Known False Positives -It is not uncommon for outlook to write legitimate zip files to the disk. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### First Time Seen Running Windows Service -This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1569.002 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`wineventlog_system` EventCode=7036 | rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | where state="running" | lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) | table _time dest service | `first_time_seen_running_windows_service_filter` -``` -#### Associated Analytic Story - -#### How To Implement -While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1569.002 | x | x | - -#### Kill Chain Phases -* Installation -* Actions on Objectives - -#### Known False Positives -A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. +There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. #### References @@ -13278,1681 +14323,8 @@ A previously unseen service is not necessarily malicious. Verify that the servic _version_: 4
---- -### WMI Temporary Event Subscription -This search looks for the creation of WMI temporary event subscriptions. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1047 -- **Last Updated**: 2018-10-23 - -
- View - -#### Search -``` -`wmi` EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?[^;|^$]+)" | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Sunburst Correlation DLL and Network Event -The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1203 -- **Last Updated**: 2020-12-14 - -
- View - -#### Search -``` -(`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) | eventstats dc(EventCode) AS dc_events | where dc_events=2 | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host | rename host as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `sunburst_correlation_dll_and_network_event_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1203 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -unknown - -#### References -* https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html - -#### Test Dataset - -_version_: 1 -
---- -### MacOS - Re-opened Applications -This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-02-07 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `macos___re_opened_applications_filter` -``` -#### Associated Analytic Story - -#### How To Implement -In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Installation -* Command and Control - -#### Known False Positives -At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect Baron Samedit CVE-2021-3156 -This search detects the heap-based buffer overflow of sudoedit - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1068 -- **Last Updated**: 2021-01-27 - -
- View - -#### Search -``` -`linux_hosts` | search "sudoedit -s \\" | `detect_baron_samedit_cve_2021_3156_filter` -``` -#### Associated Analytic Story - -#### How To Implement -Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -unknown - -#### References -* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit - -#### Test Dataset - -_version_: 1 -
---- -### Remote Desktop Process Running On System -This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1021.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.001 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Remote Desktop may be used legitimately by users on the network. - -#### References - -#### Test Dataset - -_version_: 5 -
---- -### Kubernetes Azure detect sensitive role access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-20 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=clusterroles OR clusterrolebindings | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes AWS detect sensitive role access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_aws_detect_sensitive_role_access_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes GCP detect sensitive object access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-07-11 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.resource=configmaps OR secrets | table data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name data.protoPayload.request.metadata.namespace data.labels.authorization.k8s.io/decision | dedup data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name |`kubernetes_gcp_detect_sensitive_object_access_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk add on for GCP . This search works with pubsub messaging service logs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes Azure scan fingerprint -This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1526 -- **Last Updated**: 2020-05-19 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason |`kubernetes_azure_scan_fingerprint_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1526 | x | x | - -#### Kill Chain Phases -* Reconnaissance - -#### Known False Positives -Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes AWS detect service accounts forbidden failure access -This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure | table sourceIPs{} user.username userAgent verb responseStatus.status requestURI | `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -This search can give false positives as there might be inherent issues with authentications and permissions at cluster. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes GCP detect most active service accounts by pod -This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-07-10 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.request.spec.group{}=system:serviceaccounts | table src_ip src_user http_user_agent data.protoPayload.request.spec.nonResourceAttributes.verb data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource | top src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource |`kubernetes_gcp_detect_most_active_service_accounts_by_pod_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk GCP add on. This search works with pubsub messaging service logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### aws detect permanent key creation -This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-07-27 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` CreateAccessKey | spath eventName | search eventName=CreateAccessKey "userIdentity.type"=IAMUser | table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId |`aws_detect_permanent_key_creation_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### GCP Detect high risk permissions by resource and account -This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-10-09 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.permission=iam.serviceAccounts.getaccesstoken OR iam.serviceAccounts.setIamPolicy OR iam.serviceAccounts.actas OR dataflow.jobs.create OR composer.environments.create OR dataproc.clusters.create |table data.protoPayload.requestMetadata.callerIp data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.response.bindings{}.members{} data.resource.labels.project_id | `gcp_detect_high_risk_permissions_by_resource_and_account_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk GCP add-on. This search works with gcp:pubsub:message logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -High risk permissions are part of any GCP environment, however it is important to track resource and accounts usage, this search may produce false positives. - -#### References -* https://github.com/dxa4481/gcploit -* https://www.youtube.com/watch?v=Ml09R38jpok -* https://cloud.google.com/iam/docs/permissions-reference - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes AWS detect suspicious kubectl calls -This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous | table src_ip src_user verb userAgent requestURI | stats count by src_ip src_user verb userAgent requestURI |`kubernetes_aws_detect_suspicious_kubectl_calls_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes AWS detect RBAC authorization by account -This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason | stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_aws_detect_rbac_authorization_by_account_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes Azure detect most active service accounts by pod namespace -This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-26 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace | top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### GCP Detect gcploit framework -This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-10-08 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s | table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent | `gcp_detect_gcploit_framework_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk GCP add-on. This search works with gcp:pubsub:message logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects - -#### References -* https://github.com/dxa4481/gcploit -* https://www.youtube.com/watch?v=Ml09R38jpok - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes Azure detect service accounts forbidden failure access -This search provides information on Kubernetes service accounts with failure or forbidden access status - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-20 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -This search can give false positives as there might be inherent issues with authentications and permissions at cluster. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes AWS detect most active service accounts by pod -This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts objectRef.resource=pods | table sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision | top sourceIPs{} user.username verb annotations.authorization.k8s.io/decision |`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Amazon EKS Kubernetes cluster scan detection -This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1526 -- **Last Updated**: 2020-04-15 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`amazon_eks_kubernetes_cluster_scan_detection_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1526 | x | x | - -#### Kill Chain Phases -* Reconnaissance - -#### Known False Positives -Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes Azure detect RBAC authorization by account -This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-26 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | search annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason |stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_azure_detect_rbac_authorization_by_account_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### aws detect attach to role policy -This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-07-27 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` attach policy| spath requestParameters.policyArn | table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate | `aws_detect_attach_to_role_policy_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### AWS EKS Kubernetes cluster sensitive object access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 |table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`aws_eks_kubernetes_cluster_sensitive_object_access_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. This search works with cloudwatch logs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### GCP Kubernetes cluster pod scan detection -This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1526 -- **Last Updated**: 2020-07-17 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` category=kube-audit |spath input=properties.log |search responseStatus.code=401 |table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod | `gcp_kubernetes_cluster_pod_scan_detection_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1526 | x | x | - -#### Kill Chain Phases -* Reconnaissance - -#### Known False Positives -Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### GCP Kubernetes cluster scan detection -This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1526 -- **Last Updated**: 2020-04-15 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `gcp_kubernetes_cluster_scan_detection_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1526 | x | x | - -#### Kill Chain Phases -* Reconnaissance - -#### Known False Positives -Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes GCP detect suspicious kubectl calls -This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-07-11 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous | table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path |dedup src_ip src_user |`kubernetes_gcp_detect_suspicious_kubectl_calls_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk add on for GCP. This search works with pubsub messaging logs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### gcp detect oauth token abuse -This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-09-01 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` type.googleapis.com/google.cloud.audit.AuditLog |table protoPayload.@type protoPayload.status.details{}.@type protoPayload.status.details{}.violations{}.callerIp protoPayload.status.details{}.violations{}.type protoPayload.status.message | `gcp_detect_oauth_token_abuse_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk GCP add-on. This search works with gcp:pubsub:message logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -GCP Oauth token abuse detection will only work if there are access policies in place along with audit logs. - -#### References -* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-1 -* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-2 - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes Azure detect suspicious kubectl calls -This search provides information on rare Kubectl calls with IP, verb namespace and object access context - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-26 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration | search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 | table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI | rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI |`kubernetes_azure_detect_suspicious_kubectl_calls_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes GCP detect sensitive role access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-07-11 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole dest=apis/rbac.authorization.k8s.io/v1 src_ip!=::1 | table src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason | dedup src_ip src_user |`kubernetes_gcp_detect_sensitive_role_access_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk add on for GCP. This search works with pubsub messaging servicelogs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Sensitive role resource access is necessary for cluster operation, however source IP, user agent, decision and reason may indicate possible malicious use. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### aws detect sts get session token abuse -This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1550 -- **Last Updated**: 2020-07-27 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` ASIA userIdentity.type=IAMUser| spath eventName | search eventName=GetSessionToken | table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region | `aws_detect_sts_get_session_token_abuse_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1550 | x | x | - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Amazon EKS Kubernetes Pod scan detection -This search provides detection information on unauthenticated requests against Kubernetes' Pods API - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1526 -- **Last Updated**: 2020-04-15 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1526 | x | x | - -#### Kill Chain Phases -* Reconnaissance - -#### Known False Positives -Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### aws detect role creation -This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-07-27 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* | table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate | `aws_detect_role_creation_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes GCP detect RBAC authorizations by account -This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-07-11 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole | table src_ip src_user data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason | rare src_user data.labels.authorization.k8s.io/reason |`kubernetes_gcp_detect_rbac_authorizations_by_account_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on for GCP. This search works with pubsub messaging service logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes Azure pod scan fingerprint -This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-20 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod |`kubernetes_azure_pod_scan_fingerprint_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Reconnaissance - -#### Known False Positives -Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### aws detect sts assume role abuse -This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-07-27 - -
- View - -#### Search -``` -`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role | table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate | `aws_detect_sts_assume_role_abuse_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes Azure detect sensitive object access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-20 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_object_access_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### GCP Detect accounts with high risk roles by project -This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 -- **Last Updated**: 2020-10-09 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}.role=roles/owner OR roles/editor OR roles/iam.serviceAccountUser OR roles/iam.serviceAccountAdmin OR roles/iam.serviceAccountTokenCreator OR roles/dataflow.developer OR roles/dataflow.admin OR roles/composer.admin OR roles/dataproc.admin OR roles/dataproc.editor | table data.resource.type data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.authorizationInfo{}.resource data.protoPayload.response.bindings{}.role data.protoPayload.response.bindings{}.members{} | `gcp_detect_accounts_with_high_risk_roles_by_project_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk GCP add-on. This search works with gcp:pubsub:message logs - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -Accounts with high risk roles should be reduced to the minimum number needed, however specific tasks and setups may be simply expected behavior within organization - -#### References -* https://github.com/dxa4481/gcploit -* https://www.youtube.com/watch?v=Ml09R38jpok -* https://cloud.google.com/iam/docs/understanding-roles - -#### Test Dataset - -_version_: 1 -
---- -### Kubernetes GCP detect service accounts forbidden failure access -This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` system:serviceaccounts data.protoPayload.response.status.allowed!=* | table src_ip src_user http_user_agent data.protoPayload.response.spec.resourceAttributes.namespace data.resource.labels.cluster_name data.protoPayload.response.spec.resourceAttributes.verb data.protoPayload.request.status.allowed data.protoPayload.response.status.reason data.labels.authorization.k8s.io/decision | dedup src_ip src_user | `kubernetes_gcp_detect_service_accounts_forbidden_failure_access_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must install splunk add on for GCP. This search works with pubsub messaging service logs. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Lateral Movement - -#### Known False Positives -This search can give false positives as there might be inherent issues with authentications and permissions at cluster. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect New Login Attempts to Routers -The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-12 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Legitimate router connections may appear as new connections - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Email Attachments With Lots Of Spaces -Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-19 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address "(?.*)@" | `email_attachments_with_lots_of_spaces_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ - **Splunk Phantom Playbook Integration**\ -If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases -* Delivery - -#### Known False Positives -None at this time - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Phishing Email Detection by Machine Learning Method - SSA -Malicious mails can conduct phishing that induces readers to open attachment, click links or trigger third party service. This detect uses Natural Language Processing (NLP) approach to analyze an email message's content (Sender, Subject and Body) and judge whether it is a phishing email. The detection adopts a deep learning (neural network) model that employs character level embeddings plus LSTM layers to perform classification. The model is pre-trained and then published as ONNX format. Current sample model is trained using the dataset published at https://github.com/splunk/attack_data/tree/master/datasets/T1566_Phishing_Email/splunk_train.json User are expected to re-train the model by combining with their own training data for better accuracy using the provided model file (SMLE notebook). DSP pipeline then processes the email message and passes it as an event to Apply ML Models function, which returns the probability of a phishing email. Current implementation assumes the email is fed to DSP in JSON format contains at least email's sender, subject and its message body, including reply content, if any. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: T1566 -- **Last Updated**: 2020-08-25 - -
- View - -#### Search -``` -| from read_ssa_enriched_events() | eval eventLine=concat(ucast(map_get(input_event, "From"), "string", " "), " ", ucast(map_get(input_event, "Subject"), "string", " "), " ", ucast(map_get(input_event, "Content"), "string", " "), " "), _time=map_get(input_event, "_time") | where eventLine IS NOT NULL | eval mapC={" ": 32, "!": 33, "\"": 34, "#": 35, "$": 36, "%": 37, "&": 38, "`": 39, "(": 40, ")": 41, "*": 42, "+": 43, ",": 44, "-": 45, ".": 46, "/": 47, "0": 48, "1": 49, "2": 50, "3": 51, "4": 52, "5": 53, "6": 54, "7": 55, "8": 56, "9": 57, ":": 58, ";": 59, "<": 60, "=": 61, ">": 62, "?": 63, "@": 64, "A": 65, "B": 66, "C": 67, "D": 68, "E": 69, "F": 70, "G": 71, "H": 72, "I": 73, "J": 74, "K": 75, "L": 76, "M": 77, "N": 78, "O": 79, "P": 80, "Q": 81, "R": 82, "S": 83, "T": 84, "U": 85, "V": 86, "W": 87, "X": 88, "Y": 89, "Z": 90, "[": 91, "\\": 92, "]": 93, "^": 94, "_": 95, "`": 96, "a": 97, "b": 98, "c": 99, "d": 100, "e": 101, "f": 102, "g": 103, "h": 104, "i": 105, "j": 106, "k": 107, "l": 108, "m": 109, "n": 110, "o": 111, "p": 112, "q": 113, "r": 114, "s": 115, "t": 116, "u": 117, "v": 118, "w": 119, "x": 120, "y": 121, "z": 122, "{": 123, "|": 124, "}": 125, "~": 126}, ml_in = for_each(iterator(mvrange(1,129), "i"), cast(map_get(mapC, substr(eventLine, i, 1)), "float") ) | apply_model connection_id="YOUR_S3_ONNX_CONNECTOR_ID" name="phishing_email_v8" path="s3://smle-experiments/models/phishing_email" | eval probability = mvindex(ml_out, 0) | where probability > 0.5 | eval start_time=_time, end_time=_time, entities="TBD", body="TBD" | select probability, body, entities, start_time, end_time | into write_ssa_detected_events(); -``` -#### Associated Analytic Story - -#### How To Implement -Events are fed to DSP contains at least email's sender, subject and its message body. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1566 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Because of imbalance of anomaly data in training, the model will less likely report false positive. Instead, the model is more prone to false negative. Current best recall score is ~85% - -#### References - -#### Test Dataset - -_version_: 1 -
---- +===== ### Suspicious Email - UBA Anomaly This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). @@ -14993,46 +14365,8 @@ This detection model will alert on any sender domain that is seen for the first _version_: 3 ---- -### No Windows Updates in a time frame -This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-15 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) | `security_content_ctime(lastTime)` | search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -#### Known False Positives -None identified - -#### References - -#### Test Dataset - -_version_: 1 -
---- +===== ### Suspicious Email Attachment Extensions This search looks for emails that have attachments with suspicious file extensions. @@ -15075,26 +14409,27 @@ None identified _version_: 3 ---- -### Monitor Email For Brand Abuse -This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. + +===== +### Suspicious File Write +The search looks for files created with names that have been linked to malicious activity. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: - **ATT&CK**: -- **Last Updated**: 2018-01-05 +- **Last Updated**: 2019-04-25
View #### Search ``` -| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter` +| tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` | `suspicious_writes` | `suspicious_file_write_filter` ``` #### Associated Analytic Story #### How To Implement -You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. #### Required fields @@ -15103,60 +14438,20 @@ You need to ingest email header data. Specifically the sender's address (src_use | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -#### Kill Chain Phases -* Delivery - -#### Known False Positives -None at this time - -#### References - -#### Test Dataset - -_version_: 2 -
---- -### Email servers sending high volume traffic to hosts -This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1114.002 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.002 | x | x | - #### Kill Chain Phases * Actions on Objectives #### Known False Positives -The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. +It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. #### References #### Test Dataset -_version_: 2 +_version_: 3
---- + +===== ### Suspicious Java Classes This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. @@ -15196,26 +14491,27 @@ There are no known false positives. _version_: 1 ---- -### Spectre and Meltdown Vulnerable Systems -The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. + +===== +### Suspicious MSBuild Rename +The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-01-07 +- **ATT&CK**: T1127.001, T1036.003 +- **Last Updated**: 2021-01-12
View #### Search ``` -| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest | `drop_dm_object_name(Vulnerabilities)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spectre_and_meltdown_vulnerable_systems_filter` +`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_msbuild_rename_filter` ``` #### Associated Analytic Story #### How To Implement -The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields @@ -15223,88 +14519,46 @@ The search requires that you are ingesting your vulnerability-scanner data and t | ID | technique | Tactic | | ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -#### Known False Positives -It is possible that your vulnerability scanner is not detecting that the patches have been applied. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### Detect Zerologon via Zeek -This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1190 -- **Last Updated**: 2020-09-15 - -
- View - -#### Search -``` -`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) | bin span=5m _time | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip | search opscount=3 authcount>4 passcount>0 | search `detect_zerologon_via_zeek_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json. These three operations are then correlated on the Zeek UID field. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1190 | x | x | +| T1127.001 | x | x | +| T1036.003 | x | x | #### Kill Chain Phases * Exploitation #### Known False Positives -unknown +Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive. #### References -* https://www.secura.com/blog/zero-logon -* https://github.com/SecuraBV/CVE-2020-1472 -* https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472 +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md +* https://github.com/infosecn1nja/MaliciousMacroMSBuild/ #### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log _version_: 1
---- -### DNS Query Length Outliers - MLTK -This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. + +===== +### Suspicious MSBuild Spawn +The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1071.004 -- **Last Updated**: 2020-01-22 +- **ATT&CK**: T1127.001 +- **Last Updated**: 2021-01-12
View #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_msbuild_spawn_filter` ``` #### Associated Analytic Story #### How To Implement -To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ -This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ -1. \ -1. **Label:** DNS Query Length, **Field:** query_length\ -1. \ -1. **Label:** Number of events, **Field:** count\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields @@ -15312,13 +14566,611 @@ Detailed documentation on how to create a new field within Incident Review may b | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1071.004 | x | x | +| T1127.001 | x | x | #### Kill Chain Phases -* Command and Control +* Exploitation #### Known False Positives -If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +#### References +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + +_version_: 1 +
+ +===== +### Suspicious Reg exe Process +This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1112 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup process_id| table process_id dest] | `suspicious_reg_exe_process_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1112 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. + +#### References +* https://car.mitre.org/wiki/CAR-2013-03-001 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log + +_version_: 4 +
+ +===== +### Suspicious Regsvr32 Register Suspicious Path +Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.010 +- **Last Updated**: 2021-01-28 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_regsvr32_register_suspicious_path_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.010 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues. + +#### References +* https://attack.mitre.org/techniques/T1218/010/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md +* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ +* https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 +* https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Suspicious Rundll32 Rename +The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011, T1036.003 +- **Last Updated**: 2021-02-04 + +
+ View + +#### Search +``` +`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_rundll32_rename_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | +| T1036.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Suspicious Rundll32 StartW +The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-04 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_startw_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://www.cobaltstrike.com/help-windows-executable +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Suspicious Rundll32 dllregisterserver +The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-09 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_dllregisterserver_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/seedworm-apt-iran-middle-east +* https://github.com/pan-unit42/tweets/blob/master/2020-12-10-IOCs-from-Ursnif-infection-with-Delf-variant.txt +* https://www.crowdstrike.com/blog/duck-hunting-with-falcon-complete-qakbot-zip-based-campaign/ +* https://msdn.microsoft.com/en-us/library/windows/desktop/ms682162(v=vs.85).aspx + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Suspicious Rundll32 no CommandLine Arguments +The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.011 +- **Last Updated**: 2021-02-09 + +
+ View + +#### Search +``` +`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) | regex CommandLine="(rundll32\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_no_commandline_arguments_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.011 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. + +#### References +* https://attack.mitre.org/techniques/T1218/011/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Suspicious microsoft workflow compiler rename +The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1127, T1036.003 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_microsoft_workflow_compiler_rename_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1127, T1036.003 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive. + +#### References +* https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log + +_version_: 1 +
+ +===== +### Suspicious microsoft workflow compiler usage +The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1127 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_usage_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1127 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM. + +#### References +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log + +_version_: 1 +
+ +===== +### Suspicious msbuild path +The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1127.001, T1036.003 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `suspicious_msbuild_path_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1127.001 | x | x | +| T1036.003 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on. + +#### References +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + +_version_: 1 +
+ +===== +### Suspicious mshta child process +The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.005 +- **Last Updated**: 2021-01-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_child_process_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.005 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +#### References +* https://github.com/redcanaryco/AtomicTestHarnesses +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Suspicious mshta spawn +The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1218.005 +- **Last Updated**: 2021-01-20 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_spawn_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1218.005 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +#### References +* https://codewhitesec.blogspot.com/2018/07/lethalhta.html +* https://github.com/redcanaryco/AtomicTestHarnesses +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Suspicious wevtutil Usage +The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1070.001 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `suspicious_wevtutil_usage_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1070.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-sysmon.log + +_version_: 3 +
+ +===== +### Suspicious writes to System Volume Information +This search detects writes to the 'System Volume Information' folder by something other than the System process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1036 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +(`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* | stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_writes_to_system_volume_information_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. #### References @@ -15326,7 +15178,191 @@ If you are seeing more results than desired, you may consider reducing the value _version_: 2
---- + +===== +### Suspicious writes to windows Recycle Bin +This search detects writes to the recycle bin by a process other than explorer.exe. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1036 +- **Last Updated**: 2020-07-22 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`| search [| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")` | table process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036 | x | x | + +#### Kill Chain Phases + +#### Known False Positives +Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/write_to_recycle_bin/windows-sysmon.log + +_version_: 4 +
+ +===== +### System Information Discovery Detection +Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1082 +- **Last Updated**: 2020-10-12 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest by dest | where dc_processes_by_dest > 2 | stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1082 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Administrators debugging servers + +#### References +* https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### System Process Running from Unexpected Location +An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml + +- **Product**: UEBA for Security Cloud +- **Data Models**: +- **ATT&CK**: T1036 +- **Last Updated**: 2020-08-25 + +
+ View + +#### Search +``` + $ssa_input = | from read_ssa_enriched_events() | eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), user=ucast(map_get(input_event, "dest_user_id"), "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=lower(ucast(map_get(input_event, "process_path"), "string", null)); +$cond_1 = | from $ssa_input | where process_name="arp.exe" OR process_name="adaptertroubleshooter.exe" OR process_name="applicationframehost.exe" OR process_name="atbroker.exe" OR process_name="authhost.exe" OR process_name="autoworkplace.exe" OR process_name="axinstui.exe" OR process_name="backgroundtransferhost.exe" OR process_name="bdehdcfg.exe" OR process_name="bdeuisrv.exe" OR process_name="bdeunlockwizard.exe" OR process_name="bitlockerdeviceencryption.exe" OR process_name="bitlockerwizard.exe" OR process_name="bitlockerwizardelev.exe" OR process_name="bytecodegenerator.exe" OR process_name="camerasettingsuihost.exe" OR process_name="castsrv.exe" OR process_name="certenrollctrl.exe" OR process_name="checknetisolation.exe" OR process_name="clipup.exe" OR process_name="cloudexperiencehostbroker.exe" OR process_name="cloudnotifications.exe" OR process_name="cloudstoragewizard.exe" OR process_name="compmgmtlauncher.exe" OR process_name="compattelrunner.exe" OR process_name="computerdefaults.exe" OR process_name="credentialuibroker.exe" OR process_name="dfdwiz.exe" OR process_name="dwwin.exe" OR process_name="dataexchangehost.exe" OR process_name="defrag.exe" OR process_name="devicedisplayobjectprovider.exe" OR process_name="deviceeject.exe" OR process_name="deviceenroller.exe" OR process_name="devicepairingwizard.exe" OR process_name="deviceproperties.exe" OR process_name="disksnapshot.exe" OR process_name="dism.exe" OR process_name="displayswitch.exe" OR process_name="dmnotificationbroker.exe" OR process_name="dmomacpmo.exe" OR process_name="dpiscaling.exe" OR process_name="dsmusertask.exe" OR process_name="dxpserver.exe" OR process_name="edpcleanup.exe" OR process_name="eosnotify.exe" OR process_name="eap3host.exe" OR process_name="easpoliciesbrokerhost.exe" OR process_name="easeofaccessdialog.exe" OR process_name="ehstorauthn.exe" OR process_name="fxscover.exe" OR process_name="fxssvc.exe" OR process_name="fxsunatd.exe" OR process_name="filehistory.exe" OR process_name="fondue.exe" OR process_name="gamepanel.exe" OR process_name="genvalobj.exe" OR process_name="gettingstarted.exe" OR process_name="hostname.exe" OR process_name="icsentitlementhost.exe" OR process_name="infdefaultinstall.exe" OR process_name="installagent.exe" OR process_name="languagecomponentsinstallercomhandler.exe" OR process_name="launchtm.exe" OR process_name="launchwinapp.exe" OR process_name="legacynetuxhost.exe" OR process_name="licensemanagershellext.exe" OR process_name="licensingui.exe" OR process_name="locationnotificationwindows.exe" OR process_name="locationnotifications.exe" OR process_name="locator.exe" OR process_name="lockapphost.exe" OR process_name="lockscreencontentserver.exe" OR process_name="logonui.exe" OR process_name="lsaiso.exe" OR process_name="mdeserver.exe" OR process_name="mdmagent.exe" OR process_name="mdmappinstaller.exe" OR process_name="mrinfo.exe" OR process_name="mrt.exe" OR process_name="mschedexe.exe" OR process_name="magnify.exe" OR process_name="mbaeparsertask.exe" OR process_name="mdres.exe" OR process_name="mdsched.exe" OR process_name="migautoplay.exe" OR process_name="mpsigstub.exe" OR process_name="msspellcheckinghost.exe" OR process_name="muiunattend.exe" OR process_name="multidigimon.exe" OR process_name="musnotification.exe" OR process_name="musnotificationux.exe" OR process_name="napstat.exe" OR process_name="netstat.exe" OR process_name="narrator.exe" OR process_name="netcfgnotifyobjecthost.exe" OR process_name="netevtfwdr.exe" OR process_name="netproj.exe" OR process_name="netplwiz.exe" OR process_name="networkuxbroker.exe"; +$cond_2 = | from $ssa_input | where process_name="openwith.exe" OR process_name="optionalfeatures.exe" OR process_name="pathping.exe" OR process_name="ping.exe" OR process_name="passwordonwakesettingflyout.exe" OR process_name="pickerhost.exe" OR process_name="pkgmgr.exe" OR process_name="pnpunattend.exe" OR process_name="pnputil.exe" OR process_name="presentationhost.exe" OR process_name="presentationsettings.exe" OR process_name="printbrmui.exe" OR process_name="printdialoghost.exe" OR process_name="printdialoghost3d.exe" OR process_name="printisolationhost.exe" OR process_name="proximityuxhost.exe" OR process_name="rdspnf.exe" OR process_name="rmactivate.exe" OR process_name="rmactivate_isv.exe" OR process_name="rmactivate_ssp.exe" OR process_name="rmactivate_ssp_isv.exe" OR process_name="route.exe" OR process_name="rdpsa.exe" OR process_name="rdpsaproxy.exe" OR process_name="rdpsauachelper.exe" OR process_name="reagentc.exe" OR process_name="recoverydrive.exe" OR process_name="register-cimprovider.exe" OR process_name="registeriepkeys.exe" OR process_name="relpost.exe" OR process_name="remoteposworker.exe" OR process_name="rmclient.exe" OR process_name="robocopy.exe" OR process_name="rpcping.exe" OR process_name="runlegacycplelevated.exe" OR process_name="runtimebroker.exe" OR process_name="sihclient.exe" OR process_name="searchfilterhost.exe" OR process_name="searchindexer.exe" OR process_name="searchprotocolhost.exe" OR process_name="secedit.exe" OR process_name="sensordataservice.exe" OR process_name="setieinstalleddate.exe" OR process_name="settingsynchost.exe" OR process_name="slidetoshutdown.exe" OR process_name="smartscreensettings.exe" OR process_name="sndvol.exe" OR process_name="snippingtool.exe" OR process_name="soundrecorder.exe" OR process_name="spaceagent.exe" OR process_name="sppextcomobj.exe" OR process_name="srtasks.exe" OR process_name="stikynot.exe" OR process_name="synchost.exe" OR process_name="sysreseterr.exe" OR process_name="systempropertiesadvanced.exe" OR process_name="systempropertiescomputername.exe" OR process_name="systempropertiesdataexecutionprevention.exe" OR process_name="systempropertieshardware.exe" OR process_name="systempropertiesperformance.exe" OR process_name="systempropertiesprotection.exe" OR process_name="systempropertiesremote.exe" OR process_name="systemsettingsadminflows.exe" OR process_name="systemsettingsbroker.exe" OR process_name="systemsettingsremovedevice.exe" OR process_name="tcpsvcs.exe" OR process_name="tracert.exe" OR process_name="tstheme.exe" OR process_name="tswbprxy.exe" OR process_name="tapiunattend.exe" OR process_name="taskmgr.exe" OR process_name="thumbnailextractionhost.exe" OR process_name="tokenbrokercookies.exe" OR process_name="tpminit.exe" OR process_name="tswpfwrp.exe" OR process_name="ui0detect.exe" OR process_name="upgraderesultsui.exe" OR process_name="useraccountbroker.exe" OR process_name="useraccountcontrolsettings.exe" OR process_name="usoclient.exe" OR process_name="utilman.exe" OR process_name="vssvc.exe" OR process_name="vaultcmd.exe" OR process_name="vaultsysui.exe" OR process_name="wfs.exe" OR process_name="wmpdmc.exe" OR process_name="wpdshextautoplay.exe" OR process_name="wscollect.exe" OR process_name="wsmanhttpconfig.exe" OR process_name="wsreset.exe" OR process_name="wudfhost.exe" OR process_name="wwahost.exe" OR process_name="wallpaperhost.exe" OR process_name="webcache.exe" OR process_name="werfault.exe" OR process_name="werfaultsecure.exe" OR process_name="winsat.exe" OR process_name="windows.media.backgroundplayback.exe" OR process_name="windowsactiondialog.exe" OR process_name="windowsanytimeupgrade.exe" OR process_name="windowsanytimeupgraderesults.exe"; +$cond_3 = | from $ssa_input | where process_name="windowsanytimeupgradeui.exe" OR process_name="windowsupdateelevatedinstaller.exe" OR process_name="workfolders.exe" OR process_name="wpcmon.exe" OR process_name="acu.exe" OR process_name="aitagent.exe" OR process_name="aitstatic.exe" OR process_name="alg.exe" OR process_name="appidcertstorecheck.exe" OR process_name="appidpolicyconverter.exe" OR process_name="at.exe" OR process_name="attrib.exe" OR process_name="audiodg.exe" OR process_name="auditpol.exe" OR process_name="autochk.exe" OR process_name="autoconv.exe" OR process_name="autofmt.exe" OR process_name="baaupdate.exe" OR process_name="backgroundtaskhost.exe" OR process_name="bcastdvr.exe" OR process_name="bcdboot.exe" OR process_name="bcdedit.exe" OR process_name="bdechangepin.exe" OR process_name="bdeunlock.exe" OR process_name="bitsadmin.exe" OR process_name="bootcfg.exe" OR process_name="bootim.exe" OR process_name="bootsect.exe" OR process_name="bridgeunattend.exe" OR process_name="browser_broker.exe" OR process_name="bthudtask.exe" OR process_name="cacls.exe" OR process_name="calc.exe" OR process_name="cdpreference.exe" OR process_name="certreq.exe" OR process_name="certutil.exe" OR process_name="change.exe" OR process_name="changepk.exe" OR process_name="charmap.exe" OR process_name="chglogon.exe" OR process_name="chgport.exe" OR process_name="chgusr.exe" OR process_name="chkdsk.exe" OR process_name="chkntfs.exe" OR process_name="choice.exe" OR process_name="cipher.exe" OR process_name="cleanmgr.exe" OR process_name="cliconfg.exe" OR process_name="clip.exe" OR process_name="cmd.exe" OR process_name="cmdkey.exe" OR process_name="cmdl32.exe" OR process_name="cmmon32.exe" OR process_name="cmstp.exe" OR process_name="cofire.exe" OR process_name="colorcpl.exe" OR process_name="comp.exe" OR process_name="compact.exe" OR process_name="conhost.exe" OR process_name="consent.exe" OR process_name="control.exe" OR process_name="convert.exe" OR process_name="credwiz.exe" OR process_name="cscript.exe" OR process_name="csrss.exe" OR process_name="ctfmon.exe" OR process_name="cttune.exe" OR process_name="cttunesvr.exe" OR process_name="dashost.exe" OR process_name="dccw.exe" OR process_name="dcomcnfg.exe" OR process_name="ddodiag.exe" OR process_name="dfrgui.exe" OR process_name="dialer.exe" OR process_name="diantz.exe" OR process_name="dinotify.exe" OR process_name="diskpart.exe" OR process_name="diskperf.exe" OR process_name="diskraid.exe" OR process_name="dispdiag.exe" OR process_name="djoin.exe" OR process_name="dllhost.exe" OR process_name="dllhst3g.exe" OR process_name="dmcertinst.exe" OR process_name="dmcfghost.exe" OR process_name="dmclient.exe" OR process_name="dnscacheugc.exe" OR process_name="doskey.exe" OR process_name="dpapimig.exe" OR process_name="dpnsvr.exe" OR process_name="driverquery.exe" OR process_name="drvcfg.exe" OR process_name="drvinst.exe" OR process_name="dsregcmd.exe" OR process_name="dstokenclean.exe" OR process_name="dvdplay.exe" OR process_name="dvdupgrd.exe" OR process_name="dwm.exe" OR process_name="dxdiag.exe" OR process_name="easinvoker.exe" OR process_name="efsui.exe"; +$cond_4 = | from $ssa_input | where process_name="embeddedapplauncher.exe" OR process_name="esentutl.exe" OR process_name="eudcedit.exe" OR process_name="eventcreate.exe" OR process_name="eventvwr.exe" OR process_name="expand.exe" OR process_name="extrac32.exe" OR process_name="fc.exe" OR process_name="fhmanagew.exe" OR process_name="find.exe" OR process_name="findstr.exe" OR process_name="finger.exe" OR process_name="fixmapi.exe" OR process_name="fltmc.exe" OR process_name="fodhelper.exe" OR process_name="fontdrvhost.exe" OR process_name="fontview.exe" OR process_name="forfiles.exe" OR process_name="fsavailux.exe" OR process_name="fsquirt.exe" OR process_name="fsutil.exe" OR process_name="ftp.exe" OR process_name="fvenotify.exe" OR process_name="fveprompt.exe" OR process_name="getmac.exe" OR process_name="gpresult.exe" OR process_name="gpscript.exe" OR process_name="gpupdate.exe" OR process_name="grpconv.exe" OR process_name="hdwwiz.exe" OR process_name="help.exe" OR process_name="hwrcomp.exe" OR process_name="hwrreg.exe" OR process_name="icacls.exe" OR process_name="icardagt.exe" OR process_name="icsunattend.exe" OR process_name="ie4uinit.exe" OR process_name="ieunatt.exe" OR process_name="ieetwcollector.exe" OR process_name="iexpress.exe" OR process_name="immersivetpmvscmgrsvr.exe" OR process_name="ipconfig.exe" OR process_name="irftp.exe" OR process_name="iscsicli.exe" OR process_name="iscsicpl.exe" OR process_name="isoburn.exe" OR process_name="klist.exe" OR process_name="ksetup.exe" OR process_name="ktmutil.exe" OR process_name="label.exe" OR process_name="licensingdiag.exe" OR process_name="lodctr.exe" OR process_name="logagent.exe" OR process_name="logman.exe" OR process_name="logoff.exe" OR process_name="lpkinstall.exe" OR process_name="lpksetup.exe" OR process_name="lpremove.exe" OR process_name="lsass.exe" OR process_name="lsm.exe" OR process_name="makecab.exe" OR process_name="manage-bde.exe" OR process_name="mblctr.exe" OR process_name="mcbuilder.exe" OR process_name="mctadmin.exe" OR process_name="mfpmp.exe" OR process_name="mmc.exe" OR process_name="mobsync.exe" OR process_name="mountvol.exe" OR process_name="mpnotify.exe" OR process_name="msconfig.exe" OR process_name="msdt.exe" OR process_name="msdtc.exe" OR process_name="msfeedssync.exe" OR process_name="msg.exe" OR process_name="mshta.exe" OR process_name="msiexec.exe" OR process_name="msinfo32.exe" OR process_name="mspaint.exe" OR process_name="msra.exe" OR process_name="mstsc.exe" OR process_name="mtstocom.exe" OR process_name="nbtstat.exe" OR process_name="ndadmin.exe" OR process_name="net.exe" OR process_name="net1.exe" OR process_name="netbtugc.exe" OR process_name="netcfg.exe" OR process_name="netiougc.exe" OR process_name="netsh.exe" OR process_name="newdev.exe" OR process_name="nltest.exe" OR process_name="notepad.exe" OR process_name="nslookup.exe" OR process_name="ntoskrnl.exe" OR process_name="ntprint.exe" OR process_name="ocsetup.exe" OR process_name="odbcad32.exe" OR process_name="odbcconf.exe" OR process_name="omadmclient.exe" OR process_name="omadmprc.exe"; +$cond_5 = | from $ssa_input | where process_name="openfiles.exe" OR process_name="osk.exe" OR process_name="p2phost.exe" OR process_name="pcalua.exe" OR process_name="pcaui.exe" OR process_name="pcawrk.exe" OR process_name="pcwrun.exe" OR process_name="perfmon.exe" OR process_name="phoneactivate.exe" OR process_name="plasrv.exe" OR process_name="poqexec.exe" OR process_name="powercfg.exe" OR process_name="prevhost.exe" OR process_name="print.exe" OR process_name="printfilterpipelinesvc.exe" OR process_name="printui.exe" OR process_name="proquota.exe" OR process_name="provtool.exe" OR process_name="psr.exe" OR process_name="pwlauncher.exe" OR process_name="qappsrv.exe" OR process_name="qprocess.exe" OR process_name="query.exe" OR process_name="quser.exe" OR process_name="qwinsta.exe" OR process_name="rasautou.exe" OR process_name="rasdial.exe" OR process_name="raserver.exe" OR process_name="rasphone.exe" OR process_name="rdpclip.exe" OR process_name="rdpinput.exe" OR process_name="rdrleakdiag.exe" OR process_name="recdisc.exe" OR process_name="recover.exe" OR process_name="reg.exe" OR process_name="regedt32.exe" OR process_name="regini.exe" OR process_name="regsvr32.exe" OR process_name="rekeywiz.exe" OR process_name="relog.exe" OR process_name="repair-bde.exe" OR process_name="replace.exe" OR process_name="reset.exe" OR process_name="resmon.exe" OR process_name="rmttpmvscmgrsvr.exe" OR process_name="rrinstaller.exe" OR process_name="rstrui.exe" OR process_name="runas.exe" OR process_name="rundll32.exe" OR process_name="runonce.exe" OR process_name="rwinsta.exe" OR process_name="sbunattend.exe" OR process_name="sc.exe" OR process_name="schtasks.exe" OR process_name="sdbinst.exe" OR process_name="sdchange.exe" OR process_name="sdclt.exe" OR process_name="sdiagnhost.exe" OR process_name="secinit.exe" OR process_name="services.exe" OR process_name="sessionmsg.exe" OR process_name="sethc.exe" OR process_name="setspn.exe" OR process_name="setupcl.exe" OR process_name="setupugc.exe" OR process_name="setx.exe" OR process_name="sfc.exe" OR process_name="shadow.exe" OR process_name="shrpubw.exe" OR process_name="shutdown.exe" OR process_name="sigverif.exe" OR process_name="sihost.exe" OR process_name="slui.exe" OR process_name="smss.exe" OR process_name="snmptrap.exe" OR process_name="sort.exe" OR process_name="spinstall.exe" OR process_name="spoolsv.exe" OR process_name="sppsvc.exe" OR process_name="spreview.exe" OR process_name="srdelayed.exe" OR process_name="subst.exe" OR process_name="svchost.exe" OR process_name="sxstrace.exe" OR process_name="syskey.exe" OR process_name="systeminfo.exe" OR process_name="systemreset.exe" OR process_name="systray.exe" OR process_name="tabcal.exe" OR process_name="takeown.exe" OR process_name="taskeng.exe" OR process_name="taskhost.exe" OR process_name="taskhostw.exe" OR process_name="taskkill.exe" OR process_name="tasklist.exe" OR process_name="taskmgr.exe" OR process_name="tcmsetup.exe" OR process_name="timeout.exe" OR process_name="tpmvscmgr.exe" OR process_name="tpmvscmgrsvr.exe"; +$cond_6 = | from $ssa_input | where process_name="tracerpt.exe" OR process_name="tscon.exe" OR process_name="tsdiscon.exe" OR process_name="tskill.exe" OR process_name="typeperf.exe" OR process_name="tzsync.exe" OR process_name="tzutil.exe" OR process_name="ucsvc.exe" OR process_name="unlodctr.exe" OR process_name="unregmp2.exe" OR process_name="upnpcont.exe" OR process_name="userinit.exe" OR process_name="vds.exe" OR process_name="vdsldr.exe" OR process_name="verclsid.exe" OR process_name="verifier.exe" OR process_name="verifiergui.exe" OR process_name="vmicsvc.exe" OR process_name="vssadmin.exe" OR process_name="w32tm.exe" OR process_name="waitfor.exe" OR process_name="wbadmin.exe" OR process_name="wbengine.exe" OR process_name="wecutil.exe" OR process_name="wermgr.exe" OR process_name="wevtutil.exe" OR process_name="wextract.exe" OR process_name="where.exe" OR process_name="whoami.exe" OR process_name="wiaacmgr.exe" OR process_name="wiawow64.exe" OR process_name="wifitask.exe" OR process_name="wimserv.exe" OR process_name="wininit.exe" OR process_name="winload.exe" OR process_name="winlogon.exe" OR process_name="winresume.exe" OR process_name="winrs.exe" OR process_name="winrshost.exe" OR process_name="winver.exe" OR process_name="wisptis.exe" OR process_name="wkspbroker.exe" OR process_name="wksprt.exe" OR process_name="wlanext.exe" OR process_name="wlrmdr.exe" OR process_name="wowreg32.exe" OR process_name="wpnpinst.exe" OR process_name="wpr.exe" OR process_name="write.exe" OR process_name="wscript.exe" OR process_name="wsmprovhost.exe" OR process_name="wsqmcons.exe" OR process_name="wuapihost.exe" OR process_name="wuapp.exe" OR process_name="wuauclt.exe" OR process_name="wusa.exe" OR process_name="xcopy.exe" OR process_name="xpsrchvw.exe" OR process_name="xwizard.exe"; +| from $cond_1 | union $cond_2 | union $cond_3 | union $cond_4 | union $cond_5 | union $cond_6 | where process_path!="c:\\windows\\system32" AND process_path!="c:\\windows\\syswow64" | eval start_time = timestamp, end_time = timestamp, entities = mvappend(device, user), body = "TBD" | into write_ssa_detected_events(); +``` +#### Associated Analytic Story + +#### How To Implement +Collect endpoint data such as sysmon or 4688 events. + +#### Required fields +* dest_device_id +* process_name +* _time +* dest_user_id +* process_path + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### System Processes Run From Unexpected Locations +This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1036.003 +- **Last Updated**: 2020-12-08 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name Processes.process_hash| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file` | `system_processes_run_from_unexpected_locations_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1036.003 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +None identified + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+ +===== ### TOR Traffic This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. @@ -15367,26 +15403,27 @@ None at this time _version_: 2 ---- -### Detect Large Outbound ICMP Packets -This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. + +===== +### USN Journal Deletion +The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1095 -- **Last Updated**: 2018-06-01 +- **ATT&CK**: T1070 +- **Last Updated**: 2018-12-03
View #### Search ``` -| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter` ``` #### Associated Analytic Story #### How To Implement -In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields @@ -15394,71 +15431,29 @@ In order to run this search effectively, we highly recommend that you leverage t | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1095 | x | x | +| T1070 | x | x | #### Kill Chain Phases -* Command and Control +* Actions on Objectives #### Known False Positives -ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list. +None identified #### References #### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/atomic_red_team/windows-sysmon.log _version_: 2
---- -### Detect SNICat SNI Exfiltration -This search looks for commands that the SNICat tool uses in the TLS SNI field. + +===== +### Uncommon Processes On Endpoint +This search looks for applications on the endpoint that you have marked as uncommon. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1041 -- **Last Updated**: 2020-10-21 - -
- View - -#### Search -``` -`zeek_ssl` | rex field=server_name "(?(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)" | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1041 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -Unknown - -#### References -* https://www.mnemonic.no/blog/introducing-snicat/ -* https://github.com/mnemonic-no/SNIcat -* https://attack.mitre.org/techniques/T1041/ - -#### Test Dataset - -_version_: 1 -
---- -### SMB Traffic Spike -This search looks for spikes in the number of Server Message Block (SMB) traffic connections. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1021.002 +- **ATT&CK**: T1204.002 - **Last Updated**: 2020-07-22
@@ -15466,12 +15461,12 @@ This search looks for spikes in the number of Server Message Block (SMB) traffic #### Search ``` -| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_on_endpoint_filter` ``` #### Associated Analytic Story #### How To Implement -This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. #### Required fields @@ -15479,73 +15474,28 @@ This search requires you to be ingesting your network traffic logs and populatin | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1021.002 | x | x | +| T1204.002 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -A file server may experience high-demand loads that could cause this analytic to trigger. +None identified #### References #### Test Dataset -_version_: 3 +_version_: 4
---- -### Detect Port Security Violation -By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. + +===== +### Unload Sysmon Filter Driver +Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1200, T1498, T1557.002 -- **Last Updated**: 2020-10-28 - -
- View - -#### Search -``` -`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_port_security_violation_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1200 | x | x | -| T1498 | x | x | -| T1557.002 | x | x | - -#### Kill Chain Phases -* Reconnaissance -* Delivery -* Exploitation -* Actions on Objectives - -#### Known False Positives -This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network. - -#### References - -#### Test Dataset - -_version_: 1 -
---- -### SMB Traffic Spike - MLTK -This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1021.002 +- **ATT&CK**: T1562.001 - **Last Updated**: 2020-07-22
@@ -15553,15 +15503,12 @@ This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the n #### Search ``` -| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count | `smb_traffic_spike___mltk_filter` +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` |`unload_sysmon_filter_driver_filter`| table firstTime lastTime dest user count process_name process_id parent_process_name process ``` #### Associated Analytic Story #### How To Implement -To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ -This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ -1. **Label:** Number of events, **Field:** count\ -Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. #### Required fields @@ -15569,40 +15516,42 @@ Detailed documentation on how to create a new field within Incident Review is fo | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1021.002 | x | x | +| T1562.001 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results + #### References #### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log _version_: 3
---- -### Remote Desktop Network Traffic -This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. + +===== +### Unsigned Image Loaded by LSASS +This search detects loading of unsigned images by LSASS. Deprecated because too noisy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1021.001 -- **Last Updated**: 2020-07-07 +- **ATT&CK**: T1003.001 +- **Last Updated**: 2019-12-06
View #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` +`sysmon` EventID=7 Image=*lsass.exe Signed=false | stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `unsigned_image_loaded_by_lsass_filter` ``` #### Associated Analytic Story #### How To Implement -To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. +This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. #### Required fields @@ -15610,82 +15559,42 @@ To successfully implement this search you need to identify systems that commonly | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1021.001 | x | x | +| T1003.001 | x | x | #### Kill Chain Phases * Actions on Objectives #### Known False Positives -Remote Desktop may be used legitimately by users on the network. +Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. #### References +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf #### Test Dataset -_version_: 3 +_version_: 1
---- -### DNS Query Length With High Standard Deviation -This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048.003 -- **Last Updated**: 2021-01-18 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query | `drop_dm_object_name("DNS")` | eval query_length = len(query) | table query query_length record_type count | eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2) | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter` -``` -#### Associated Analytic Story - -#### How To Implement -To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048.003 | x | x | - -#### Kill Chain Phases -* Command and Control - -#### Known False Positives -It's possible there can be long domain names that are legitimate. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log - -_version_: 3 -
---- -### Detect Unauthorized Assets by MAC address -By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. +===== +### Unsuccessful Netbackup backups +This search gives you the hosts where a backup was attempted and then failed. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: - **ATT&CK**: -- **Last Updated**: 2017-09-13 +- **Last Updated**: 2017-09-12
View #### Search ``` -| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac | dedup All_Sessions.dest_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` | search NOT [| inputlookup asset_lookup_by_str |rename mac as dest_mac | fields + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter` +`netbackup` | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature | `unsuccessful_netbackup_backups_filter` ``` #### Associated Analytic Story #### How To Implement -This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. +To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. #### Required fields @@ -15695,12 +15604,9 @@ This search uses the Network_Sessions data model shipped with Enterprise Securit | ----------- | ----------- |:-------------:| #### Kill Chain Phases -* Reconnaissance -* Delivery -* Actions on Objectives #### Known False Positives -This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. +None identified #### References @@ -15708,176 +15614,45 @@ This search might be prone to high false positives. Please consider this when co _version_: 1
---- -### Detect Outbound SMB Traffic -This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +===== +### Unusually Long Command Line +Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez. + +- **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1071.002 -- **Last Updated**: 2020-07-21 +- **ATT&CK**: +- **Last Updated**: 2020-10-06
View #### Search ``` -| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(start_time)` | `security_content_ctime(end_time)` | `detect_outbound_smb_traffic_filter` + | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) | where cmd_line!=null and dest_user_id!=null | eval cmd_line_norm=replace(cast(cmd_line, "string"), /\s(--?\w+)|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) | select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input | adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 | where label AND quantile>0.99 | first_time_event input_columns=["dest_device_id", "cmd_line"] | where first_time_dest_device_id_cmd_line | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); ``` #### Associated Analytic Story #### How To Implement -In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model +You must be ingesting sysmon endpoint data that monitors command lines. #### Required fields +* process_name +* _time +* dest_device_id +* dest_user_id +* process #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1071.002 | x | x | #### Kill Chain Phases * Actions on Objectives -* Command and Control #### Known False Positives -It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. - -#### References - -#### Test Dataset - -_version_: 3 -
---- -### Detect Windows DNS SIGRed via Zeek -This search detects SIGRed via Zeek DNS and Zeek Conn data. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1203 -- **Last Updated**: 2020-07-28 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id | rename DNS.flow_id as flow_id | append [| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id | rename All_Traffic.flow_id as flow_id] | `detect_windows_dns_sigred_via_zeek_filter` | stats count by flow_id | where count>1 | fields - count -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The Network Resolution and Network Traffic datamodels are in use for this search. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1203 | x | x | - -#### Kill Chain Phases -* Exploitation - -#### Known False Positives -unknown - -#### References -* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ - -#### Test Dataset - -_version_: 1 -
---- -### Detect hosts connecting to dynamic domain providers -Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1189 -- **Last Updated**: 2021-01-14 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter` -``` -#### Associated Analytic Story - -#### How To Implement -First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ -This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ -1. \ -1. **Label:** DNS Answer, **Field:** answer\ -1. \ -1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1189 | x | x | - -#### Kill Chain Phases -* Command and Control -* Actions on Objectives - -#### Known False Positives -Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log - -_version_: 3 -
---- -### Detect Traffic Mirroring -Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1200, T1498, T1020.001 -- **Last Updated**: 2020-10-28 - -
- View - -#### Search -``` -`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_traffic_mirroring_filter` -``` -#### Associated Analytic Story - -#### How To Implement -This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1200 | x | x | -| T1498 | x | x | -| T1020.001 | x | x | - -#### Kill Chain Phases -* Delivery -* Actions on Objectives - -#### Known False Positives -This search will return false positives for any legitimate traffic captures by network administrators. +This detection may flag suspiciously long command lines when there is not sufficient evidence (samples) for a given process that this detection is tracking; or when there is high variability in the length of the command line for the tracked process. Also, some legitimate applications may use long command lines. Such is the case of Ansible, that encodes Powershell scripts using long base64. Attackers may use this technique to obfuscate their payloads. #### References @@ -15885,7 +15660,91 @@ This search will return false positives for any legitimate traffic captures by n _version_: 1
---- + +===== +### Unusually Long Command Line +Command lines that are extremely long may be indicative of malicious activity on your hosts. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2020-12-08 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process | `unusually_long_command_line_filter` |eval threshold = 3 | where maxlen > ((threshold*stdevperhost) + avgperhost) +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the process field in the Endpoint data model. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Some legitimate applications start with long command lines. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + +_version_: 5 +
+ +===== +### Unusually Long Command Line - MLTK +Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2019-05-08 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime lastTime user dest process_name process processlen count | `unusually_long_command_line___mltk_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== ### Unusually Long Content-Type Length This search looks for unusually long strings in the Content-Type http header that the client sends the server. @@ -15925,26 +15784,27 @@ Very few legitimate Content-Type fields will have a length greater than 100 char _version_: 1 ---- -### Detect Rogue DHCP Server -By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). + +===== +### WBAdmin Delete System Backups +This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1200, T1498, T1557 -- **Last Updated**: 2020-08-11 +- **ATT&CK**: T1490 +- **Last Updated**: 2021-01-22
View #### Search ``` -`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_rogue_dhcp_server_filter` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `wbadmin_delete_system_backups_filter` ``` #### Associated Analytic Story #### How To Implement -This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. #### Required fields @@ -15952,44 +15812,46 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1200 | x | x | -| T1498 | x | x | -| T1557 | x | x | +| T1490 | x | x | #### Kill Chain Phases -* Reconnaissance -* Delivery * Actions on Objectives #### Known False Positives -This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface. +Administrators may modify the boot configuration. #### References +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md +* https://thedfirreport.com/2020/10/08/ryuks-return/ +* https://attack.mitre.org/techniques/T1490/ +* https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin #### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log _version_: 1
---- -### Detect IPv6 Network Infrastructure Threats -By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. + +===== +### WMI Permanent Event Subscription +This search looks for the creation of WMI permanent event subscriptions. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1200, T1498, T1557.002 -- **Last Updated**: 2020-10-28 +- **ATT&CK**: T1047 +- **Last Updated**: 2018-10-23
View #### Search ``` -`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") | eval src_interface=src_int_prefix_long+src_int_suffix | eval dest_interface=dest_int_prefix_long+dest_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface | table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detect_ipv6_network_infrastructure_threats_filter` +`wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | rename ComputerName as dest | `wmi_permanent_event_subscription_filter` ``` #### Associated Analytic Story #### How To Implement -This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. +To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. #### Required fields @@ -15997,52 +15859,41 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1200 | x | x | -| T1498 | x | x | -| T1557.002 | x | x | +| T1047 | x | x | #### Kill Chain Phases -* Reconnaissance -* Delivery * Actions on Objectives #### Known False Positives -None currently known +Although unlikely, administrators may use event subscriptions for legitimate purposes. #### References -* https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html #### Test Dataset _version_: 1
---- -### Detect Windows DNS SIGRed via Splunk Stream -This search detects SIGRed via Splunk Stream. + +===== +### WMI Permanent Event Subscription - Sysmon +This search looks for the creation of WMI permanent event subscriptions. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1203 -- **Last Updated**: 2020-07-28 +- **ATT&CK**: T1546.003 +- **Last Updated**: 2020-12-08
View #### Search ``` -`stream_dns` | spath "query_type{}" | search "query_type{}" IN (SIG,KEY) | spath protocol_stack | search protocol_stack="ip:tcp:dns" | append [search `stream_tcp` bytes_out>65000] | `detect_windows_dns_sigred_via_splunk_stream_filter` | stats count by flow_id | where count>1 | fields - count +`sysmon` EventCode=21 | rename host as dest | table _time, dest, user, Operation, EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter` ``` #### Associated Analytic Story #### How To Implement -You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. +To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. #### Required fields @@ -16050,83 +15901,42 @@ You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1203 | x | x | +| T1546.003 | x | x | #### Kill Chain Phases -* Exploitation +* Actions on Objectives #### Known False Positives -unknown - -#### References -* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ - -#### Test Dataset - -_version_: 1 -
---- -### Remote Desktop Network Bruteforce -This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1021.001 -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev | `remote_desktop_network_bruteforce_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must ensure that your network traffic data is populating the Network_Traffic data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.001 | x | x | - -#### Kill Chain Phases -* Reconnaissance -* Delivery - -#### Known False Positives -RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. +Although unlikely, administrators may use event subscriptions for legitimate purposes. #### References #### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log _version_: 2
---- -### Detect Software Download To Network Device -Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. + +===== +### WMI Temporary Event Subscription +This search looks for the creation of WMI temporary event subscriptions. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1542.005 -- **Last Updated**: 2020-10-28 +- **ATT&CK**: T1047 +- **Last Updated**: 2018-10-23
View #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter` +`wmi` EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?[^;|^$]+)" | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter` ``` #### Associated Analytic Story #### How To Implement -This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. +To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. #### Required fields @@ -16134,13 +15944,13 @@ This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -| T1542.005 | x | x | +| T1047 | x | x | #### Kill Chain Phases -* Delivery +* Actions on Objectives #### Known False Positives -This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices. +Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. #### References @@ -16148,7 +15958,52 @@ This search will also report any legitimate attempts of software downloads to ne _version_: 1
---- + +===== +### Web Fraud - Account Harvesting +This search is used to identify the creation of multiple user accounts using the same email domain name. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1136 +- **Last Updated**: 2018-10-08 + +
+ View + +#### Search +``` +`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" | rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^&|^$]+)" | search Username=* | rex field=Username "@(?.*)" | stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain | where UniqueUsernames> 25 | `web_fraud___account_harvesting_filter` +``` +#### Associated Analytic Story + +#### How To Implement +We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1136 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. + +#### References +* https://splunkbase.splunk.com/app/2734/ +* https://splunkbase.splunk.com/app/1809/ + +#### Test Dataset + +_version_: 1 +
+ +===== ### Web Fraud - Anomalous User Clickspeed This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. @@ -16193,50 +16048,8 @@ As is common with many fraud-related searches, we are usually looking to attribu _version_: 1 ---- -### Web Fraud - Account Harvesting -This search is used to identify the creation of multiple user accounts using the same email domain name. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1136 -- **Last Updated**: 2018-10-08 - -
- View - -#### Search -``` -`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" | rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^&|^$]+)" | search Username=* | rex field=Username "@(?.*)" | stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain | where UniqueUsernames> 25 | `web_fraud___account_harvesting_filter` -``` -#### Associated Analytic Story - -#### How To Implement -We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. - -#### References -* https://splunkbase.splunk.com/app/2734/ -* https://splunkbase.splunk.com/app/1809/ - -#### Test Dataset - -_version_: 1 -
---- +===== ### Web Fraud - Password Sharing Across Accounts This search is used to identify user accounts that share a common password. @@ -16279,4 +16092,558 @@ As is common with many fraud-related searches, we are usually looking to attribu _version_: 1 ---- + +===== +### Web Servers Executing Suspicious Processes +This search looks for suspicious processes on all systems labeled as web servers. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1082 +- **Last Updated**: 2019-04-01 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1082 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Windows AdFind Exe +This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1018 +- **Last Updated**: 2020-12-16 + +
+ View + +#### Search +``` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_adfind_exe_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting logs with the process name, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1018 | x | x | + +#### Kill Chain Phases +* Exploitation + +#### Known False Positives +administrators rarely use adfind, usually not used for legitimate reasons + +#### References +* https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/ +* https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log + +_version_: 1 +
+ +===== +### Windows DisableAntiSpyware Registry +The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1562.001 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1562.001 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +It is unusual to turn this feature on a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Windows Event Log Cleared +This search looks for Windows events that indicate one of the Windows event logs has been purged. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1070.001 +- **Last Updated**: 2020-07-06 + +
+ View + +#### Search +``` +(`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_log_cleared_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you need to be ingesting Windows event logs from your hosts. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1070.001 | x | x | + +#### Kill Chain Phases +* Actions on Objectives + +#### Known False Positives +It is possible that these logs may be legitimately cleared by Administrators. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-security.log +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-system.log + +_version_: 4 +
+ +===== +### Windows Security Account Manager Stopped +The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1489 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_security_account_manager_stopped_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1489 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified. + +#### References + +#### Test Dataset +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log + +_version_: 1 +
+ +===== +### Windows connhost exe started forcefully +The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1059.003 +- **Last Updated**: 2020-11-06 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_connhost_exe_started_forcefully_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1059.003 | x | x | + +#### Kill Chain Phases +* Delivery + +#### Known False Positives +This process should not be ran forcefully, we have not see any false positives for this detection + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### Windows hosts file modification +The search looks for modifications to the hosts file on all Windows endpoints across your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: +- **Last Updated**: 2018-11-02 + +
+ View + +#### Search +``` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* | `drop_dm_object_name(Filesystem)` | `windows_hosts_file_modification_filter` +``` +#### Associated Analytic Story + +#### How To Implement +To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| + +#### Kill Chain Phases +* Command and Control + +#### Known False Positives +There may be legitimate reasons for system administrators to add entries to this file. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### aws detect attach to role policy +This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-07-27 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` attach policy| spath requestParameters.policyArn | table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate | `aws_detect_attach_to_role_policy_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### aws detect permanent key creation +This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-07-27 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` CreateAccessKey | spath eventName | search eventName=CreateAccessKey "userIdentity.type"=IAMUser | table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId |`aws_detect_permanent_key_creation_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### aws detect role creation +This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-07-27 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* | table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate | `aws_detect_role_creation_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### aws detect sts assume role abuse +This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-07-27 + +
+ View + +#### Search +``` +`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role | table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate | `aws_detect_sts_assume_role_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### aws detect sts get session token abuse +This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1550 +- **Last Updated**: 2020-07-27 + +
+ View + +#### Search +``` +`aws_cloudwatchlogs_eks` ASIA userIdentity.type=IAMUser| spath eventName | search eventName=GetSessionToken | table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region | `aws_detect_sts_get_session_token_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1550 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used. + +#### References + +#### Test Dataset + +_version_: 1 +
+ +===== +### gcp detect oauth token abuse +This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: +- **ATT&CK**: T1078 +- **Last Updated**: 2020-09-01 + +
+ View + +#### Search +``` +`google_gcp_pubsub_message` type.googleapis.com/google.cloud.audit.AuditLog |table protoPayload.@type protoPayload.status.details{}.@type protoPayload.status.details{}.violations{}.callerIp protoPayload.status.details{}.violations{}.type protoPayload.status.message | `gcp_detect_oauth_token_abuse_filter` +``` +#### Associated Analytic Story + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required fields + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1078 | x | x | + +#### Kill Chain Phases +* Lateral Movement + +#### Known False Positives +GCP Oauth token abuse detection will only work if there are access policies in place along with audit logs. + +#### References +* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-1 +* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-2 + +#### Test Dataset + +_version_: 1 +
+ +===== From 46d9f30f852779d2bee430a0aa72d6944bf17626 Mon Sep 17 00:00:00 2001 From: divious1 Date: Thu, 25 Feb 2021 01:20:16 -0500 Subject: [PATCH 04/62] fixing divider --- bin/jinja2_templates/doc_detections_markdown.j2 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bin/jinja2_templates/doc_detections_markdown.j2 b/bin/jinja2_templates/doc_detections_markdown.j2 index 31b665e9bf..1ac8e56336 100644 --- a/bin/jinja2_templates/doc_detections_markdown.j2 +++ b/bin/jinja2_templates/doc_detections_markdown.j2 @@ -118,5 +118,5 @@ All the detections shipped to different Splunk products. Below is a breakdown by _version_: {{detection.version}} -===== +--- {% endfor %} From d3a983d0a5d05f8fbf6d3b2648881b6a2bdeb090 Mon Sep 17 00:00:00 2001 From: divious1 Date: Thu, 25 Feb 2021 01:21:52 -0500 Subject: [PATCH 05/62] added example --- docs/detections.md | 734 ++++++++++++++++++++++----------------------- 1 file changed, 367 insertions(+), 367 deletions(-) diff --git a/docs/detections.md b/docs/detections.md index 575b00dc50..bdcbe0f18e 100644 --- a/docs/detections.md +++ b/docs/detections.md @@ -388,7 +388,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif _version_: 1 -===== +--- ### AWS Cloud Provisioning From Previously Unseen Country This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. @@ -430,7 +430,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif _version_: 1 -===== +--- ### AWS Cloud Provisioning From Previously Unseen IP Address This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. @@ -471,7 +471,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif _version_: 1 -===== +--- ### AWS Cloud Provisioning From Previously Unseen Region This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. @@ -513,7 +513,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif _version_: 1 -===== +--- ### AWS Cross Account Activity From Previously Unseen Account This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. This search is deprecated and have been translated to use the latest Authentication Datamodel. @@ -555,7 +555,7 @@ Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicio _version_: 1 -===== +--- ### AWS Detect Users creating keys with encrypt policy without MFA This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company. @@ -600,7 +600,7 @@ unknown _version_: 1 -===== +--- ### AWS Detect Users with KMS keys performing encryption S3 This search provides detection of users with KMS keys performing encryption specifically against S3 buckets. @@ -645,7 +645,7 @@ bucket with S3 encryption _version_: 1 -===== +--- ### AWS EKS Kubernetes cluster sensitive object access This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets @@ -686,7 +686,7 @@ Sensitive object access is not necessarily malicious but user and object context _version_: 1 -===== +--- ### AWS Network Access Control List Created with All Open Ports The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. @@ -729,7 +729,7 @@ It's possible that an admin has created this ACL with all ports open for some le _version_: 2 -===== +--- ### AWS Network Access Control List Deleted Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. @@ -772,7 +772,7 @@ It's possible that a user has legitimately deleted a network ACL. _version_: 2 -===== +--- ### AWS SAML Access by Provider User and Principal This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider. @@ -818,7 +818,7 @@ Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very diff _version_: 1 -===== +--- ### AWS SAML Update identity provider This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker. @@ -864,7 +864,7 @@ Updating a SAML provider or creating a new one may not necessarily be malicious _version_: 1 -===== +--- ### Abnormally High AWS Instances Launched by User This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel @@ -906,7 +906,7 @@ Many service accounts configured within an AWS infrastructure are known to exhib _version_: 2 -===== +--- ### Abnormally High AWS Instances Launched by User - MLTK This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -948,7 +948,7 @@ Many service accounts configured within an AWS infrastructure are known to exhib _version_: 2 -===== +--- ### Abnormally High AWS Instances Terminated by User This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -990,7 +990,7 @@ Many service accounts configured with your AWS infrastructure are known to exhib _version_: 2 -===== +--- ### Abnormally High AWS Instances Terminated by User - MLTK This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -1032,7 +1032,7 @@ Many service accounts configured within an AWS infrastructure are known to exhib _version_: 2 -===== +--- ### Abnormally High Number Of Cloud Infrastructure API Calls This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user. @@ -1075,7 +1075,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base _version_: 1 -===== +--- ### Abnormally High Number Of Cloud Instances Destroyed This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. @@ -1117,7 +1117,7 @@ Many service accounts configured within a cloud infrastructure are known to exhi _version_: 1 -===== +--- ### Abnormally High Number Of Cloud Instances Launched This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. @@ -1159,7 +1159,7 @@ Many service accounts configured within an AWS infrastructure are known to exhib _version_: 2 -===== +--- ### Abnormally High Number Of Cloud Security Group API Calls This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user. @@ -1202,7 +1202,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base _version_: 1 -===== +--- ### Access LSASS Memory for Dump Creation Detect memory dumping of the LSASS process. @@ -1246,7 +1246,7 @@ Administrators can create memory dumps for debugging purposes, but memory dumps _version_: 2 -===== +--- ### Amazon EKS Kubernetes Pod scan detection This search provides detection information on unauthenticated requests against Kubernetes' Pods API @@ -1288,7 +1288,7 @@ Not all unauthenticated requests are malicious, but frequency, UA and source IPs _version_: 1 -===== +--- ### Amazon EKS Kubernetes cluster scan detection This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS @@ -1330,7 +1330,7 @@ Not all unauthenticated requests are malicious, but frequency, UA and source IPs _version_: 1 -===== +--- ### Applying Stolen Credentials via Mimikatz modules This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack. @@ -1390,7 +1390,7 @@ None identified. _version_: 1 -===== +--- ### Applying Stolen Credentials via PowerSploit modules Stolen credentials are applied by methods such as user impersonation, credential injection, spoofing of authentication processes or getting hold of critical accounts. This detection indicates such activities carried out by PowerSploit exploit kit APIs. @@ -1449,7 +1449,7 @@ None identified. _version_: 1 -===== +--- ### Assessment of Credential Strength via DSInternals modules This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised. @@ -1503,7 +1503,7 @@ None identified. _version_: 1 -===== +--- ### Attempt To Add Certificate To Untrusted Store Attempt to add a certificate to the certificate store @@ -1547,7 +1547,7 @@ There may be legitimate reasons for administrators to add a certificate to the u _version_: 6 -===== +--- ### Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. @@ -1591,7 +1591,7 @@ Administrators may attempt to change the default execution policy on a system fo _version_: 6 -===== +--- ### Attempt To Stop Security Service This search looks for attempts to stop security-related services on the endpoint. @@ -1635,7 +1635,7 @@ None identified. Attempts to disable security-related services should be identif _version_: 3 -===== +--- ### Attempted Credential Dump From Registry via Reg exe Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. @@ -1678,7 +1678,7 @@ None identified. _version_: 4 -===== +--- ### Attempted Credential Dump From Registry via Reg exe Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. @@ -1726,7 +1726,7 @@ None identified. _version_: 1 -===== +--- ### BCDEdit Failure Recovery Modification This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. @@ -1770,7 +1770,7 @@ Administrators may modify the boot configuration. _version_: 1 -===== +--- ### Batch File Write to System32 The search looks for a batch file (.bat) written to the Windows system directory tree. @@ -1813,7 +1813,7 @@ It is possible for this search to generate a notable event for a batch file writ _version_: 1 -===== +--- ### Certutil exe certificate extraction This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS. @@ -1855,7 +1855,7 @@ Unless there are specific use cases, manipulating or exporting certificates usin _version_: 1 -===== +--- ### Child Processes of Spoolsv exe This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. @@ -1897,7 +1897,7 @@ Some legitimate printer-related processes may show up as children of spoolsv.exe _version_: 3 -===== +--- ### Clients Connecting to Multiple DNS Servers This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. @@ -1941,7 +1941,7 @@ It's possible that an enterprise has more than five DNS servers that are configu _version_: 3 -===== +--- ### Cloud API Calls From Previously Unseen User Roles This search looks for new commands from each user role. @@ -1983,7 +1983,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. _version_: 1 -===== +--- ### Cloud Compute Instance Created By Previously Unseen User This search looks for cloud compute instances created by users who have not created them before. @@ -2025,7 +2025,7 @@ It's possible that a user will start to create compute instances for the first t _version_: 1 -===== +--- ### Cloud Compute Instance Created In Previously Unused Region This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. @@ -2068,7 +2068,7 @@ It's possible that a user has unknowingly started an instance in a new region. P _version_: 1 -===== +--- ### Cloud Compute Instance Created With Previously Unseen Image This search looks for cloud compute instances being created with previously unseen image IDs. @@ -2109,7 +2109,7 @@ After a new image is created, the first systems created with that image will cau _version_: 1 -===== +--- ### Cloud Compute Instance Created With Previously Unseen Instance Type Find EC2 instances being created with previously unseen instance types. @@ -2150,7 +2150,7 @@ It is possible that an admin will create a new system using a new instance type _version_: 1 -===== +--- ### Cloud Instance Modified By Previously Unseen User This search looks for cloud instances being modified by users who have not previously modified them. @@ -2192,7 +2192,7 @@ It's possible that a new user will start to modify EC2 instances when they haven _version_: 1 -===== +--- ### Cloud Network Access Control List Deleted Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate @@ -2233,7 +2233,7 @@ It's possible that a user has legitimately deleted a network ACL. _version_: 1 -===== +--- ### Cloud Provisioning Activity From Previously Unseen City This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. @@ -2276,7 +2276,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif _version_: 1 -===== +--- ### Cloud Provisioning Activity From Previously Unseen Country This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. @@ -2319,7 +2319,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif _version_: 1 -===== +--- ### Cloud Provisioning Activity From Previously Unseen IP Address This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. @@ -2362,7 +2362,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif _version_: 1 -===== +--- ### Cloud Provisioning Activity From Previously Unseen Region This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. @@ -2405,7 +2405,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif _version_: 1 -===== +--- ### Common Ransomware Extensions The search looks for file modifications with extensions commonly used by Ransomware @@ -2452,7 +2452,7 @@ It is possible for a legitimate file with these extensions to be created. If thi _version_: 4 -===== +--- ### Common Ransomware Notes The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. @@ -2495,7 +2495,7 @@ It's possible that a legitimate file could be created with the same name used by _version_: 4 -===== +--- ### Create Remote Thread into LSASS Detect remote thread creation into LSASS consistent with credential dumping. @@ -2539,7 +2539,7 @@ Other tools can access LSASS for legitimate reasons and generate an event. In th _version_: 1 -===== +--- ### Create local admin accounts using net exe This search looks for the creation of local administrator accounts using net.exe. @@ -2584,7 +2584,7 @@ Administrators often leverage net.exe to create admin accounts. _version_: 4 -===== +--- ### Create or delete windows shares using net exe This search looks for the creation or deletion of hidden shares using net.exe. @@ -2628,7 +2628,7 @@ Administrators often leverage net.exe to create or delete network shares. You sh _version_: 5 -===== +--- ### Creation of Shadow Copy Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. @@ -2672,7 +2672,7 @@ Legitimate administrator usage of Vssadmin or Wmic will create false positives. _version_: 1 -===== +--- ### Creation of Shadow Copy with wmic and powershell This search detects the use of wmic and Powershell to create a shadow copy. @@ -2716,7 +2716,7 @@ Legtimate administrator usage of wmic to create a shadow copy. _version_: 1 -===== +--- ### Creation of lsass Dump with Taskmgr Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp. @@ -2762,7 +2762,7 @@ Administrators can create memory dumps for debugging purposes, but memory dumps _version_: 1 -===== +--- ### Credential Dumping via Copy Command from Shadow Copy This search detects credential dumping using copy command from a shadow copy. @@ -2806,7 +2806,7 @@ unknown _version_: 1 -===== +--- ### Credential Dumping via Symlink to Shadow Copy This search detects the creation of a symlink to a shadow copy. @@ -2850,7 +2850,7 @@ unknown _version_: 1 -===== +--- ### Credential Extraction indicative of FGDump and CacheDump with s option Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. @@ -2901,7 +2901,7 @@ None identified. _version_: 1 -===== +--- ### Credential Extraction indicative of FGDump and CacheDump with v option Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. @@ -2951,7 +2951,7 @@ None identified. _version_: 1 -===== +--- ### Credential Extraction indicative of Lazagne command line options Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc. @@ -3000,7 +3000,7 @@ None identified. _version_: 1 -===== +--- ### Credential Extraction indicative of use of DSInternals credential conversion modules Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. @@ -3052,7 +3052,7 @@ None identified. _version_: 1 -===== +--- ### Credential Extraction indicative of use of DSInternals modules Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. @@ -3104,7 +3104,7 @@ None identified. _version_: 1 -===== +--- ### Credential Extraction indicative of use of Mimikatz modules Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits. @@ -3153,7 +3153,7 @@ None identified. _version_: 1 -===== +--- ### Credential Extraction indicative of use of PowerSploit modules Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits. @@ -3202,7 +3202,7 @@ None identified. _version_: 1 -===== +--- ### Credential Extraction native Microsoft debuggers peek into the kernel Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. @@ -3253,7 +3253,7 @@ Although unlikely, using debuggers this way may be indicative of developers anal _version_: 1 -===== +--- ### Credential Extraction native Microsoft debuggers via z command line option Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. @@ -3302,7 +3302,7 @@ Although unlikely, using debuggers this way may be indicative of developers anal _version_: 1 -===== +--- ### Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments. @@ -3350,7 +3350,7 @@ None identified. _version_: 1 -===== +--- ### DNS Query Length Outliers - MLTK This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. @@ -3398,7 +3398,7 @@ If you are seeing more results than desired, you may consider reducing the value _version_: 2 -===== +--- ### DNS Query Length With High Standard Deviation This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. @@ -3441,7 +3441,7 @@ It's possible there can be long domain names that are legitimate. _version_: 3 -===== +--- ### DNS Query Requests Resolved by Unauthorized DNS Servers This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. @@ -3483,7 +3483,7 @@ Legitimate DNS activity can be detected in this search. Investigate, verify and _version_: 3 -===== +--- ### DNS record changed The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. @@ -3529,7 +3529,7 @@ Legitimate DNS changes can be detected in this search. Investigate, verify and u _version_: 3 -===== +--- ### Deleting Shadow Copies The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. @@ -3572,7 +3572,7 @@ vssadmin.exe and wmic.exe are standard applications shipped with modern versions _version_: 4 -===== +--- ### Detect API activity from users without MFA This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. @@ -3618,7 +3618,7 @@ Many service accounts configured within an AWS infrastructure do not have multi _version_: 1 -===== +--- ### Detect ARP Poisoning By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. @@ -3664,7 +3664,7 @@ This search might be prone to high false positives if DHCP Snooping or ARP inspe _version_: 1 -===== +--- ### Detect AWS API Activities From Unapproved Accounts This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. @@ -3712,7 +3712,7 @@ It's likely that you'll find activity detected by users/service accounts that ar _version_: 2 -===== +--- ### Detect AWS Console Login by New User This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour @@ -3754,7 +3754,7 @@ When a legitimate new user logins for the first time, this activity will be dete _version_: 1 -===== +--- ### Detect AWS Console Login by User from New City This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour @@ -3797,7 +3797,7 @@ When a legitimate new user logins for the first time, this activity will be dete _version_: 1 -===== +--- ### Detect AWS Console Login by User from New Country This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour @@ -3840,7 +3840,7 @@ When a legitimate new user logins for the first time, this activity will be dete _version_: 1 -===== +--- ### Detect AWS Console Login by User from New Region This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour @@ -3883,7 +3883,7 @@ When a legitimate new user logins for the first time, this activity will be dete _version_: 1 -===== +--- ### Detect Activity Related to Pass the Hash Attacks This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. @@ -3926,7 +3926,7 @@ Legitimate logon activity by authorized NTLM systems may be detected by this sea _version_: 5 -===== +--- ### Detect Baron Samedit CVE-2021-3156 This search detects the heap-based buffer overflow of sudoedit @@ -3969,7 +3969,7 @@ unknown _version_: 1 -===== +--- ### Detect Baron Samedit CVE-2021-3156 Segfault This search detects the heap-based buffer overflow of sudoedit @@ -4012,7 +4012,7 @@ If sudoedit is throwing segfaults for other reasons this will pick those up too. _version_: 1 -===== +--- ### Detect Baron Samedit CVE-2021-3156 via OSQuery This search detects the heap-based buffer overflow of sudoedit @@ -4055,7 +4055,7 @@ unknown _version_: 1 -===== +--- ### Detect Computer Changed with Anonymous Account This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. @@ -4098,7 +4098,7 @@ None thus far found _version_: 1 -===== +--- ### Detect Credential Dumping through LSASS access This search looks for reading lsass memory consistent with credential dumping. @@ -4141,7 +4141,7 @@ The activity may be legitimate. Other tools can access lsass for legitimate reas _version_: 3 -===== +--- ### Detect DNS requests to Phishing Sites leveraging EvilGinx2 This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. @@ -4188,7 +4188,7 @@ If a known good domain is not listed in the legit_domains.csv file, then the sea _version_: 2 -===== +--- ### Detect Dump LSASS Memory using comsvcs This search detects the memory of lsass.exe being dumped for offline credential theft attack. @@ -4236,7 +4236,7 @@ None identified. _version_: 1 -===== +--- ### Detect Excessive Account Lockouts From Endpoint This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. @@ -4283,7 +4283,7 @@ It's possible that a widely used system, such as a kiosk, could cause a large nu _version_: 5 -===== +--- ### Detect Excessive User Account Lockouts This search detects user accounts that have been locked out a relatively high number of times in a short period. @@ -4326,7 +4326,7 @@ It is possible that a legitimate user is experiencing an issue causing multiple _version_: 3 -===== +--- ### Detect F5 TMUI RCE CVE-2020-5902 This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices @@ -4371,7 +4371,7 @@ unknown _version_: 1 -===== +--- ### Detect GCP Storage access from a new IP This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. @@ -4413,7 +4413,7 @@ GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow i _version_: 1 -===== +--- ### Detect HTML Help Renamed The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. @@ -4459,7 +4459,7 @@ Although unlikely a renamed instance of hh.exe will be used legitimately, filter _version_: 1 -===== +--- ### Detect HTML Help Spawn Child Process The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. @@ -4507,7 +4507,7 @@ Although unlikely, some legitimate applications (ex. web browsers) may spawn a c _version_: 1 -===== +--- ### Detect HTML Help URL in Command Line The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. @@ -4556,7 +4556,7 @@ Although unlikely, some legitimate applications may retrieve a CHM remotely, fil _version_: 1 -===== +--- ### Detect HTML Help Using InfoTech Storage Handlers The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. @@ -4605,7 +4605,7 @@ It is rare to see instances of InfoTech Storage Handlers being used, but it does _version_: 1 -===== +--- ### Detect IPv6 Network Infrastructure Threats By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. @@ -4659,7 +4659,7 @@ None currently known _version_: 1 -===== +--- ### Detect Kerberoasting This search detects a potential kerberoasting attack via service principal name requests @@ -4708,7 +4708,7 @@ Older systems that support kerberos RC4 by default NetApp may generate false pos _version_: 1 -===== +--- ### Detect Large Outbound ICMP Packets This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. @@ -4750,7 +4750,7 @@ ICMP packets are used in a variety of ways to help troubleshoot networking issue _version_: 2 -===== +--- ### Detect Long DNS TXT Record Response This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. @@ -4792,7 +4792,7 @@ It's possible that legitimate TXT record responses can be long enough to trigger _version_: 2 -===== +--- ### Detect MSHTA Url in Command Line This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. @@ -4838,7 +4838,7 @@ It is possible legitimate applications may perform this behavior and will need t _version_: 1 -===== +--- ### Detect Mimikatz Using Loaded Images This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code. @@ -4881,7 +4881,7 @@ Other tools can import the same DLLs. These tools should be part of a whitelist. _version_: 1 -===== +--- ### Detect Mimikatz Via PowerShell And EventCode 4703 This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. @@ -4923,7 +4923,7 @@ The activity may be legitimate. PowerShell is often used by administrators to pe _version_: 2 -===== +--- ### Detect New Local Admin account This search looks for newly created accounts that have been elevated to local administrators. @@ -4969,7 +4969,7 @@ The activity may be legitimate. For this reason, it's best to verify the account _version_: 2 -===== +--- ### Detect New Login Attempts to Routers The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. @@ -5010,7 +5010,7 @@ Legitimate router connections may appear as new connections _version_: 1 -===== +--- ### Detect New Open GCP Storage Buckets This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. @@ -5052,7 +5052,7 @@ While this search has no known false positives, it is possible that a GCP admin _version_: 1 -===== +--- ### Detect New Open S3 Buckets over AWS CLI This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli. @@ -5095,7 +5095,7 @@ While this search has no known false positives, it is possible that an AWS admin _version_: 1 -===== +--- ### Detect New Open S3 buckets This search looks for CloudTrail events where a user has created an open/public S3 bucket. @@ -5138,7 +5138,7 @@ While this search has no known false positives, it is possible that an AWS admin _version_: 2 -===== +--- ### Detect Oulook exe writing a zip file This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. @@ -5181,7 +5181,7 @@ It is not uncommon for outlook to write legitimate zip files to the disk. _version_: 3 -===== +--- ### Detect Outbound SMB Traffic This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. @@ -5224,7 +5224,7 @@ It is likely that the outbound Server Message Block (SMB) traffic is legitimate, _version_: 3 -===== +--- ### Detect Pass the Hash This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique. @@ -5273,7 +5273,7 @@ Legitimate logon activity by authorized NTLM systems may be detected by this sea _version_: 1 -===== +--- ### Detect Path Interception By Creation Of program exe The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. @@ -5317,7 +5317,7 @@ unknown _version_: 3 -===== +--- ### Detect Port Security Violation By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. @@ -5364,7 +5364,7 @@ This search might be prone to high false positives if you have malfunctioning de _version_: 1 -===== +--- ### Detect Prohibited Applications Spawning cmd exe This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. @@ -5407,7 +5407,7 @@ There are circumstances where an application may legitimately execute and intera _version_: 5 -===== +--- ### Detect Prohibited Applications Spawning cmd exe This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. This is a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe` by @bpatel. @@ -5457,7 +5457,7 @@ There are circumstances where an application may legitimately execute and intera _version_: 1 -===== +--- ### Detect PsExec With accepteula Flag This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. @@ -5500,7 +5500,7 @@ Administrators can leverage PsExec for accessing remote systems and might pass ` _version_: 3 -===== +--- ### Detect Rare Executables This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. @@ -5543,7 +5543,7 @@ Some legitimate processes may be only rarely executed in your environment. As th _version_: 5 -===== +--- ### Detect Regasm Spawning a Process The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. @@ -5590,7 +5590,7 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa _version_: 1 -===== +--- ### Detect Regasm with Network Connection The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. @@ -5636,7 +5636,7 @@ Although unlikely, limited instances of regasm.exe with a network connection may _version_: 1 -===== +--- ### Detect Regasm with no Command Line Arguments The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. @@ -5682,7 +5682,7 @@ Although unlikely, limited instances of regasm.exe or may cause a false positive _version_: 1 -===== +--- ### Detect Regsvcs Spawning a Process The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. @@ -5728,7 +5728,7 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa _version_: 1 -===== +--- ### Detect Regsvcs with Network Connection The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. @@ -5774,7 +5774,7 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive. _version_: 1 -===== +--- ### Detect Regsvcs with No Command Line Arguments The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. @@ -5820,7 +5820,7 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive. _version_: 1 -===== +--- ### Detect Regsvr32 Application Control Bypass Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. @@ -5868,7 +5868,7 @@ Limited false positives related to third party software registering .DLL's. _version_: 1 -===== +--- ### Detect Rogue DHCP Server By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). @@ -5914,7 +5914,7 @@ This search might be prone to high false positives if DHCP Snooping has been inc _version_: 1 -===== +--- ### Detect Rundll32 Application Control Bypass - advpack The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. @@ -5962,7 +5962,7 @@ Although unlikely, some legitimate applications may use advpack.dll or ieadvpack _version_: 1 -===== +--- ### Detect Rundll32 Application Control Bypass - setupapi The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. @@ -6010,7 +6010,7 @@ Although unlikely, some legitimate applications may use setupapi triggering a fa _version_: 1 -===== +--- ### Detect Rundll32 Application Control Bypass - syssetup The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. @@ -6058,7 +6058,7 @@ Although unlikely, some legitimate applications may use syssetup.dll, triggering _version_: 1 -===== +--- ### Detect Rundll32 Inline HTA Execution The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. @@ -6104,7 +6104,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg _version_: 1 -===== +--- ### Detect S3 access from a new IP This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. @@ -6146,7 +6146,7 @@ S3 buckets can be accessed from any IP, as long as it can make a successful conn _version_: 1 -===== +--- ### Detect SNICat SNI Exfiltration This search looks for commands that the SNICat tool uses in the TLS SNI field. @@ -6191,7 +6191,7 @@ Unknown _version_: 1 -===== +--- ### Detect Software Download To Network Device Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. @@ -6233,7 +6233,7 @@ This search will also report any legitimate attempts of software downloads to ne _version_: 1 -===== +--- ### Detect Spike in AWS API Activity This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -6281,7 +6281,7 @@ Detailed documentation on how to create a new field within Incident Review may b _version_: 2 -===== +--- ### Detect Spike in AWS Security Hub Alerts for EC2 Instance This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals @@ -6322,7 +6322,7 @@ None _version_: 3 -===== +--- ### Detect Spike in AWS Security Hub Alerts for User This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. @@ -6362,7 +6362,7 @@ None _version_: 3 -===== +--- ### Detect Spike in Network ACL Activity This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -6404,7 +6404,7 @@ The false-positive rate may vary based on the values of`dataPointThreshold` and _version_: 1 -===== +--- ### Detect Spike in S3 Bucket deletion This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. @@ -6446,7 +6446,7 @@ Based on the values of`dataPointThreshold` and `deviationThreshold`, the false p _version_: 1 -===== +--- ### Detect Spike in Security Group Activity This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -6488,7 +6488,7 @@ Based on the values of`dataPointThreshold` and `deviationThreshold`, the false p _version_: 1 -===== +--- ### Detect Spike in blocked Outbound Traffic from your AWS This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. @@ -6530,7 +6530,7 @@ The false-positive rate may vary based on the values of`dataPointThreshold` and _version_: 1 -===== +--- ### Detect Traffic Mirroring Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. @@ -6575,7 +6575,7 @@ This search will return false positives for any legitimate traffic captures by n _version_: 1 -===== +--- ### Detect USB device insertion The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. @@ -6617,7 +6617,7 @@ Legitimate USB activity will also be detected. Please verify and investigate as _version_: 1 -===== +--- ### Detect Unauthorized Assets by MAC address By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. @@ -6660,7 +6660,7 @@ This search might be prone to high false positives. Please consider this when co _version_: 1 -===== +--- ### Detect Use of cmd exe to Launch Script Interpreters This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine @@ -6703,7 +6703,7 @@ Some legitimate applications may exhibit this behavior. _version_: 4 -===== +--- ### Detect Windows DNS SIGRed via Splunk Stream This search detects SIGRed via Splunk Stream. @@ -6746,7 +6746,7 @@ unknown _version_: 1 -===== +--- ### Detect Windows DNS SIGRed via Zeek This search detects SIGRed via Zeek DNS and Zeek Conn data. @@ -6789,7 +6789,7 @@ unknown _version_: 1 -===== +--- ### Detect Zerologon via Zeek This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC @@ -6834,7 +6834,7 @@ unknown _version_: 1 -===== +--- ### Detect attackers scanning for vulnerable JBoss servers This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. @@ -6876,7 +6876,7 @@ It's possible for legitimate HTTP requests to be made to URLs containing the sus _version_: 1 -===== +--- ### Detect hosts connecting to dynamic domain providers Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. @@ -6926,7 +6926,7 @@ Some users and applications may leverage Dynamic DNS to reach out to some domain _version_: 3 -===== +--- ### Detect malicious requests to exploit JBoss servers This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. @@ -6967,7 +6967,7 @@ No known false positives for this detection. _version_: 1 -===== +--- ### Detect mshta inline hta execution The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. @@ -7013,7 +7013,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg _version_: 5 -===== +--- ### Detect mshta renamed The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta. @@ -7058,7 +7058,7 @@ Although unlikely, some legitimate applications may use a moved copy of mshta.ex _version_: 1 -===== +--- ### Detect new API calls from user roles This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. @@ -7099,7 +7099,7 @@ It is possible that there are legitimate user roles making new or infrequently u _version_: 1 -===== +--- ### Detect new user AWS Console Login This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. @@ -7141,7 +7141,7 @@ When a legitimate new user logins for the first time, this activity will be dete _version_: 2 -===== +--- ### Detect processes used for System Network Configuration Discovery This search looks for fast execution of processes used for system network configuration discovery on the endpoint. @@ -7186,7 +7186,7 @@ It is uncommon for normal users to execute a series of commands used for network _version_: 2 -===== +--- ### Detect web traffic to dynamic domain providers This search looks for web connections to dynamic DNS providers. @@ -7231,7 +7231,7 @@ It is possible that list of dynamic DNS providers is outdated and/or that the UR _version_: 2 -===== +--- ### Detection of DNS Tunnels This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. @@ -7274,7 +7274,7 @@ It's possible that normal DNS traffic will exhibit this behavior. If an alert is _version_: 2 -===== +--- ### Detection of tools built by NirSoft This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. @@ -7317,7 +7317,7 @@ While legitimate, these NirSoft tools are prone to abuse. You should verfiy that _version_: 3 -===== +--- ### Disabling Remote User Account Control The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). @@ -7360,7 +7360,7 @@ This registry key may be modified via administrators to implement a change in sy _version_: 4 -===== +--- ### Dump LSASS via comsvcs DLL Detect the usage of comsvcs.dll for dumping the lsass process. @@ -7405,7 +7405,7 @@ None identified. _version_: 1 -===== +--- ### Dump LSASS via procdump Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\ During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. @@ -7452,7 +7452,7 @@ None identified. _version_: 1 -===== +--- ### Dump LSASS via procdump Rename Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\ During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. @@ -7499,7 +7499,7 @@ None identified. _version_: 1 -===== +--- ### EC2 Instance Modified With Previously Unseen User This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -7540,7 +7540,7 @@ It's possible that a new user will start to modify EC2 instances when they haven _version_: 3 -===== +--- ### EC2 Instance Started In Previously Unseen Region This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started @@ -7582,7 +7582,7 @@ It's possible that a user has unknowingly started an instance in a new region. P _version_: 1 -===== +--- ### EC2 Instance Started With Previously Unseen AMI This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -7622,7 +7622,7 @@ After a new AMI is created, the first systems created with that AMI will cause t _version_: 1 -===== +--- ### EC2 Instance Started With Previously Unseen Instance Type This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -7662,7 +7662,7 @@ It is possible that an admin will create a new system using a new instance type _version_: 2 -===== +--- ### EC2 Instance Started With Previously Unseen User This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -7703,7 +7703,7 @@ It's possible that a user will start to create EC2 instances when they haven't b _version_: 2 -===== +--- ### Email Attachments With Lots Of Spaces Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. @@ -7746,7 +7746,7 @@ None at this time _version_: 2 -===== +--- ### Email files written outside of the Outlook directory The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. @@ -7788,7 +7788,7 @@ Administrators and users sometimes prefer backing up their email data by moving _version_: 3 -===== +--- ### Email servers sending high volume traffic to hosts This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. @@ -7830,7 +7830,7 @@ The false-positive rate will vary based on how you set the deviation_threshold a _version_: 2 -===== +--- ### Excessive DNS Failures This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. @@ -7872,7 +7872,7 @@ It is possible legitimate traffic can trigger this rule. Please investigate as a _version_: 2 -===== +--- ### Execution of File With Spaces Before Extension This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. @@ -7914,7 +7914,7 @@ None identified. _version_: 3 -===== +--- ### Execution of File with Multiple Extensions This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. @@ -7957,7 +7957,7 @@ None identified. _version_: 3 -===== +--- ### Extended Period Without Successful Netbackup Backups This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. @@ -7997,7 +7997,7 @@ None identified _version_: 1 -===== +--- ### File with Samsam Extension The search looks for file writes with extensions consistent with a SamSam ransomware attack. @@ -8039,7 +8039,7 @@ Because these extensions are not typically used in normal operations, you should _version_: 1 -===== +--- ### First Time Seen Child Process of Zoom This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. @@ -8082,7 +8082,7 @@ A new child process of zoom isn't malicious by that fact alone. Further investig _version_: 1 -===== +--- ### First Time Seen Running Windows Service This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. @@ -8125,7 +8125,7 @@ A previously unseen service is not necessarily malicious. Verify that the servic _version_: 4 -===== +--- ### First time seen command line argument This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel. @@ -8175,7 +8175,7 @@ Legitimate programs can also use command-line arguments to execute. Please verif _version_: 2 -===== +--- ### First time seen command line argument This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. @@ -8219,7 +8219,7 @@ Legitimate programs can also use command-line arguments to execute. Please verif _version_: 5 -===== +--- ### GCP Detect accounts with high risk roles by project This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema. @@ -8264,7 +8264,7 @@ Accounts with high risk roles should be reduced to the minimum number needed, ho _version_: 1 -===== +--- ### GCP Detect gcploit framework This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts. @@ -8308,7 +8308,7 @@ Payload.request.function.timeout value can possibly be match with other function _version_: 1 -===== +--- ### GCP Detect high risk permissions by resource and account This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges. @@ -8353,7 +8353,7 @@ High risk permissions are part of any GCP environment, however it is important t _version_: 1 -===== +--- ### GCP GCR container uploaded This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. @@ -8394,7 +8394,7 @@ Uploading container is a normal behavior from developers or users with access to _version_: 1 -===== +--- ### GCP Kubernetes cluster pod scan detection This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods @@ -8436,7 +8436,7 @@ Not all unauthenticated requests are malicious, but frequency, User Agent, sourc _version_: 1 -===== +--- ### GCP Kubernetes cluster scan detection This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster @@ -8478,7 +8478,7 @@ Not all unauthenticated requests are malicious, but frequency, User Agent and so _version_: 1 -===== +--- ### Hiding Files And Directories With Attrib exe Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. @@ -8521,7 +8521,7 @@ Some applications and users may legitimately use attrib.exe to interact with the _version_: 4 -===== +--- ### High Number of Login Failures from a single source This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. @@ -8563,7 +8563,7 @@ unknown _version_: 1 -===== +--- ### Hosts receiving high volume of network traffic from email server This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. @@ -8605,7 +8605,7 @@ The false-positive rate will vary based on how you set the deviation_threshold a _version_: 2 -===== +--- ### Identify New User Accounts This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. @@ -8646,7 +8646,7 @@ If the Identity_Management data model is not updated regularly, this search coul _version_: 1 -===== +--- ### Illegal Access To User Content via PowerSploit modules This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc. @@ -8698,7 +8698,7 @@ None identified. _version_: 1 -===== +--- ### Illegal Account Creation via PowerSploit modules This detection identifies access to PowerSploit modules that create accounts illegaly. @@ -8747,7 +8747,7 @@ None identified. _version_: 1 -===== +--- ### Illegal Deletion of Logs via Mimikatz modules This detection identifies access to PowerSploit modules that delete event logs. @@ -8796,7 +8796,7 @@ None identified. _version_: 1 -===== +--- ### Illegal Enabling or Disabling of Accounts via DSInternals modules This detection identifies use of DSInternals modules that enable or disable accounts illegaly. @@ -8846,7 +8846,7 @@ None identified. _version_: 1 -===== +--- ### Illegal Management of Active Directory Elements and Policies via DSInternals modules This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies. @@ -8897,7 +8897,7 @@ None identified. _version_: 1 -===== +--- ### Illegal Management of Computers and Active Directory Elements via PowerSploit modules This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements. @@ -8949,7 +8949,7 @@ None identified. _version_: 1 -===== +--- ### Illegal Privilege Elevation and Persistence via PowerSploit modules This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities. @@ -9000,7 +9000,7 @@ None identified. _version_: 1 -===== +--- ### Illegal Privilege Elevation via Mimikatz modules This detection identifies use of Mimikatz modules for illegal privilege elevation. @@ -9050,7 +9050,7 @@ None identified. _version_: 1 -===== +--- ### Illegal Service and Process Control via Mimikatz modules This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service. @@ -9101,7 +9101,7 @@ None identified. _version_: 1 -===== +--- ### Illegal Service and Process Control via PowerSploit modules This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc. @@ -9153,7 +9153,7 @@ None identified. _version_: 1 -===== +--- ### Kerberoasting spn request with RC4 encryption This search detects a potential kerberoasting attack via service principal name requests @@ -9198,7 +9198,7 @@ Older systems that support kerberos RC4 by default NetApp may generate false pos _version_: 3 -===== +--- ### Kubernetes AWS detect RBAC authorization by account This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences @@ -9239,7 +9239,7 @@ Not all RBAC Authorications are malicious. RBAC authorizations can uncover malic _version_: 1 -===== +--- ### Kubernetes AWS detect most active service accounts by pod This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision @@ -9280,7 +9280,7 @@ Not all service accounts interactions are malicious. Analyst must consider IP, v _version_: 1 -===== +--- ### Kubernetes AWS detect sensitive role access This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets @@ -9321,7 +9321,7 @@ Sensitive role resource access is necessary for cluster operation, however sourc _version_: 1 -===== +--- ### Kubernetes AWS detect service accounts forbidden failure access This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI @@ -9362,7 +9362,7 @@ This search can give false positives as there might be inherent issues with auth _version_: 1 -===== +--- ### Kubernetes AWS detect suspicious kubectl calls This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context @@ -9403,7 +9403,7 @@ Kubectl calls are not malicious by nature. However source IP, verb and Object ca _version_: 1 -===== +--- ### Kubernetes Azure detect RBAC authorization by account This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences @@ -9444,7 +9444,7 @@ Not all RBAC Authorications are malicious. RBAC authorizations can uncover malic _version_: 1 -===== +--- ### Kubernetes Azure detect most active service accounts by pod namespace This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb @@ -9485,7 +9485,7 @@ Not all service accounts interactions are malicious. Analyst must consider IP an _version_: 1 -===== +--- ### Kubernetes Azure detect sensitive object access This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets @@ -9526,7 +9526,7 @@ Sensitive object access is not necessarily malicious but user and object context _version_: 1 -===== +--- ### Kubernetes Azure detect sensitive role access This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets @@ -9567,7 +9567,7 @@ Sensitive role resource access is necessary for cluster operation, however sourc _version_: 1 -===== +--- ### Kubernetes Azure detect service accounts forbidden failure access This search provides information on Kubernetes service accounts with failure or forbidden access status @@ -9608,7 +9608,7 @@ This search can give false positives as there might be inherent issues with auth _version_: 1 -===== +--- ### Kubernetes Azure detect suspicious kubectl calls This search provides information on rare Kubectl calls with IP, verb namespace and object access context @@ -9649,7 +9649,7 @@ Kubectl calls are not malicious by nature. However source IP, verb and Object ca _version_: 1 -===== +--- ### Kubernetes Azure pod scan fingerprint This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure @@ -9690,7 +9690,7 @@ Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, _version_: 1 -===== +--- ### Kubernetes Azure scan fingerprint This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure @@ -9732,7 +9732,7 @@ Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, _version_: 1 -===== +--- ### Kubernetes GCP detect RBAC authorizations by account This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences @@ -9773,7 +9773,7 @@ Not all RBAC Authorications are malicious. RBAC authorizations can uncover malic _version_: 1 -===== +--- ### Kubernetes GCP detect most active service accounts by pod This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision @@ -9814,7 +9814,7 @@ Not all service accounts interactions are malicious. Analyst must consider IP, v _version_: 1 -===== +--- ### Kubernetes GCP detect sensitive object access This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets @@ -9855,7 +9855,7 @@ Sensitive object access is not necessarily malicious but user and object context _version_: 1 -===== +--- ### Kubernetes GCP detect sensitive role access This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets @@ -9896,7 +9896,7 @@ Sensitive role resource access is necessary for cluster operation, however sourc _version_: 1 -===== +--- ### Kubernetes GCP detect service accounts forbidden failure access This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI @@ -9937,7 +9937,7 @@ This search can give false positives as there might be inherent issues with auth _version_: 1 -===== +--- ### Kubernetes GCP detect suspicious kubectl calls This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context @@ -9978,7 +9978,7 @@ Kubectl calls are not malicious by nature. However source IP, source user, user _version_: 1 -===== +--- ### Large Volume of DNS ANY Queries The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. @@ -10020,7 +10020,7 @@ Legitimate ANY requests may trigger this search, however it is unusual to see a _version_: 1 -===== +--- ### MacOS - Re-opened Applications This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. @@ -10062,7 +10062,7 @@ At this stage, there are no known false positives. During testing, no process ev _version_: 1 -===== +--- ### Malicious PowerShell Process - Connect To Internet With Hidden Window This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. @@ -10106,7 +10106,7 @@ Legitimate process can have this combination of command-line options, but it's n _version_: 5 -===== +--- ### Malicious PowerShell Process - Encoded Command This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. @@ -10150,7 +10150,7 @@ System administrators may use this option, but it's not common. _version_: 4 -===== +--- ### Malicious PowerShell Process - Execution Policy Bypass This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. @@ -10194,7 +10194,7 @@ There may be legitimate reasons to bypass the PowerShell execution policy. The P _version_: 4 -===== +--- ### Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command @@ -10237,7 +10237,7 @@ Legitimate process can have this combination of command-line options, but it's n _version_: 6 -===== +--- ### Malicious PowerShell Process With Obfuscation Techniques This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. @@ -10281,7 +10281,7 @@ These characters might be legitimately on the command-line, but it is not common _version_: 4 -===== +--- ### Monitor DNS For Brand Abuse This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. @@ -10323,7 +10323,7 @@ None at this time _version_: 1 -===== +--- ### Monitor Email For Brand Abuse This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. @@ -10364,7 +10364,7 @@ None at this time _version_: 2 -===== +--- ### Monitor Registry Keys for Print Monitors This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. @@ -10407,7 +10407,7 @@ You will encounter noise from legitimate print-monitor registry entries. _version_: 2 -===== +--- ### Monitor Web Traffic For Brand Abuse This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. @@ -10448,7 +10448,7 @@ None at this time _version_: 1 -===== +--- ### More than usual number of LOLBAS applications in short time period Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine. @@ -10496,7 +10496,7 @@ Some administrative tasks may involve multiple use of LOLBAS applications in a s _version_: 1 -===== +--- ### Multiple Okta Users With Invalid Credentials From The Same IP This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. @@ -10537,7 +10537,7 @@ A single public IP address servicing multiple legitmate users may trigger this s _version_: 2 -===== +--- ### NLTest Domain Trust Discovery This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next. @@ -10587,7 +10587,7 @@ Administrators may use nltest for troubleshooting purposes, otherwise, rarely us _version_: 1 -===== +--- ### New container uploaded to AWS ECR This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. @@ -10628,7 +10628,7 @@ Uploading container is a normal behavior from developers or users with access to _version_: 1 -===== +--- ### No Windows Updates in a time frame This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. @@ -10668,7 +10668,7 @@ None identified _version_: 1 -===== +--- ### Ntdsutil export ntds Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \ ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ @@ -10717,7 +10717,7 @@ Highly possible Server Administrators will troubleshoot with ntdsutil.exe, gener _version_: 1 -===== +--- ### O365 Add App Role Assignment Grant User This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. @@ -10762,7 +10762,7 @@ The creation of a new Federation is not necessarily malicious, however this even _version_: 1 -===== +--- ### O365 Added Service Principal This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. @@ -10809,7 +10809,7 @@ The creation of a new Federation is not necessarily malicious, however these eve _version_: 1 -===== +--- ### O365 Bypass MFA via Trusted IP This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system. @@ -10854,7 +10854,7 @@ Unless it is a special case, it is uncommon to continually update Trusted IPs to _version_: 1 -===== +--- ### O365 Disable MFA This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user @@ -10898,7 +10898,7 @@ Unless it is a special case, it is uncommon to disable MFA or Strong Authenticat _version_: 1 -===== +--- ### O365 Excessive Authentication Failures Alert This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes @@ -10942,7 +10942,7 @@ The threshold for alert is above 10 attempts and this should reduce the number o _version_: 1 -===== +--- ### O365 Excessive SSO logon errors This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse. @@ -10986,7 +10986,7 @@ Logon errors may not be malicious in nature however it may indicate attempts to _version_: 1 -===== +--- ### O365 New Federated Domain Added This search detects the addition of a new Federated domain. @@ -11034,7 +11034,7 @@ The creation of a new Federated domain is not necessarily malicious, however the _version_: 1 -===== +--- ### O365 PST export alert This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content @@ -11078,7 +11078,7 @@ PST export can be done for legitimate purposes but due to the sensitive nature o _version_: 1 -===== +--- ### O365 Suspicious Admin Email Forwarding This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination. @@ -11121,7 +11121,7 @@ unknown _version_: 1 -===== +--- ### O365 Suspicious Rights Delegation This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account. @@ -11164,7 +11164,7 @@ Service Accounts _version_: 1 -===== +--- ### O365 Suspicious User Email Forwarding This search detects when multiple user configured a forwarding rule to the same destination. @@ -11207,7 +11207,7 @@ unknown _version_: 1 -===== +--- ### Okta Account Lockout Events Detect Okta user lockout events @@ -11248,7 +11248,7 @@ None. Account lockouts should be followed up on to determine if the actual user _version_: 2 -===== +--- ### Okta Failed SSO Attempts Detect failed Okta SSO events @@ -11289,7 +11289,7 @@ There may be a faulty config preventing legitmate users from accessing apps they _version_: 2 -===== +--- ### Okta User Logins From Multiple Cities This search detects logins from the same user from different cities in a 24 hour period. @@ -11330,7 +11330,7 @@ Users in your enviornment may legitmately be travelling and loggin in from diffe _version_: 2 -===== +--- ### Open Redirect in Splunk Web This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. @@ -11371,7 +11371,7 @@ None identified _version_: 1 -===== +--- ### Osquery pack - ColdRoot detection This search looks for ColdRoot events from the osx-attacks osquery pack. @@ -11413,7 +11413,7 @@ There are no known false positives. _version_: 1 -===== +--- ### Overwriting Accessibility Binaries Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. @@ -11456,7 +11456,7 @@ Microsoft may provide updates to these binaries. Verify that these changes do no _version_: 4 -===== +--- ### Phishing Email Detection by Machine Learning Method - SSA Malicious mails can conduct phishing that induces readers to open attachment, click links or trigger third party service. This detect uses Natural Language Processing (NLP) approach to analyze an email message's content (Sender, Subject and Body) and judge whether it is a phishing email. The detection adopts a deep learning (neural network) model that employs character level embeddings plus LSTM layers to perform classification. The model is pre-trained and then published as ONNX format. Current sample model is trained using the dataset published at https://github.com/splunk/attack_data/tree/master/datasets/T1566_Phishing_Email/splunk_train.json User are expected to re-train the model by combining with their own training data for better accuracy using the provided model file (SMLE notebook). DSP pipeline then processes the email message and passes it as an event to Apply ML Models function, which returns the probability of a phishing email. Current implementation assumes the email is fed to DSP in JSON format contains at least email's sender, subject and its message body, including reply content, if any. @@ -11498,7 +11498,7 @@ Because of imbalance of anomaly data in training, the model will less likely rep _version_: 1 -===== +--- ### Probing Access with Stolen Credentials via PowerSploit modules This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services. @@ -11548,7 +11548,7 @@ None identified. _version_: 1 -===== +--- ### Process Creating LNK file in Suspicious Location This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools. @@ -11594,7 +11594,7 @@ This detection should yield little or no false positive results. It is uncommon _version_: 4 -===== +--- ### Process Execution via WMI This search looks for processes launched via WMI. @@ -11637,7 +11637,7 @@ Although unlikely, administrators may use wmi to execute commands for legitimate _version_: 3 -===== +--- ### Processes Tapping Keyboard Events This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input @@ -11678,7 +11678,7 @@ There might be some false positives as keyboard event taps are used by processes _version_: 1 -===== +--- ### Processes created by netsh This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. @@ -11720,7 +11720,7 @@ It is unusual for netsh.exe to have any child processes in most environments. It _version_: 5 -===== +--- ### Processes launching netsh This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. @@ -11763,7 +11763,7 @@ Some VPN applications are known to launch netsh.exe. Outside of these instances, _version_: 3 -===== +--- ### Prohibited Network Traffic Allowed This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. @@ -11806,7 +11806,7 @@ None identified _version_: 2 -===== +--- ### Prohibited Software On Endpoint This search looks for applications on the endpoint that you have marked as prohibited. @@ -11849,7 +11849,7 @@ None identified _version_: 2 -===== +--- ### Protocol or Port Mismatch This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. @@ -11891,7 +11891,7 @@ None identified _version_: 2 -===== +--- ### Protocols passing authentication in cleartext This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. @@ -11933,7 +11933,7 @@ Some networks may use kerberized FTP or telnet servers, however, this is rare. _version_: 2 -===== +--- ### Rare Parent-Child Process Relationship An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries @@ -11985,7 +11985,7 @@ Some custom tools used by admins could be used rarely to launch remotely applica _version_: 1 -===== +--- ### Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over. @@ -12036,7 +12036,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance and Access to Accounts and Groups via Mimikatz modules This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them. @@ -12087,7 +12087,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations. @@ -12140,7 +12140,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance and Access to Computers and Domains via PowerSploit modules This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over. @@ -12191,7 +12191,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance and Access to Computers via Mimikatz modules This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them. @@ -12240,7 +12240,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance and Access to Operating System Elements via PowerSploit modules This detection identifies access to PowerSploit modules that discover and access operating system elements, such as processes, services, registry locations, security packages and files. @@ -12296,7 +12296,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance and Access to Processes and Services via Mimikatz modules This detection identifies use of Mimikatz modules for discovery and access to services and processes. @@ -12347,7 +12347,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance and Access to Shared Resources via Mimikatz modules This detection identifies use of Mimikatz modules for discovery and access to network shares. @@ -12398,7 +12398,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance and Access to Shared Resources via PowerSploit modules This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares. @@ -12449,7 +12449,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance of Access and Persistence Opportunities via PowerSploit modules This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised. @@ -12503,7 +12503,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance of Connectivity via PowerSploit modules This detection identifies access to PowerSploit modules for reconnaissance of connectivity. @@ -12554,7 +12554,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance of Credential Stores and Services via Mimikatz modules This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules. @@ -12608,7 +12608,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance of Defensive Tools via PowerSploit modules This detection identifies use of PowerSploit modules for assessment of presence of defensive tools. @@ -12658,7 +12658,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities. @@ -12709,7 +12709,7 @@ None identified. _version_: 1 -===== +--- ### Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary. @@ -12761,7 +12761,7 @@ None identified. _version_: 1 -===== +--- ### Reg exe Manipulating Windows Services Registry Keys The search looks for reg.exe modifying registry keys that define Windows services and their configurations. @@ -12804,7 +12804,7 @@ It is unusual for a service to be created or modified by directly manipulating t _version_: 5 -===== +--- ### Reg exe used to hide files directories via registry keys The search looks for command-line arguments used to hide a file or directory using the reg add command. @@ -12846,7 +12846,7 @@ None at the moment _version_: 2 -===== +--- ### Registry Keys Used For Persistence The search looks for modifications to registry keys that can be used to launch an application or service at system startup. @@ -12889,7 +12889,7 @@ There are many legitimate applications that must execute on system startup and w _version_: 5 -===== +--- ### Registry Keys Used For Privilege Escalation This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. @@ -12933,7 +12933,7 @@ There are many legitimate applications that must execute upon system startup and _version_: 4 -===== +--- ### Registry Keys for Creating SHIM Databases This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. @@ -12976,7 +12976,7 @@ There are many legitimate applications that leverage shim databases for compatib _version_: 3 -===== +--- ### Remote Desktop Network Bruteforce This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. @@ -13019,7 +13019,7 @@ RDP gateways may have unusually high amounts of traffic from all other hosts' RD _version_: 2 -===== +--- ### Remote Desktop Network Traffic This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. @@ -13061,7 +13061,7 @@ Remote Desktop may be used legitimately by users on the network. _version_: 3 -===== +--- ### Remote Desktop Process Running On System This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. @@ -13103,7 +13103,7 @@ Remote Desktop may be used legitimately by users on the network. _version_: 5 -===== +--- ### Remote Process Instantiation via WMI This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. @@ -13146,7 +13146,7 @@ The wmic.exe utility is a benign Windows application. It may be used legitimatel _version_: 5 -===== +--- ### Remote Registry Key modifications This search monitors for remote modifications to registry keys. @@ -13187,7 +13187,7 @@ This technique may be legitimately used by administrators to modify remote regis _version_: 3 -===== +--- ### Remote WMI Command Attempt This search looks for wmic.exe being launched with parameters to operate on remote systems. @@ -13229,7 +13229,7 @@ Administrators may use this legitimately to gather info from remote systems. _version_: 2 -===== +--- ### RunDLL Loading DLL By Ordinal This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. @@ -13272,7 +13272,7 @@ While not common, loading a DLL under %AppData% and calling a function by ordina _version_: 4 -===== +--- ### Ryuk Test Files Detected The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation. @@ -13315,7 +13315,7 @@ If there are files with this keywoord as file names it might trigger false possi _version_: 1 -===== +--- ### SMB Traffic Spike This search looks for spikes in the number of Server Message Block (SMB) traffic connections. @@ -13357,7 +13357,7 @@ A file server may experience high-demand loads that could cause this analytic to _version_: 3 -===== +--- ### SMB Traffic Spike - MLTK This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. @@ -13402,7 +13402,7 @@ If you are seeing more results than desired, you may consider reducing the value _version_: 3 -===== +--- ### SQL Injection with Long URLs This search looks for long URLs that have several SQL commands visible within them. @@ -13444,7 +13444,7 @@ It's possible that legitimate traffic will have long URLs or long user agent str _version_: 2 -===== +--- ### Samsam Test File Write The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. @@ -13487,7 +13487,7 @@ No false positives have been identified. _version_: 1 -===== +--- ### Sc exe Manipulating Windows Services This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. @@ -13530,7 +13530,7 @@ Using sc.exe to manipulate Windows services is uncommon. However, there may be l _version_: 4 -===== +--- ### Scheduled Task Deleted Or Created via CMD This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. @@ -13573,7 +13573,7 @@ Tasks should not be manually created via CLI, this is rarely done by admins as w _version_: 5 -===== +--- ### Scheduled tasks used in BadRabbit ransomware This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection @@ -13615,7 +13615,7 @@ No known false positives _version_: 3 -===== +--- ### Schtasks scheduling job on remote system This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. @@ -13658,7 +13658,7 @@ Administrators may create jobs on remote systems, but this activity is usually l _version_: 4 -===== +--- ### Schtasks used for forcing a reboot This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. @@ -13701,7 +13701,7 @@ Administrators may create jobs on systems forcing reboots to perform updates, ma _version_: 4 -===== +--- ### Script Execution via WMI This search looks for scripts launched via WMI. @@ -13744,7 +13744,7 @@ Although unlikely, administrators may use wmi to launch scripts for legitimate p _version_: 3 -===== +--- ### Setting Credentials via DSInternals modules This detection identifies illegal setting of credentials via DSInternals modules. @@ -13798,7 +13798,7 @@ None identified. _version_: 1 -===== +--- ### Setting Credentials via Mimikatz modules This detection identifies illegal setting of credentials via Mimikatz modules. @@ -13849,7 +13849,7 @@ None identified. _version_: 1 -===== +--- ### Setting Credentials via PowerSploit modules This detection identifies illegal setting of credentials via PowerSploit modules. @@ -13900,7 +13900,7 @@ None identified. _version_: 1 -===== +--- ### Shim Database File Creation This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. @@ -13943,7 +13943,7 @@ Because legitimate shim files are created and used all the time, this event, in _version_: 3 -===== +--- ### Shim Database Installation With Suspicious Parameters This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. @@ -13986,7 +13986,7 @@ None identified _version_: 4 -===== +--- ### Short Lived Windows Accounts This search detects accounts that were created and deleted in a short time period. @@ -14030,7 +14030,7 @@ It is possible that an administrator created and deleted an account in a short t _version_: 2 -===== +--- ### Single Letter Process On Endpoint This search looks for process names that consist only of a single letter. @@ -14073,7 +14073,7 @@ Single-letter executables are not always malicious. Investigate this activity wi _version_: 3 -===== +--- ### Spectre and Meltdown Vulnerable Systems The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. @@ -14113,7 +14113,7 @@ It is possible that your vulnerability scanner is not detecting that the patches _version_: 1 -===== +--- ### Spike in File Writes The search looks for a sharp increase in the number of files written to a particular host @@ -14154,7 +14154,7 @@ It is important to understand that if you happen to install any new applications _version_: 3 -===== +--- ### Splunk Enterprise Information Disclosure This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. @@ -14195,7 +14195,7 @@ Retrieving server information may be a legitimate API request. Verify that the a _version_: 1 -===== +--- ### Sunburst Correlation DLL and Network Event The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events. @@ -14238,7 +14238,7 @@ unknown _version_: 1 -===== +--- ### Supernova Webshell This search aims to detect the Supernova webshell used in the SUNBURST attack. @@ -14282,7 +14282,7 @@ There might be false positives associted with this detection since items like ar _version_: 1 -===== +--- ### Suspicious Changes to File Associations This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. @@ -14324,7 +14324,7 @@ There may be other processes in your environment that users may legitimately use _version_: 4 -===== +--- ### Suspicious Email - UBA Anomaly This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). @@ -14366,7 +14366,7 @@ This detection model will alert on any sender domain that is seen for the first _version_: 3 -===== +--- ### Suspicious Email Attachment Extensions This search looks for emails that have attachments with suspicious file extensions. @@ -14410,7 +14410,7 @@ None identified _version_: 3 -===== +--- ### Suspicious File Write The search looks for files created with names that have been linked to malicious activity. @@ -14451,7 +14451,7 @@ It's possible for a legitimate file to be created with the same name as one note _version_: 3 -===== +--- ### Suspicious Java Classes This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. @@ -14492,7 +14492,7 @@ There are no known false positives. _version_: 1 -===== +--- ### Suspicious MSBuild Rename The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. @@ -14539,7 +14539,7 @@ Although unlikely, some legitimate applications may use a moved copy of msbuild, _version_: 1 -===== +--- ### Suspicious MSBuild Spawn The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated. @@ -14584,7 +14584,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg _version_: 1 -===== +--- ### Suspicious Reg exe Process This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. @@ -14628,7 +14628,7 @@ It's possible for system administrators to write scripts that exhibit this behav _version_: 4 -===== +--- ### Suspicious Regsvr32 Register Suspicious Path Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. @@ -14676,7 +14676,7 @@ Limited false positives with the query restricted to specified paths. Add more w _version_: 1 -===== +--- ### Suspicious Rundll32 Rename The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. @@ -14723,7 +14723,7 @@ Although unlikely, some legitimate applications may use a moved copy of rundll32 _version_: 1 -===== +--- ### Suspicious Rundll32 StartW The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not. @@ -14771,7 +14771,7 @@ Although unlikely, some legitimate applications may use Start as a function and _version_: 1 -===== +--- ### Suspicious Rundll32 dllregisterserver The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. @@ -14821,7 +14821,7 @@ This is likely to produce false positives and will require some filtering. Tune _version_: 1 -===== +--- ### Suspicious Rundll32 no CommandLine Arguments The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. @@ -14868,7 +14868,7 @@ Although unlikely, some legitimate applications may use a moved copy of rundll32 _version_: 1 -===== +--- ### Suspicious microsoft workflow compiler rename The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect. @@ -14913,7 +14913,7 @@ Although unlikely, some legitimate applications may use a moved copy of microsof _version_: 1 -===== +--- ### Suspicious microsoft workflow compiler usage The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications. @@ -14958,7 +14958,7 @@ Although unlikely, limited instances have been identified coming from native Mic _version_: 1 -===== +--- ### Suspicious msbuild path The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild. @@ -15004,7 +15004,7 @@ Some legitimate applications may use a moved copy of msbuild.exe, triggering a f _version_: 1 -===== +--- ### Suspicious mshta child process The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. @@ -15049,7 +15049,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg _version_: 1 -===== +--- ### Suspicious mshta spawn The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe. @@ -15095,7 +15095,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg _version_: 1 -===== +--- ### Suspicious wevtutil Usage The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. @@ -15138,7 +15138,7 @@ The wevtutil.exe application is a legitimate Windows event log utility. Administ _version_: 3 -===== +--- ### Suspicious writes to System Volume Information This search detects writes to the 'System Volume Information' folder by something other than the System process. @@ -15179,7 +15179,7 @@ It is possible that other utilities or system processes may legitimately write t _version_: 2 -===== +--- ### Suspicious writes to windows Recycle Bin This search detects writes to the recycle bin by a process other than explorer.exe. @@ -15221,7 +15221,7 @@ Because the Recycle Bin is a hidden folder in modern versions of Windows, it wou _version_: 4 -===== +--- ### System Information Discovery Detection Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it. @@ -15265,7 +15265,7 @@ Administrators debugging servers _version_: 1 -===== +--- ### System Process Running from Unexpected Location An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml @@ -15319,7 +15319,7 @@ None _version_: 1 -===== +--- ### System Processes Run From Unexpected Locations This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. @@ -15362,7 +15362,7 @@ None identified _version_: 5 -===== +--- ### TOR Traffic This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. @@ -15404,7 +15404,7 @@ None at this time _version_: 2 -===== +--- ### USN Journal Deletion The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. @@ -15447,7 +15447,7 @@ None identified _version_: 2 -===== +--- ### Uncommon Processes On Endpoint This search looks for applications on the endpoint that you have marked as uncommon. @@ -15489,7 +15489,7 @@ None identified _version_: 4 -===== +--- ### Unload Sysmon Filter Driver Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. @@ -15532,7 +15532,7 @@ You must be ingesting data that records process activity from your hosts to popu _version_: 3 -===== +--- ### Unsigned Image Loaded by LSASS This search detects loading of unsigned images by LSASS. Deprecated because too noisy. @@ -15575,7 +15575,7 @@ Other tools could load images into LSASS for legitimate reason. But enterprise t _version_: 1 -===== +--- ### Unsuccessful Netbackup backups This search gives you the hosts where a backup was attempted and then failed. @@ -15615,7 +15615,7 @@ None identified _version_: 1 -===== +--- ### Unusually Long Command Line Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez. @@ -15661,7 +15661,7 @@ This detection may flag suspiciously long command lines when there is not suffic _version_: 1 -===== +--- ### Unusually Long Command Line Command lines that are extremely long may be indicative of malicious activity on your hosts. @@ -15703,7 +15703,7 @@ Some legitimate applications start with long command lines. _version_: 5 -===== +--- ### Unusually Long Command Line - MLTK Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. @@ -15744,7 +15744,7 @@ Some legitimate applications use long command lines for installs or updates. You _version_: 1 -===== +--- ### Unusually Long Content-Type Length This search looks for unusually long strings in the Content-Type http header that the client sends the server. @@ -15785,7 +15785,7 @@ Very few legitimate Content-Type fields will have a length greater than 100 char _version_: 1 -===== +--- ### WBAdmin Delete System Backups This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery. @@ -15832,7 +15832,7 @@ Administrators may modify the boot configuration. _version_: 1 -===== +--- ### WMI Permanent Event Subscription This search looks for the creation of WMI permanent event subscriptions. @@ -15874,7 +15874,7 @@ Although unlikely, administrators may use event subscriptions for legitimate pur _version_: 1 -===== +--- ### WMI Permanent Event Subscription - Sysmon This search looks for the creation of WMI permanent event subscriptions. @@ -15917,7 +15917,7 @@ Although unlikely, administrators may use event subscriptions for legitimate pur _version_: 2 -===== +--- ### WMI Temporary Event Subscription This search looks for the creation of WMI temporary event subscriptions. @@ -15959,7 +15959,7 @@ Some software may create WMI temporary event subscriptions for various purposes. _version_: 1 -===== +--- ### Web Fraud - Account Harvesting This search is used to identify the creation of multiple user accounts using the same email domain name. @@ -16003,7 +16003,7 @@ As is common with many fraud-related searches, we are usually looking to attribu _version_: 1 -===== +--- ### Web Fraud - Anomalous User Clickspeed This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. @@ -16049,7 +16049,7 @@ As is common with many fraud-related searches, we are usually looking to attribu _version_: 1 -===== +--- ### Web Fraud - Password Sharing Across Accounts This search is used to identify user accounts that share a common password. @@ -16093,7 +16093,7 @@ As is common with many fraud-related searches, we are usually looking to attribu _version_: 1 -===== +--- ### Web Servers Executing Suspicious Processes This search looks for suspicious processes on all systems labeled as web servers. @@ -16135,7 +16135,7 @@ Some of these processes may be used legitimately on web servers during maintenan _version_: 1 -===== +--- ### Windows AdFind Exe This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. @@ -16180,7 +16180,7 @@ administrators rarely use adfind, usually not used for legitimate reasons _version_: 1 -===== +--- ### Windows DisableAntiSpyware Registry The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. @@ -16222,7 +16222,7 @@ It is unusual to turn this feature on a Windows system since it is a default sec _version_: 1 -===== +--- ### Windows Event Log Cleared This search looks for Windows events that indicate one of the Windows event logs has been purged. @@ -16266,7 +16266,7 @@ It is possible that these logs may be legitimately cleared by Administrators. _version_: 4 -===== +--- ### Windows Security Account Manager Stopped The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints. @@ -16309,7 +16309,7 @@ SAM is a critical windows service, stopping it would cause major issues on an en _version_: 1 -===== +--- ### Windows connhost exe started forcefully The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. @@ -16351,7 +16351,7 @@ This process should not be ran forcefully, we have not see any false positives f _version_: 1 -===== +--- ### Windows hosts file modification The search looks for modifications to the hosts file on all Windows endpoints across your environment. @@ -16392,7 +16392,7 @@ There may be legitimate reasons for system administrators to add entries to this _version_: 1 -===== +--- ### aws detect attach to role policy This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges. @@ -16434,7 +16434,7 @@ Attach to policy can create a lot of noise. This search can be adjusted to provi _version_: 1 -===== +--- ### aws detect permanent key creation This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor. @@ -16476,7 +16476,7 @@ Not all permanent key creations are malicious. If there is a policy of rotating _version_: 1 -===== +--- ### aws detect role creation This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges. @@ -16518,7 +16518,7 @@ CreateRole is not very common in common users. This search can be adjusted to pr _version_: 1 -===== +--- ### aws detect sts assume role abuse This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. @@ -16560,7 +16560,7 @@ Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross _version_: 1 -===== +--- ### aws detect sts get session token abuse This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. @@ -16602,7 +16602,7 @@ Sts:GetSessionToken can be very noisy as in certain environments numerous calls _version_: 1 -===== +--- ### gcp detect oauth token abuse This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally. @@ -16646,4 +16646,4 @@ GCP Oauth token abuse detection will only work if there are access policies in p _version_: 1 -===== +--- From 10c6f54604a51e8e8cd99a303a5815845686155f Mon Sep 17 00:00:00 2001 From: divious1 Date: Thu, 25 Feb 2021 18:53:26 -0500 Subject: [PATCH 06/62] fixed up a few items with markdown --- bin/doc_gen.py | 6 +- .../doc_detections_markdown.j2 | 13 +- docs/detections.md | 12419 ++++++++++++++-- 3 files changed, 11507 insertions(+), 931 deletions(-) diff --git a/bin/doc_gen.py b/bin/doc_gen.py index f3b115d558..b3a98fc183 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -38,8 +38,9 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): detections.append(detection_yaml) sorted_detections= sorted(detections, key=lambda i: i['name']) + j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), - trim_blocks=True) + trim_blocks=False) template = j2_env.get_template('doc_detections_markdown.j2') output_path = path.join(OUTPUT_DIR + '/detections.md') output = template.render(detections=sorted_detections) @@ -47,9 +48,6 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): f.write(output) print("doc_gen.py wrote {0} detection documentation to: {1}".format(len(detections),output_path)) - - - return False if __name__ == "__main__": diff --git a/bin/jinja2_templates/doc_detections_markdown.j2 b/bin/jinja2_templates/doc_detections_markdown.j2 index 1ac8e56336..92f0500d80 100644 --- a/bin/jinja2_templates/doc_detections_markdown.j2 +++ b/bin/jinja2_templates/doc_detections_markdown.j2 @@ -1,3 +1,4 @@ +#jinja2: trim_blocks:True # Splunk Security Content Detections ![security_content](static/logo.png) ===== @@ -65,8 +66,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by {{ detection.description }} - **Product**: {{ detection.tags.product|join(', ') }} -- **Data Models**: {{ detection.datamodels|join(', ') }} -- **ATT&CK**: {{ detection.tags.mitre_attack_id|join(', ') }} +- **Data Models**: {{ detection.datamodel|join(', ') }} +- **ATT&CK**: {% for mitre_attack_id in detection.tags.mitre_attack_id %}[{{ mitre_attack_id }}](https://attack.mitre.org/techniques/{{ mitre_attack_id }}/){% if not loop.last %}, {% endif %}{% endfor %} - **Last Updated**: {{ detection.date }}
@@ -74,10 +75,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by #### Search ``` -{{ detection.search }} +{{ detection.search|replace("|", "\n|") }} ``` #### Associated Analytic Story -{% for story in detection.tags.analytics_story %} +{% for story in detection.tags.analytic_story %} * {{ story }} {% endfor %} @@ -93,9 +94,9 @@ All the detections shipped to different Splunk products. Below is a breakdown by | ID | technique | Tactic | | ----------- | ----------- |:-------------:| -{% for id in detection.tags.mitre_attack_id %} +{%- for id in detection.tags.mitre_attack_id %} | {{ id }} | x | x | -{% endfor %} +{%- endfor %} #### Kill Chain Phases {% for phase in detection.tags.kill_chain_phases %} diff --git a/docs/detections.md b/docs/detections.md index bdcbe0f18e..e12218a47c 100644 --- a/docs/detections.md +++ b/docs/detections.md @@ -1,3 +1,4 @@ +#jinja2: trim_blocks:True # Splunk Security Content Detections ![security_content](static/logo.png) ===== @@ -7,352 +8,4340 @@ All the detections shipped to different Splunk products. Below is a breakdown by
View + + + + + + + + + + - [AWS Cross Account Activity From Previously Unseen Account](#aws-cross-account-activity-from-previously-unseen-account) + + + - [AWS Detect Users creating keys with encrypt policy without MFA](#aws-detect-users-creating-keys-with-encrypt-policy-without-mfa) + + + - [AWS Detect Users with KMS keys performing encryption S3](#aws-detect-users-with-kms-keys-performing-encryption-s3) + + + - [AWS EKS Kubernetes cluster sensitive object access](#aws-eks-kubernetes-cluster-sensitive-object-access) + + + - [AWS Network Access Control List Created with All Open Ports](#aws-network-access-control-list-created-with-all-open-ports) + + + - [AWS Network Access Control List Deleted](#aws-network-access-control-list-deleted) + + + - [AWS SAML Access by Provider User and Principal](#aws-saml-access-by-provider-user-and-principal) + + + - [AWS SAML Update identity provider](#aws-saml-update-identity-provider) + + + + + + + + + + + - [Abnormally High Number Of Cloud Infrastructure API Calls](#abnormally-high-number-of-cloud-infrastructure-api-calls) + + + - [Abnormally High Number Of Cloud Instances Destroyed](#abnormally-high-number-of-cloud-instances-destroyed) + + + - [Abnormally High Number Of Cloud Instances Launched](#abnormally-high-number-of-cloud-instances-launched) + + + - [Abnormally High Number Of Cloud Security Group API Calls](#abnormally-high-number-of-cloud-security-group-api-calls) + + + + + - [Amazon EKS Kubernetes Pod scan detection](#amazon-eks-kubernetes-pod-scan-detection) + + + - [Amazon EKS Kubernetes cluster scan detection](#amazon-eks-kubernetes-cluster-scan-detection) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Cloud API Calls From Previously Unseen User Roles](#cloud-api-calls-from-previously-unseen-user-roles) + + + - [Cloud Compute Instance Created By Previously Unseen User](#cloud-compute-instance-created-by-previously-unseen-user) + + + - [Cloud Compute Instance Created In Previously Unused Region](#cloud-compute-instance-created-in-previously-unused-region) + + + - [Cloud Compute Instance Created With Previously Unseen Image](#cloud-compute-instance-created-with-previously-unseen-image) + + + - [Cloud Compute Instance Created With Previously Unseen Instance Type](#cloud-compute-instance-created-with-previously-unseen-instance-type) + + + - [Cloud Instance Modified By Previously Unseen User](#cloud-instance-modified-by-previously-unseen-user) + + + + + - [Cloud Provisioning Activity From Previously Unseen City](#cloud-provisioning-activity-from-previously-unseen-city) + + + - [Cloud Provisioning Activity From Previously Unseen Country](#cloud-provisioning-activity-from-previously-unseen-country) + + + - [Cloud Provisioning Activity From Previously Unseen IP Address](#cloud-provisioning-activity-from-previously-unseen-ip-address) + + + - [Cloud Provisioning Activity From Previously Unseen Region](#cloud-provisioning-activity-from-previously-unseen-region) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Detect AWS Console Login by New User](#detect-aws-console-login-by-new-user) + + + - [Detect AWS Console Login by User from New City](#detect-aws-console-login-by-user-from-new-city) + + + - [Detect AWS Console Login by User from New Country](#detect-aws-console-login-by-user-from-new-country) + + + - [Detect AWS Console Login by User from New Region](#detect-aws-console-login-by-user-from-new-region) + + + + + + + + + + + + + + + + + + + + + + + + + - [Detect GCP Storage access from a new IP](#detect-gcp-storage-access-from-a-new-ip) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Detect New Open GCP Storage Buckets](#detect-new-open-gcp-storage-buckets) + + + - [Detect New Open S3 Buckets over AWS CLI](#detect-new-open-s3-buckets-over-aws-cli) + + + - [Detect New Open S3 buckets](#detect-new-open-s3-buckets) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Detect S3 access from a new IP](#detect-s3-access-from-a-new-ip) + + + + + + + + + - [Detect Spike in AWS Security Hub Alerts for EC2 Instance](#detect-spike-in-aws-security-hub-alerts-for-ec2-instance) + + + - [Detect Spike in AWS Security Hub Alerts for User](#detect-spike-in-aws-security-hub-alerts-for-user) + + + + + - [Detect Spike in S3 Bucket deletion](#detect-spike-in-s3-bucket-deletion) + + + + + - [Detect Spike in blocked Outbound Traffic from your AWS](#detect-spike-in-blocked-outbound-traffic-from-your-aws) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [GCP Detect accounts with high risk roles by project](#gcp-detect-accounts-with-high-risk-roles-by-project) + + + - [GCP Detect gcploit framework](#gcp-detect-gcploit-framework) + + + - [GCP Detect high risk permissions by resource and account](#gcp-detect-high-risk-permissions-by-resource-and-account) + + + + + - [GCP Kubernetes cluster pod scan detection](#gcp-kubernetes-cluster-pod-scan-detection) + + + - [GCP Kubernetes cluster scan detection](#gcp-kubernetes-cluster-scan-detection) + + + + + - [High Number of Login Failures from a single source](#high-number-of-login-failures-from-a-single-source) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Kubernetes AWS detect RBAC authorization by account](#kubernetes-aws-detect-rbac-authorization-by-account) + + + - [Kubernetes AWS detect most active service accounts by pod](#kubernetes-aws-detect-most-active-service-accounts-by-pod) + + + - [Kubernetes AWS detect sensitive role access](#kubernetes-aws-detect-sensitive-role-access) + + + - [Kubernetes AWS detect service accounts forbidden failure access](#kubernetes-aws-detect-service-accounts-forbidden-failure-access) + + + - [Kubernetes AWS detect suspicious kubectl calls](#kubernetes-aws-detect-suspicious-kubectl-calls) + + + - [Kubernetes Azure detect RBAC authorization by account](#kubernetes-azure-detect-rbac-authorization-by-account) + + + - [Kubernetes Azure detect most active service accounts by pod namespace](#kubernetes-azure-detect-most-active-service-accounts-by-pod-namespace) + + + - [Kubernetes Azure detect sensitive object access](#kubernetes-azure-detect-sensitive-object-access) + + + - [Kubernetes Azure detect sensitive role access](#kubernetes-azure-detect-sensitive-role-access) + + + - [Kubernetes Azure detect service accounts forbidden failure access](#kubernetes-azure-detect-service-accounts-forbidden-failure-access) + + + - [Kubernetes Azure detect suspicious kubectl calls](#kubernetes-azure-detect-suspicious-kubectl-calls) + + + - [Kubernetes Azure pod scan fingerprint](#kubernetes-azure-pod-scan-fingerprint) + + + - [Kubernetes Azure scan fingerprint](#kubernetes-azure-scan-fingerprint) + + + - [Kubernetes GCP detect RBAC authorizations by account](#kubernetes-gcp-detect-rbac-authorizations-by-account) + + + - [Kubernetes GCP detect most active service accounts by pod](#kubernetes-gcp-detect-most-active-service-accounts-by-pod) + + + - [Kubernetes GCP detect sensitive object access](#kubernetes-gcp-detect-sensitive-object-access) + + + - [Kubernetes GCP detect sensitive role access](#kubernetes-gcp-detect-sensitive-role-access) + + + - [Kubernetes GCP detect service accounts forbidden failure access](#kubernetes-gcp-detect-service-accounts-forbidden-failure-access) + + + - [Kubernetes GCP detect suspicious kubectl calls](#kubernetes-gcp-detect-suspicious-kubectl-calls) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [New container uploaded to AWS ECR](#new-container-uploaded-to-aws-ecr) + + + + + + + - [O365 Add App Role Assignment Grant User](#o365-add-app-role-assignment-grant-user) + + + - [O365 Added Service Principal](#o365-added-service-principal) + + + - [O365 Bypass MFA via Trusted IP](#o365-bypass-mfa-via-trusted-ip) + + + - [O365 Disable MFA](#o365-disable-mfa) + + + - [O365 Excessive Authentication Failures Alert](#o365-excessive-authentication-failures-alert) + + + - [O365 Excessive SSO logon errors](#o365-excessive-sso-logon-errors) + + + - [O365 New Federated Domain Added](#o365-new-federated-domain-added) + + + - [O365 PST export alert](#o365-pst-export-alert) + + + - [O365 Suspicious Admin Email Forwarding](#o365-suspicious-admin-email-forwarding) + + + - [O365 Suspicious Rights Delegation](#o365-suspicious-rights-delegation) + + + - [O365 Suspicious User Email Forwarding](#o365-suspicious-user-email-forwarding) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [aws detect attach to role policy](#aws-detect-attach-to-role-policy) + + + - [aws detect permanent key creation](#aws-detect-permanent-key-creation) + + + - [aws detect role creation](#aws-detect-role-creation) + + + - [aws detect sts assume role abuse](#aws-detect-sts-assume-role-abuse) + + + - [aws detect sts get session token abuse](#aws-detect-sts-get-session-token-abuse) + + + - [gcp detect oauth token abuse](#gcp-detect-oauth-token-abuse) + +
## Endpoint
View + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Access LSASS Memory for Dump Creation](#access-lsass-memory-for-dump-creation) + + + + + + + - [Applying Stolen Credentials via Mimikatz modules](#applying-stolen-credentials-via-mimikatz-modules) + + + - [Applying Stolen Credentials via PowerSploit modules](#applying-stolen-credentials-via-powersploit-modules) + + + - [Assessment of Credential Strength via DSInternals modules](#assessment-of-credential-strength-via-dsinternals-modules) + + + - [Attempt To Add Certificate To Untrusted Store](#attempt-to-add-certificate-to-untrusted-store) + + + - [Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass](#attempt-to-set-default-powershell-execution-policy-to-unrestricted-or-bypass) + + + - [Attempt To Stop Security Service](#attempt-to-stop-security-service) + + + - [Attempted Credential Dump From Registry via Reg exe](#attempted-credential-dump-from-registry-via-reg-exe) + + + - [Attempted Credential Dump From Registry via Reg exe](#attempted-credential-dump-from-registry-via-reg-exe) + + + - [BCDEdit Failure Recovery Modification](#bcdedit-failure-recovery-modification) + + + - [Batch File Write to System32](#batch-file-write-to-system32) + + + - [Certutil exe certificate extraction](#certutil-exe-certificate-extraction) + + + - [Child Processes of Spoolsv exe](#child-processes-of-spoolsv-exe) + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Common Ransomware Extensions](#common-ransomware-extensions) + + + - [Common Ransomware Notes](#common-ransomware-notes) + + + - [Create Remote Thread into LSASS](#create-remote-thread-into-lsass) + + + - [Create local admin accounts using net exe](#create-local-admin-accounts-using-net-exe) + + + - [Create or delete windows shares using net exe](#create-or-delete-windows-shares-using-net-exe) + + + - [Creation of Shadow Copy](#creation-of-shadow-copy) + + + - [Creation of Shadow Copy with wmic and powershell](#creation-of-shadow-copy-with-wmic-and-powershell) + + + - [Creation of lsass Dump with Taskmgr](#creation-of-lsass-dump-with-taskmgr) + + + - [Credential Dumping via Copy Command from Shadow Copy](#credential-dumping-via-copy-command-from-shadow-copy) + + + - [Credential Dumping via Symlink to Shadow Copy](#credential-dumping-via-symlink-to-shadow-copy) + + + - [Credential Extraction indicative of FGDump and CacheDump with s option](#credential-extraction-indicative-of-fgdump-and-cachedump-with-s-option) + + + - [Credential Extraction indicative of FGDump and CacheDump with v option](#credential-extraction-indicative-of-fgdump-and-cachedump-with-v-option) + + + - [Credential Extraction indicative of Lazagne command line options](#credential-extraction-indicative-of-lazagne-command-line-options) + + + - [Credential Extraction indicative of use of DSInternals credential conversion modules](#credential-extraction-indicative-of-use-of-dsinternals-credential-conversion-modules) + + + - [Credential Extraction indicative of use of DSInternals modules](#credential-extraction-indicative-of-use-of-dsinternals-modules) + + + - [Credential Extraction indicative of use of Mimikatz modules](#credential-extraction-indicative-of-use-of-mimikatz-modules) + + + - [Credential Extraction indicative of use of PowerSploit modules](#credential-extraction-indicative-of-use-of-powersploit-modules) + + + - [Credential Extraction native Microsoft debuggers peek into the kernel](#credential-extraction-native-microsoft-debuggers-peek-into-the-kernel) + + + - [Credential Extraction native Microsoft debuggers via z command line option](#credential-extraction-native-microsoft-debuggers-via-z-command-line-option) + + + - [Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals](#credential-extraction-via-get-addbaccount-module-present-in-powersploit-and-dsinternals) + + + + + + + + + + + - [Deleting Shadow Copies](#deleting-shadow-copies) + + + + + + + + + + + + + + + + + - [Detect Activity Related to Pass the Hash Attacks](#detect-activity-related-to-pass-the-hash-attacks) + + + - [Detect Baron Samedit CVE-2021-3156](#detect-baron-samedit-cve-2021-3156) + + + - [Detect Baron Samedit CVE-2021-3156 Segfault](#detect-baron-samedit-cve-2021-3156-segfault) + + + - [Detect Baron Samedit CVE-2021-3156 via OSQuery](#detect-baron-samedit-cve-2021-3156-via-osquery) + + + - [Detect Computer Changed with Anonymous Account](#detect-computer-changed-with-anonymous-account) + + + - [Detect Credential Dumping through LSASS access](#detect-credential-dumping-through-lsass-access) + + + + + - [Detect Dump LSASS Memory using comsvcs](#detect-dump-lsass-memory-using-comsvcs) + + + - [Detect Excessive Account Lockouts From Endpoint](#detect-excessive-account-lockouts-from-endpoint) + + + - [Detect Excessive User Account Lockouts](#detect-excessive-user-account-lockouts) + + + + + + + - [Detect HTML Help Renamed](#detect-html-help-renamed) + + + - [Detect HTML Help Spawn Child Process](#detect-html-help-spawn-child-process) + + + - [Detect HTML Help URL in Command Line](#detect-html-help-url-in-command-line) + + + - [Detect HTML Help Using InfoTech Storage Handlers](#detect-html-help-using-infotech-storage-handlers) + + + + + - [Detect Kerberoasting](#detect-kerberoasting) + + + + + + + - [Detect MSHTA Url in Command Line](#detect-mshta-url-in-command-line) + + + + + + + - [Detect New Local Admin account](#detect-new-local-admin-account) + + + + + + + + + + + - [Detect Oulook exe writing a zip file](#detect-oulook-exe-writing-a--zip-file) + + + + + - [Detect Pass the Hash](#detect-pass-the-hash) + + + - [Detect Path Interception By Creation Of program exe](#detect-path-interception-by-creation-of-program-exe) + + + + + - [Detect Prohibited Applications Spawning cmd exe](#detect-prohibited-applications-spawning-cmd-exe) + + + - [Detect Prohibited Applications Spawning cmd exe](#detect-prohibited-applications-spawning-cmd-exe) + + + - [Detect PsExec With accepteula Flag](#detect-psexec-with-accepteula-flag) + + + - [Detect Rare Executables](#detect-rare-executables) + + + - [Detect Regasm Spawning a Process](#detect-regasm-spawning-a-process) + + + - [Detect Regasm with Network Connection](#detect-regasm-with-network-connection) + + + - [Detect Regasm with no Command Line Arguments](#detect-regasm-with-no-command-line-arguments) + + + - [Detect Regsvcs Spawning a Process](#detect-regsvcs-spawning-a-process) + + + - [Detect Regsvcs with Network Connection](#detect-regsvcs-with-network-connection) + + + - [Detect Regsvcs with No Command Line Arguments](#detect-regsvcs-with-no-command-line-arguments) + + + - [Detect Regsvr32 Application Control Bypass](#detect-regsvr32-application-control-bypass) + + + + + - [Detect Rundll32 Application Control Bypass - advpack](#detect-rundll32-application-control-bypass---advpack) + + + - [Detect Rundll32 Application Control Bypass - setupapi](#detect-rundll32-application-control-bypass---setupapi) + + + - [Detect Rundll32 Application Control Bypass - syssetup](#detect-rundll32-application-control-bypass---syssetup) + + + - [Detect Rundll32 Inline HTA Execution](#detect-rundll32-inline-hta-execution) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Detect Use of cmd exe to Launch Script Interpreters](#detect-use-of-cmd-exe-to-launch-script-interpreters) + + + + + + + + + + + + + + + - [Detect mshta inline hta execution](#detect-mshta-inline-hta-execution) + + + - [Detect mshta renamed](#detect-mshta-renamed) + + + + + + + - [Detect processes used for System Network Configuration Discovery](#detect-processes-used-for-system-network-configuration-discovery) + + + + + + + - [Detection of tools built by NirSoft](#detection-of-tools-built-by-nirsoft) + + + - [Disabling Remote User Account Control](#disabling-remote-user-account-control) + + + - [Dump LSASS via comsvcs DLL](#dump-lsass-via-comsvcs-dll) + + + - [Dump LSASS via procdump](#dump-lsass-via-procdump) + + + - [Dump LSASS via procdump Rename](#dump-lsass-via-procdump-rename) + + + + + + + + + + + + + + + + + + + + + + + - [Execution of File with Multiple Extensions](#execution-of-file-with-multiple-extensions) + + + + + - [File with Samsam Extension](#file-with-samsam-extension) + + + - [First Time Seen Child Process of Zoom](#first-time-seen-child-process-of-zoom) + + + - [First Time Seen Running Windows Service](#first-time-seen-running-windows-service) + + + - [First time seen command line argument](#first-time-seen-command-line-argument) + + + + + + + + + + + + + + + + + - [Hiding Files And Directories With Attrib exe](#hiding-files-and-directories-with-attrib-exe) + + + + + + + + + - [Illegal Access To User Content via PowerSploit modules](#illegal-access-to-user-content-via-powersploit-modules) + + + - [Illegal Account Creation via PowerSploit modules](#illegal-account-creation-via-powersploit-modules) + + + - [Illegal Deletion of Logs via Mimikatz modules](#illegal-deletion-of-logs-via-mimikatz-modules) + + + - [Illegal Enabling or Disabling of Accounts via DSInternals modules](#illegal-enabling-or-disabling-of-accounts-via-dsinternals-modules) + + + - [Illegal Management of Active Directory Elements and Policies via DSInternals modules](#illegal-management-of-active-directory-elements-and-policies-via-dsinternals-modules) + + + - [Illegal Management of Computers and Active Directory Elements via PowerSploit modules](#illegal-management-of-computers-and-active-directory-elements-via-powersploit-modules) + + + - [Illegal Privilege Elevation and Persistence via PowerSploit modules](#illegal-privilege-elevation-and-persistence-via-powersploit-modules) + + + - [Illegal Privilege Elevation via Mimikatz modules](#illegal-privilege-elevation-via-mimikatz-modules) + + + - [Illegal Service and Process Control via Mimikatz modules](#illegal-service-and-process-control-via-mimikatz-modules) + + + - [Illegal Service and Process Control via PowerSploit modules](#illegal-service-and-process-control-via-powersploit-modules) + + + - [Kerberoasting spn request with RC4 encryption](#kerberoasting-spn-request-with-rc4-encryption) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [MacOS - Re-opened Applications](#macos---re-opened-applications) + + + - [Malicious PowerShell Process - Connect To Internet With Hidden Window](#malicious-powershell-process---connect-to-internet-with-hidden-window) + + + - [Malicious PowerShell Process - Encoded Command](#malicious-powershell-process---encoded-command) + + + - [Malicious PowerShell Process - Execution Policy Bypass](#malicious-powershell-process---execution-policy-bypass) + + + + + - [Malicious PowerShell Process With Obfuscation Techniques](#malicious-powershell-process-with-obfuscation-techniques) + + + + + + + - [Monitor Registry Keys for Print Monitors](#monitor-registry-keys-for-print-monitors) + + + + + - [More than usual number of LOLBAS applications in short time period](#more-than-usual-number-of-lolbas-applications-in-short-time-period) + + + + + - [NLTest Domain Trust Discovery](#nltest-domain-trust-discovery) + + + + + + + - [Ntdsutil export ntds](#ntdsutil-export-ntds) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Overwriting Accessibility Binaries](#overwriting-accessibility-binaries) + + + + + - [Probing Access with Stolen Credentials via PowerSploit modules](#probing-access-with-stolen-credentials-via-powersploit-modules) + + + - [Process Creating LNK file in Suspicious Location](#process-creating-lnk-file-in-suspicious-location) + + + - [Process Execution via WMI](#process-execution-via-wmi) + + + - [Processes Tapping Keyboard Events](#processes-tapping-keyboard-events) + + + + + - [Processes launching netsh](#processes-launching-netsh) + + + + + + + + + + + - [Rare Parent-Child Process Relationship](#rare-parent-child-process-relationship) + + + - [Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules](#reconnaissance-and-access-to-accounts-groups-and-policies-via-powersploit-modules) + + + - [Reconnaissance and Access to Accounts and Groups via Mimikatz modules](#reconnaissance-and-access-to-accounts-and-groups-via-mimikatz-modules) + + + - [Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules](#reconnaissance-and-access-to-active-directoty-infrastructure-via-powersploit-modules) + + + - [Reconnaissance and Access to Computers and Domains via PowerSploit modules](#reconnaissance-and-access-to-computers-and-domains-via-powersploit-modules) + + + - [Reconnaissance and Access to Computers via Mimikatz modules](#reconnaissance-and-access-to-computers-via-mimikatz-modules) + + + - [Reconnaissance and Access to Operating System Elements via PowerSploit modules](#reconnaissance-and-access-to-operating-system-elements-via-powersploit-modules) + + + - [Reconnaissance and Access to Processes and Services via Mimikatz modules](#reconnaissance-and-access-to-processes-and-services-via-mimikatz-modules) + + + - [Reconnaissance and Access to Shared Resources via Mimikatz modules](#reconnaissance-and-access-to-shared-resources-via-mimikatz-modules) + + + - [Reconnaissance and Access to Shared Resources via PowerSploit modules](#reconnaissance-and-access-to-shared-resources-via-powersploit-modules) + + + - [Reconnaissance of Access and Persistence Opportunities via PowerSploit modules](#reconnaissance-of-access-and-persistence-opportunities-via-powersploit-modules) + + + - [Reconnaissance of Connectivity via PowerSploit modules](#reconnaissance-of-connectivity-via-powersploit-modules) + + + - [Reconnaissance of Credential Stores and Services via Mimikatz modules](#reconnaissance-of-credential-stores-and-services-via-mimikatz-modules) + + + - [Reconnaissance of Defensive Tools via PowerSploit modules](#reconnaissance-of-defensive-tools-via-powersploit-modules) + + + - [Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules](#reconnaissance-of-privilege-escalation-opportunities-via-powersploit-modules) + + + - [Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules](#reconnaissance-of-process-or-service-hijacking-opportunities-via-mimikatz-modules) + + + - [Reg exe Manipulating Windows Services Registry Keys](#reg-exe-manipulating-windows-services-registry-keys) + + + + + - [Registry Keys Used For Persistence](#registry-keys-used-for-persistence) + + + - [Registry Keys Used For Privilege Escalation](#registry-keys-used-for-privilege-escalation) + + + - [Registry Keys for Creating SHIM Databases](#registry-keys-for-creating-shim-databases) + + + + + + + - [Remote Desktop Process Running On System](#remote-desktop-process-running-on-system) + + + - [Remote Process Instantiation via WMI](#remote-process-instantiation-via-wmi) + + + + + + + - [RunDLL Loading DLL By Ordinal](#rundll-loading-dll-by-ordinal) + + + - [Ryuk Test Files Detected](#ryuk-test-files-detected) + + + + + + + + + - [Samsam Test File Write](#samsam-test-file-write) + + + - [Sc exe Manipulating Windows Services](#sc-exe-manipulating-windows-services) + + + - [Scheduled Task Deleted Or Created via CMD](#scheduled-task-deleted-or-created-via-cmd) + + + + + - [Schtasks scheduling job on remote system](#schtasks-scheduling-job-on-remote-system) + + + - [Schtasks used for forcing a reboot](#schtasks-used-for-forcing-a-reboot) + + + - [Script Execution via WMI](#script-execution-via-wmi) + + + - [Setting Credentials via DSInternals modules](#setting-credentials-via-dsinternals-modules) + + + - [Setting Credentials via Mimikatz modules](#setting-credentials-via-mimikatz-modules) + + + - [Setting Credentials via PowerSploit modules](#setting-credentials-via-powersploit-modules) + + + - [Shim Database File Creation](#shim-database-file-creation) + + + - [Shim Database Installation With Suspicious Parameters](#shim-database-installation-with-suspicious-parameters) + + + - [Short Lived Windows Accounts](#short-lived-windows-accounts) + + + - [Single Letter Process On Endpoint](#single-letter-process-on-endpoint) + + + + + - [Spike in File Writes](#spike-in-file-writes) + + + + + - [Sunburst Correlation DLL and Network Event](#sunburst-correlation-dll-and-network-event) + + + + + + + + + + + + + + + - [Suspicious MSBuild Rename](#suspicious-msbuild-rename) + + + - [Suspicious MSBuild Spawn](#suspicious-msbuild-spawn) + + + - [Suspicious Reg exe Process](#suspicious-reg-exe-process) + + + - [Suspicious Regsvr32 Register Suspicious Path](#suspicious-regsvr32-register-suspicious-path) + + + - [Suspicious Rundll32 Rename](#suspicious-rundll32-rename) + + + - [Suspicious Rundll32 StartW](#suspicious-rundll32-startw) + + + - [Suspicious Rundll32 dllregisterserver](#suspicious-rundll32-dllregisterserver) + + + - [Suspicious Rundll32 no CommandLine Arguments](#suspicious-rundll32-no-commandline-arguments) + + + - [Suspicious microsoft workflow compiler rename](#suspicious-microsoft-workflow-compiler-rename) + + + - [Suspicious microsoft workflow compiler usage](#suspicious-microsoft-workflow-compiler-usage) + + + - [Suspicious msbuild path](#suspicious-msbuild-path) + + + - [Suspicious mshta child process](#suspicious-mshta-child-process) + + + - [Suspicious mshta spawn](#suspicious-mshta-spawn) + + + - [Suspicious wevtutil Usage](#suspicious-wevtutil-usage) + + + + + - [Suspicious writes to windows Recycle Bin](#suspicious-writes-to-windows-recycle-bin) + + + - [System Information Discovery Detection](#system-information-discovery-detection) + + + - [System Process Running from Unexpected Location](#system-process-running-from-unexpected-location) + + + - [System Processes Run From Unexpected Locations](#system-processes-run-from-unexpected-locations) + + + + + - [USN Journal Deletion](#usn-journal-deletion) + + + + + - [Unload Sysmon Filter Driver](#unload-sysmon-filter-driver) + + + + + + + - [Unusually Long Command Line](#unusually-long-command-line) + + + - [Unusually Long Command Line](#unusually-long-command-line) + + + - [Unusually Long Command Line - MLTK](#unusually-long-command-line---mltk) + + + + + - [WBAdmin Delete System Backups](#wbadmin-delete-system-backups) + + + - [WMI Permanent Event Subscription](#wmi-permanent-event-subscription) + + + - [WMI Permanent Event Subscription - Sysmon](#wmi-permanent-event-subscription---sysmon) + + + - [WMI Temporary Event Subscription](#wmi-temporary-event-subscription) + + + + + + + + + + + - [Windows AdFind Exe](#windows-adfind-exe) + + + + + - [Windows Event Log Cleared](#windows-event-log-cleared) + + + - [Windows Security Account Manager Stopped](#windows-security-account-manager-stopped) + + + + + + + + + + + + + + + + + +
## Network
View + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [DNS Query Length Outliers - MLTK](#dns-query-length-outliers---mltk) + + + - [DNS Query Length With High Standard Deviation](#dns-query-length-with-high-standard-deviation) + + + + + - [DNS record changed](#dns-record-changed) + + + + + + + - [Detect ARP Poisoning](#detect-arp-poisoning) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Detect IPv6 Network Infrastructure Threats](#detect-ipv6-network-infrastructure-threats) + + + + + - [Detect Large Outbound ICMP Packets](#detect-large-outbound-icmp-packets) + + + + + + + + + + + + + + + + + + + + + + + - [Detect Outbound SMB Traffic](#detect-outbound-smb-traffic) + + + + + + + - [Detect Port Security Violation](#detect-port-security-violation) + + + + + + + + + + + + + + + + + + + + + + + + + - [Detect Rogue DHCP Server](#detect-rogue-dhcp-server) + + + + + + + + + + + + + - [Detect SNICat SNI Exfiltration](#detect-snicat-sni-exfiltration) + + + - [Detect Software Download To Network Device](#detect-software-download-to-network-device) + + + + + + + + + + + + + + + + + - [Detect Traffic Mirroring](#detect-traffic-mirroring) + + + + + - [Detect Unauthorized Assets by MAC address](#detect-unauthorized-assets-by-mac-address) + + + + + - [Detect Windows DNS SIGRed via Splunk Stream](#detect-windows-dns-sigred-via-splunk-stream) + + + - [Detect Windows DNS SIGRed via Zeek](#detect-windows-dns-sigred-via-zeek) + + + - [Detect Zerologon via Zeek](#detect-zerologon-via-zeek) + + + + + - [Detect hosts connecting to dynamic domain providers](#detect-hosts-connecting-to-dynamic-domain-providers) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Excessive DNS Failures](#excessive-dns-failures) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Hosts receiving high volume of network traffic from email server](#hosts-receiving-high-volume-of-network-traffic-from-email-server) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Large Volume of DNS ANY Queries](#large-volume-of-dns-any-queries) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Prohibited Network Traffic Allowed](#prohibited-network-traffic-allowed) + + + + + - [Protocol or Port Mismatch](#protocol-or-port-mismatch) + + + - [Protocols passing authentication in cleartext](#protocols-passing-authentication-in-cleartext) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Remote Desktop Network Bruteforce](#remote-desktop-network-bruteforce) + + + - [Remote Desktop Network Traffic](#remote-desktop-network-traffic) + + + + + + + + + + + + + + + - [SMB Traffic Spike](#smb-traffic-spike) + + + - [SMB Traffic Spike - MLTK](#smb-traffic-spike---mltk) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [TOR Traffic](#tor-traffic) + + + + + + + + + + + + + + + + + + + - [Unusually Long Content-Type Length](#unusually-long-content-type-length) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
## Application
View + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Detect New Login Attempts to Routers](#detect-new-login-attempts-to-routers) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Email Attachments With Lots Of Spaces](#email-attachments-with-lots-of-spaces) + + + - [Email files written outside of the Outlook directory](#email-files-written-outside-of-the-outlook-directory) + + + - [Email servers sending high volume traffic to hosts](#email-servers-sending-high-volume-traffic-to-hosts) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Monitor Email For Brand Abuse](#monitor-email-for-brand-abuse) + + + + + + + + + - [Multiple Okta Users With Invalid Credentials From The Same IP](#multiple-okta-users-with-invalid-credentials-from-the-same-ip) + + + + + + + - [No Windows Updates in a time frame](#no-windows-updates-in-a-time-frame) + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Okta Account Lockout Events](#okta-account-lockout-events) + + + - [Okta Failed SSO Attempts](#okta-failed-sso-attempts) + + + - [Okta User Logins From Multiple Cities](#okta-user-logins-from-multiple-cities) + + + + + + + + + - [Phishing Email Detection by Machine Learning Method - SSA](#phishing-email-detection-by-machine-learning-method---ssa) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Spectre and Meltdown Vulnerable Systems](#spectre-and-meltdown-vulnerable-systems) + + + + + + + + + + + + + - [Suspicious Email - UBA Anomaly](#suspicious-email---uba-anomaly) + + + - [Suspicious Email Attachment Extensions](#suspicious-email-attachment-extensions) + + + + + - [Suspicious Java Classes](#suspicious-java-classes) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Web Servers Executing Suspicious Processes](#web-servers-executing-suspicious-processes) + + + + + + + + + + + + + + + + + + + + + + + + + +
## Web
View + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Detect F5 TMUI RCE CVE-2020-5902](#detect-f5-tmui-rce-cve-2020-5902) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Detect attackers scanning for vulnerable JBoss servers](#detect-attackers-scanning-for-vulnerable-jboss-servers) + + + + + - [Detect malicious requests to exploit JBoss servers](#detect-malicious-requests-to-exploit-jboss-servers) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Monitor Web Traffic For Brand Abuse](#monitor-web-traffic-for-brand-abuse) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [SQL Injection with Long URLs](#sql-injection-with-long-urls) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Supernova Webshell](#supernova-webshell) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [Web Fraud - Account Harvesting](#web-fraud---account-harvesting) + + + - [Web Fraud - Anomalous User Clickspeed](#web-fraud---anomalous-user-clickspeed) + + + - [Web Fraud - Password Sharing Across Accounts](#web-fraud---password-sharing-across-accounts) + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ ### AWS Cloud Provisioning From Previously Unseen City This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1535 +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2018-03-16
@@ -360,15 +4349,35 @@ This search looks for AWS provisioning activities from previously unseen cities. #### Search ``` -`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_city_filter` +`cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search City=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by City +| eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newCity=1 +| table City] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, City, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_city_filter` ``` #### Associated Analytic Story +* AWS Suspicious Provisioning Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -377,24 +4386,28 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References + #### Test Dataset + _version_: 1
--- + ### AWS Cloud Provisioning From Previously Unseen Country This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1535 +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2018-03-16
@@ -402,15 +4415,35 @@ This search looks for AWS provisioning activities from previously unseen countri #### Search ``` -`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_country_filter` +`cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Country=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by Country +| eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newCountry=1 +| table Country] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, Country, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_country_filter` ``` #### Associated Analytic Story +* AWS Suspicious Provisioning Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -419,18 +4452,22 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References + #### Test Dataset + _version_: 1
--- + ### AWS Cloud Provisioning From Previously Unseen IP Address This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. @@ -444,15 +4481,33 @@ This search looks for AWS provisioning activities from previously unseen IP addr #### Search ``` -`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_ip_address_filter` +`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Country=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress +| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newIP=1 +| table sourceIPAddress] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_ip_address_filter` ``` #### Associated Analytic Story +* AWS Suspicious Provisioning Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -460,24 +4515,28 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References + #### Test Dataset + _version_: 1
--- + ### AWS Cloud Provisioning From Previously Unseen Region This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1535 +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2018-03-16
@@ -485,15 +4544,35 @@ This search looks for AWS provisioning activities from previously unseen regions #### Search ``` -`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_region_filter` +`cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Region=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by Region +| eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newRegion=1 +| table Region] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, Region, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_region_filter` ``` #### Associated Analytic Story +* AWS Suspicious Provisioning Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -502,23 +4581,27 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References + #### Test Dataset + _version_: 1
--- + ### AWS Cross Account Activity From Previously Unseen Account This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. This search is deprecated and have been translated to use the latest Authentication Datamodel. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Authentication - **ATT&CK**: - **Last Updated**: 2020-05-28 @@ -527,41 +4610,62 @@ This search looks for AssumeRole events where an IAM role in a different account #### Search ``` -| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime | eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") | where status = "New Cross Account Activity" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_cross_account_activity_from_previously_unseen_account_filter` + +| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role +| `drop_dm_object_name(Authentication)` +| rex field=user_role "arn:aws:sts:*:(?.*):" +| where vendor_account != dest_account +| rename vendor_account as requestingAccountId dest_account as requestedAccountId +| lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime +| eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") +| where status = "New Cross Account Activity" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `aws_cross_account_activity_from_previously_unseen_account_filter` ``` #### Associated Analytic Story +* Suspicious Cloud Authentication Activities + + #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen AWS Cross Account Activity - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen AWS Cross Account Activity - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `aws_cross_account_activity_from_previously_unseen_account_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1 --- + ### AWS Detect Users creating keys with encrypt policy without MFA This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1486 +- **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/) - **Last Updated**: 2021-01-11
@@ -569,15 +4673,30 @@ This search provides detection of KMS keys which action kms:Encrypt is accessibl #### Search ``` -`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy | spath input=requestParameters.policy output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | spath input=key_policy_statements output=key_policy_action_2 path=Action{} | eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) | spath input=key_policy_statements output=key_policy_principal path=Principal.AWS | search key_policy_action="kms:Encrypt" AND key_policy_principal="*" | stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` +`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy +| spath input=requestParameters.policy output=key_policy_statements path=Statement{} +| mvexpand key_policy_statements +| spath input=key_policy_statements output=key_policy_action_1 path=Action +| spath input=key_policy_statements output=key_policy_action_2 path=Action{} +| eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) +| spath input=key_policy_statements output=key_policy_principal path=Principal.AWS +| search key_policy_action="kms:Encrypt" AND key_policy_principal="*" +| stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` ``` #### Associated Analytic Story +* Ransomware Cloud + + #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -586,27 +4705,35 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Kill Chain Phases + #### Known False Positives unknown #### References + * https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ + * https://github.com/d1vious/git-wild-hunt + * https://www.youtube.com/watch?v=PgzNib37g0M + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json + _version_: 1
--- + ### AWS Detect Users with KMS keys performing encryption S3 This search provides detection of users with KMS keys performing encryption specifically against S3 buckets. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1486 +- **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/) - **Last Updated**: 2021-01-11
@@ -614,15 +4741,24 @@ This search provides detection of users with KMS keys performing encryption spec #### Search ``` -`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" | rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter` +`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" +| rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file +| stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|`aws_detect_users_with_kms_keys_performing_encryption_s3_filter` ``` #### Associated Analytic Story +* Ransomware Cloud + + #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -631,21 +4767,29 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Kill Chain Phases + #### Known False Positives bucket with S3 encryption #### References + * https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ + * https://github.com/d1vious/git-wild-hunt + * https://www.youtube.com/watch?v=PgzNib37g0M + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json + _version_: 1
--- + ### AWS EKS Kubernetes cluster sensitive object access This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets @@ -659,40 +4803,52 @@ This search provides information on Kubernetes accounts accessing sensitve objec #### Search ``` -`aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 |table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`aws_eks_kubernetes_cluster_sensitive_object_access_filter` +`aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 +|table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason +|dedup user.username user.groups{} +|`aws_eks_kubernetes_cluster_sensitive_object_access_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Object Access Activity + + #### How To Implement You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. This search works with cloudwatch logs. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. #### References + #### Test Dataset + _version_: 1 --- + ### AWS Network Access Control List Created with All Open Ports The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1562.007 +- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562.007/) - **Last Updated**: 2021-01-11
@@ -700,15 +4856,27 @@ The search looks for CloudTrail events to detect if any network ACLs were create #### Search ``` -`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 | eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' | where port_range>1024] | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter` +`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 +| append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 +| eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' +| where port_range>1024] +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `aws_network_access_control_list_created_with_all_open_ports_filter` ``` #### Associated Analytic Story +* AWS Network ACL Activity + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -716,26 +4884,32 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1562.007 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/aws_cloudtrail_events.json + _version_: 2
--- + ### AWS Network Access Control List Deleted Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1562.007 +- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562.007/) - **Last Updated**: 2021-01-12
@@ -743,15 +4917,24 @@ Enforcing network-access controls is one of the defensive mechanisms used by clo #### Search ``` -`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_deleted_filter` +`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `aws_network_access_control_list_deleted_filter` ``` #### Associated Analytic Story +* AWS Network ACL Activity + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -759,26 +4942,32 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1562.007 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It's possible that a user has legitimately deleted a network ACL. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/aws_cloudtrail_events.json + _version_: 2
--- + ### AWS SAML Access by Provider User and Principal This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2021-01-26
@@ -786,15 +4975,23 @@ This search provides specific SAML access from specific Service Provider, user a #### Search ``` -`cloudtrail` eventName=Assumerolewithsaml | stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_access_by_provider_user_and_principal_filter` +`cloudtrail` eventName=Assumerolewithsaml +| stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|`aws_saml_access_by_provider_user_and_principal_filter` ``` #### Associated Analytic Story +* Cloud Federated Credential Abuse + + #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -803,28 +5000,37 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Kill Chain Phases + #### Known False Positives Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks. #### References + * https://us-cert.cisa.gov/ncas/alerts/aa21-008a + * https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + * https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + * https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/assume_role_with_saml/assume_role_with_saml.json + _version_: 1
--- + ### AWS SAML Update identity provider This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2021-01-26
@@ -832,15 +5038,23 @@ This search provides detection of updates to SAML provider in AWS. Updates to SA #### Search ``` -`cloudtrail` eventName=UpdateSAMLProvider | stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_update_identity_provider_filter` +`cloudtrail` eventName=UpdateSAMLProvider +| stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|`aws_saml_update_identity_provider_filter` ``` #### Associated Analytic Story +* Cloud Federated Credential Abuse + + #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -849,28 +5063,37 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Kill Chain Phases + #### Known False Positives Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored. #### References + * https://us-cert.cisa.gov/ncas/alerts/aa21-008a + * https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + * https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + * https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json + _version_: 1
--- + ### Abnormally High AWS Instances Launched by User This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.004 +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-07-21
@@ -878,15 +5101,30 @@ This search looks for CloudTrail events where a user successfully launches an ab #### Search ``` -`cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev | `abnormally_high_aws_instances_launched_by_user_filter` +`cloudtrail` eventName=RunInstances errorCode=success +| bucket span=10m _time +| stats count AS instances_launched by _time userName +| eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev +| eval threshold_value = 4 +| eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) +| search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") +| eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) +| table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev +| `abnormally_high_aws_instances_launched_by_user_filter` ``` #### Associated Analytic Story +* AWS Cryptomining + +* Suspicious AWS EC2 Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -894,25 +5132,30 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. #### References + #### Test Dataset + _version_: 2
--- + ### Abnormally High AWS Instances Launched by User - MLTK This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.004 +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-07-21
@@ -920,15 +5163,26 @@ This search looks for CloudTrail events where a user successfully launches an ab #### Search ``` -`cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | apply ec2_excessive_runinstances_v1 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 +`cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` +| bucket span=10m _time +| stats count as instances_launched by _time src_user +| apply ec2_excessive_runinstances_v1 +| rename "IsOutlier(instances_launched)" as isOutlier +| where isOutlier=1 ``` #### Associated Analytic Story +* AWS Cryptomining + +* Suspicious AWS EC2 Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -936,25 +5190,30 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. #### References + #### Test Dataset + _version_: 2
--- + ### Abnormally High AWS Instances Terminated by User This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.004 +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-07-21
@@ -962,15 +5221,28 @@ This search looks for CloudTrail events where an abnormally high number of insta #### Search ``` -`cloudtrail` eventName=TerminateInstances errorCode=success | bucket span=10m _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev | `abnormally_high_aws_instances_terminated_by_user_filter` +`cloudtrail` eventName=TerminateInstances errorCode=success +| bucket span=10m _time +| stats count AS instances_terminated by _time userName +| eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev +| eval threshold_value = 4 +| eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) +| search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") +| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) +|table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev +| `abnormally_high_aws_instances_terminated_by_user_filter` ``` #### Associated Analytic Story +* Suspicious AWS EC2 Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -978,25 +5250,30 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. #### References + #### Test Dataset + _version_: 2
--- + ### Abnormally High AWS Instances Terminated by User - MLTK This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.004 +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-07-21
@@ -1004,15 +5281,24 @@ This search looks for CloudTrail events where a user successfully terminates an #### Search ``` -`cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | apply ec2_excessive_terminateinstances_v1 | rename "IsOutlier(instances_terminated)" as isOutlier | where isOutlier=1 +`cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` +| bucket span=10m _time +| stats count as instances_terminated by _time src_user +| apply ec2_excessive_terminateinstances_v1 +| rename "IsOutlier(instances_terminated)" as isOutlier +| where isOutlier=1 ``` #### Associated Analytic Story +* Suspicious AWS EC2 Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1020,25 +5306,30 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. #### References + #### Test Dataset + _version_: 2
--- + ### Abnormally High Number Of Cloud Infrastructure API Calls This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 +- **Data Models**: Change +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-09-07
@@ -1046,15 +5337,35 @@ This search will detect a spike in the number of API calls made to your cloud in #### Search ``` -| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_api_calls_v1 threshold=0.005 | rename "IsOutlier(api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold = api_calls - expected_upper_threshold | table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` + +| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] +| where cardinality >=16 +| apply cloud_excessive_api_calls_v1 threshold=0.005 +| rename "IsOutlier(api_calls)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| where api_calls > expected_upper_threshold +| eval distance_from_threshold = api_calls - expected_upper_threshold +| table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold +| `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` ``` #### Associated Analytic Story +* Suspicious Cloud User Activities + + #### How To Implement You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to create the probability density function. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1062,26 +5373,32 @@ You must be ingesting your cloud infrastructure logs. You also must run the base | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Abnormally High Number Of Cloud Instances Destroyed This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 +- **Data Models**: Change +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-08-21
@@ -1089,15 +5406,34 @@ This search finds for the number successfully destroyed cloud instances for ever #### Search ``` -| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] | where cardinality >=16 | apply cloud_excessive_instances_destroyed_v1 threshold=0.005 | rename "IsOutlier(instances_destroyed)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_destroyed - expected_upper_threshold | table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter` + +| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] +| where cardinality >=16 +| apply cloud_excessive_instances_destroyed_v1 threshold=0.005 +| rename "IsOutlier(instances_destroyed)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| eval distance_from_threshold = instances_destroyed - expected_upper_threshold +| table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id +| `abnormally_high_number_of_cloud_instances_destroyed_filter` ``` #### Associated Analytic Story +* Suspicious Cloud Instance Activities + + #### How To Implement You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability density function. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1105,25 +5441,30 @@ You must be ingesting your cloud infrastructure logs. You also must run the base | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Many service accounts configured within a cloud infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. #### References + #### Test Dataset + _version_: 1
--- + ### Abnormally High Number Of Cloud Instances Launched This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 +- **Data Models**: Change +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-08-21
@@ -1131,15 +5472,36 @@ This search finds for the number successfully created cloud instances for every #### Search ``` -| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] | where cardinality >=16 | apply cloud_excessive_instances_created_v1 threshold=0.005 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_launched - expected_upper_threshold | table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_launched_filter` + +| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] +| where cardinality >=16 +| apply cloud_excessive_instances_created_v1 threshold=0.005 +| rename "IsOutlier(instances_launched)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| eval distance_from_threshold = instances_launched - expected_upper_threshold +| table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id +| `abnormally_high_number_of_cloud_instances_launched_filter` ``` #### Associated Analytic Story +* Cloud Cryptomining + +* Suspicious Cloud Instance Activities + + #### How To Implement You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Launched` to create the probability density function. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1147,25 +5509,30 @@ You must be ingesting your cloud infrastructure logs. You also must run the base | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. #### References + #### Test Dataset + _version_: 2
--- + ### Abnormally High Number Of Cloud Security Group API Calls This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 +- **Data Models**: Change +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-09-07
@@ -1173,15 +5540,35 @@ This search will detect a spike in the number of API calls made to your cloud in #### Search ``` -| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 | rename "IsOutlier(security_group_api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where security_group_api_calls > expected_upper_threshold | eval distance_from_threshold = security_group_api_calls - expected_upper_threshold | table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter` + +| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] +| where cardinality >=16 +| apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 +| rename "IsOutlier(security_group_api_calls)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| where security_group_api_calls > expected_upper_threshold +| eval distance_from_threshold = security_group_api_calls - expected_upper_threshold +| table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold +| `abnormally_high_number_of_cloud_security_group_api_calls_filter` ``` #### Associated Analytic Story +* Suspicious Cloud User Activities + + #### How To Implement You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to create the probability density function model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1189,26 +5576,32 @@ You must be ingesting your cloud infrastructure logs. You also must run the base | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Access LSASS Memory for Dump Creation Detect memory dumping of the LSASS process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1003.001 +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2019-12-06
@@ -1216,15 +5609,24 @@ Detect memory dumping of the LSASS process. #### Search ``` -`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter` +`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `access_lsass_memory_for_dump_creation_filter` ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1232,27 +5634,34 @@ This search requires Sysmon Logs and a Sysmon configuration, which includes Even | T1003.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. #### References + * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + _version_: 2
--- + ### Amazon EKS Kubernetes Pod scan detection This search provides detection information on unauthenticated requests against Kubernetes' Pods API - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1526 +- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) - **Last Updated**: 2020-04-15
@@ -1260,15 +5669,24 @@ This search provides detection information on unauthenticated requests against K #### Search ``` -`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter` +`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" +| rename source as cluster_name sourceIPs{} as src_ip +| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `amazon_eks_kubernetes_pod_scan_detection_filter` ``` #### Associated Analytic Story +* Kubernetes Scanning Activity + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1276,25 +5694,30 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1526 | x | x | #### Kill Chain Phases + * Reconnaissance + #### Known False Positives Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. #### References + #### Test Dataset + _version_: 1
--- + ### Amazon EKS Kubernetes cluster scan detection This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1526 +- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) - **Last Updated**: 2020-04-15
@@ -1302,15 +5725,24 @@ This search provides information of unauthenticated requests via user agent, and #### Search ``` -`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`amazon_eks_kubernetes_cluster_scan_detection_filter` +`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" +| rename sourceIPs{} as src_ip +| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +|`amazon_eks_kubernetes_cluster_scan_detection_filter` ``` #### Associated Analytic Story +* Kubernetes Scanning Activity + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1318,25 +5750,30 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1526 | x | x | #### Kill Chain Phases + * Reconnaissance + #### Known False Positives Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. #### References + #### Test Dataset + _version_: 1
--- + ### Applying Stolen Credentials via Mimikatz modules This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1055, T1068, T1078, T1098, T1134, T1543, T1547, T1548, T1554, T1556, T1558 +- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/), [T1134](https://attack.mitre.org/techniques/T1134/), [T1543](https://attack.mitre.org/techniques/T1543/), [T1547](https://attack.mitre.org/techniques/T1547/), [T1548](https://attack.mitre.org/techniques/T1548/), [T1554](https://attack.mitre.org/techniques/T1554/), [T1556](https://attack.mitre.org/techniques/T1556/), [T1558](https://attack.mitre.org/techniques/T1558/) - **Last Updated**: 2020-11-03
@@ -1344,21 +5781,32 @@ This detection indicates use of Mimikatz modules that facilitate Pass-the-Token #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)kerberos::ptt/)=true OR match_regex(cmd_line, /(?i)kerberos::golden/)=true OR match_regex(cmd_line, /(?i)kerberos::silver/)=true OR match_regex(cmd_line, /(?i)misc::skeleton/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)kerberos::ptt/)=true OR match_regex(cmd_line, /(?i)kerberos::golden/)=true OR match_regex(cmd_line, /(?i)kerberos::silver/)=true OR match_regex(cmd_line, /(?i)misc::skeleton/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -1376,27 +5824,34 @@ You must be ingesting Windows Security logs from devices of interest, including | T1558 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + * https://adsecurity.org/?p=1275 + #### Test Dataset + _version_: 1
--- + ### Applying Stolen Credentials via PowerSploit modules Stolen credentials are applied by methods such as user impersonation, credential injection, spoofing of authentication processes or getting hold of critical accounts. This detection indicates such activities carried out by PowerSploit exploit kit APIs. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1055, T1068, T1078, T1098, T1134, T1543, T1547, T1548, T1554, T1556, T1558 +- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/), [T1134](https://attack.mitre.org/techniques/T1134/), [T1543](https://attack.mitre.org/techniques/T1543/), [T1547](https://attack.mitre.org/techniques/T1547/), [T1548](https://attack.mitre.org/techniques/T1548/), [T1554](https://attack.mitre.org/techniques/T1554/), [T1556](https://attack.mitre.org/techniques/T1556/), [T1558](https://attack.mitre.org/techniques/T1558/) - **Last Updated**: 2020-11-03
@@ -1404,21 +5859,32 @@ Stolen credentials are applied by methods such as user impersonation, credential #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-CredentialInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-TokenManipulation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserImpersonation/)=true OR match_regex(cmd_line, /(?i)Get-System/)=true OR match_regex(cmd_line, /(?i)Invoke-RevertToSelf/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-CredentialInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-TokenManipulation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserImpersonation/)=true OR match_regex(cmd_line, /(?i)Get-System/)=true OR match_regex(cmd_line, /(?i)Invoke-RevertToSelf/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -1436,26 +5902,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1558 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Assessment of Credential Strength via DSInternals modules This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1078, T1098, T1087, T1201, T1552, T1555 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/), [T1087](https://attack.mitre.org/techniques/T1087/), [T1201](https://attack.mitre.org/techniques/T1201/), [T1552](https://attack.mitre.org/techniques/T1552/), [T1555](https://attack.mitre.org/techniques/T1555/) - **Last Updated**: 2020-11-03
@@ -1463,21 +5935,32 @@ This detection identifies use of DSInternals modules that verify password streng #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-PasswordQuality/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-PasswordQuality/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -1490,26 +5973,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1555 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/MichaelGrafnetter/DSInternals + #### Test Dataset + _version_: 1
--- + ### Attempt To Add Certificate To Untrusted Store Attempt to add a certificate to the certificate store - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1553.004 +- **Data Models**: Endpoint +- **ATT&CK**: [T1553.004](https://attack.mitre.org/techniques/T1553.004/) - **Last Updated**: 2020-11-03
@@ -1517,15 +6006,24 @@ Attempt to add a certificate to the certificate store #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `attempt_to_add_certificate_to_untrusted_store_filter` ``` #### Associated Analytic Story +* Disabling Security Tools + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1533,27 +6031,34 @@ You must be ingesting data that records process activity from your hosts to popu | T1553.004 | x | x | #### Kill Chain Phases + * Installation + * Actions on Objectives + #### Known False Positives There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log + _version_: 6
--- + ### Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/) - **Last Updated**: 2020-11-06
@@ -1561,15 +6066,26 @@ Monitor for changes of the ExecutionPolicy in the registry to the values "unrest #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest +| `drop_dm_object_name(Registry)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter` ``` #### Associated Analytic Story +* Malicious PowerShell + +* Credential Dumping + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1577,27 +6093,34 @@ You must be ingesting data that records process activity from your hosts to popu | T1059.001 | x | x | #### Kill Chain Phases + * Installation + * Actions on Objectives + #### Known False Positives Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log + _version_: 6
--- + ### Attempt To Stop Security Service This search looks for attempts to stop security-related services on the endpoint. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562.001/) - **Last Updated**: 2020-07-21
@@ -1605,15 +6128,26 @@ This search looks for attempts to stop security-related services on the endpoint #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |lookup security_services_lookup service as process OUTPUTNEW category, description | search category=security | `attempt_to_stop_security_service_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|lookup security_services_lookup service as process OUTPUTNEW category, description +| search category=security +| `attempt_to_stop_security_service_filter` ``` #### Associated Analytic Story +* Disabling Security Tools + + #### How To Implement You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1621,70 +6155,34 @@ You must be ingesting data that records the file-system activity from your hosts | T1562.001 | x | x | #### Kill Chain Phases + * Installation + * Actions on Objectives + #### Known False Positives None identified. Attempts to disable security-related services should be identified and understood. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log + _version_: 3
--- -### Attempted Credential Dump From Registry via Reg exe -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.002 -- **Last Updated**: 2019-12-02 - -
- View - -#### Search -``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_exe_filter` -``` -#### Associated Analytic Story - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.002 | x | x | - -#### Kill Chain Phases -* Actions on Objectives - -#### Known False Positives -None identified. - -#### References - -#### Test Dataset -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - -_version_: 4 -
- ---- ### Attempted Credential Dump From Registry via Reg exe Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003 +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-6-04
@@ -1692,20 +6190,36 @@ Monitor for execution of reg.exe with parameters specifying an export of keys th #### Search ``` - | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) | where process_name="cmd.exe" OR process_name="reg.exe" | where cmd_line != null AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) +| where process_name="cmd.exe" OR process_name="reg.exe" +| where cmd_line != null AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement You must be ingesting windows endpoint data that tracks process activity, including parent-child relationships from your endpoints. #### Required fields + * process_name + * _time + * dest_device_id + * dest_user_id + * process + #### ATT&CK | ID | technique | Tactic | @@ -1713,26 +6227,90 @@ You must be ingesting windows endpoint data that tracks process activity, includ | T1003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/splunk/security_content/blob/55a17c65f9f56c2220000b62701765422b46125d/detections/attempted_credential_dump_from_registry_via_reg_exe.yml + #### Test Dataset + _version_: 1
--- + +### Attempted Credential Dump From Registry via Reg exe +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Data Models**: Endpoint +- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/) +- **Last Updated**: 2019-12-02 + +
+ View + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` +``` +#### Associated Analytic Story + +* Credential Dumping + + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required fields + + +#### ATT&CK + +| ID | technique | Tactic | +| ----------- | ----------- |:-------------:| +| T1003.002 | x | x | + +#### Kill Chain Phases + +* Actions on Objectives + + +#### Known False Positives +None identified. + +#### References + + +#### Test Dataset + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + + +_version_: 4 +
+ +--- + ### BCDEdit Failure Recovery Modification This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1490 +- **Data Models**: Endpoint +- **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/) - **Last Updated**: 2020-12-21
@@ -1740,15 +6318,26 @@ This search looks for flags passed to bcdedit.exe modifications to the built-in #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bcdedit_failure_recovery_modification_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `bcdedit_failure_recovery_modification_filter` ``` #### Associated Analytic Story +* Ryuk Ransomware + +* Ransomware + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1756,27 +6345,34 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1490 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators may modify the boot configuration. #### References + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Batch File Write to System32 The search looks for a batch file (.bat) written to the Windows system directory tree. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1204.002 +- **Data Models**: Endpoint +- **ATT&CK**: [T1204.002](https://attack.mitre.org/techniques/T1204.002/) - **Last Updated**: 2018-12-14
@@ -1784,15 +6380,26 @@ The search looks for a batch file (.bat) written to the Windows system directory #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat | `batch_file_write_to_system32_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| rex field=file_name "(?\.[^\.]+)$" +| search file_path=*system32* AND file_extension=.bat +| `batch_file_write_to_system32_filter` ``` #### Associated Analytic Story +* SamSam Ransomware + + #### How To Implement You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1800,25 +6407,31 @@ You must be ingesting data that records the file-system activity from your hosts | T1204.002 | x | x | #### Kill Chain Phases + * Delivery + #### Known False Positives It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/batch_file_in_system32/windows-sysmon.log + _version_: 1
--- + ### Certutil exe certificate extraction This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Endpoint - **ATT&CK**: - **Last Updated**: 2021-01-26 @@ -1827,41 +6440,58 @@ This search looks for arguments to certutil.exe indicating the manipulation or e #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `certutil_exe_certificate_extraction_filter` ``` #### Associated Analytic Story +* Windows Persistence Techniques + +* Cloud Federated Credential Abuse + + #### How To Implement #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Installation + #### Known False Positives Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log + _version_: 1 --- + ### Child Processes of Spoolsv exe This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1068 +- **Data Models**: Endpoint +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) - **Last Updated**: 2020-03-16
@@ -1869,15 +6499,24 @@ This search looks for child processes of spoolsv.exe. This activity is associate #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `child_processes_of_spoolsv_exe_filter` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `child_processes_of_spoolsv_exe_filter` ``` #### Associated Analytic Story +* Windows Privilege Escalation + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1885,25 +6524,30 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1068 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. #### References + #### Test Dataset + _version_: 3
--- + ### Clients Connecting to Multiple DNS Servers This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048.003 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) - **Last Updated**: 2020-07-21
@@ -1911,10 +6555,23 @@ This search allows you to identify the endpoints that have connected to more tha #### Search ``` -| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 | `clients_connecting_to_multiple_dns_servers_filter` + +| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src +| `drop_dm_object_name("Network_Resolution")` +|where dest_count > 5 +| `clients_connecting_to_multiple_dns_servers_filter` ``` #### Associated Analytic Story +* DNS Hijacking + +* Command and Control + +* Suspicious DNS Traffic + +* Host Redirection + + #### How To Implement This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ @@ -1922,6 +6579,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1929,25 +6587,30 @@ Detailed documentation on how to create a new field within Incident Review may b | T1048.003 | x | x | #### Kill Chain Phases + * Command and Control + #### Known False Positives It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. #### References + #### Test Dataset + _version_: 3
--- + ### Cloud API Calls From Previously Unseen User Roles This search looks for new commands from each user role. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 +- **Data Models**: Change +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-09-04
@@ -1955,15 +6618,30 @@ This search looks for new commands from each user role. #### Search ``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUserApiCall=min(firstTimeSeen) | where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") | table firstTime, user, object, command |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `cloud_api_calls_from_previously_unseen_user_roles_filter` + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUserApiCall=min(firstTimeSeen) +| where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") +| table firstTime, user, object, command +|`security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `cloud_api_calls_from_previously_unseen_user_roles_filter` ``` #### Associated Analytic Story +* Suspicious Cloud User Activities + + #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter` #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -1972,24 +6650,29 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. #### Kill Chain Phases + #### Known False Positives . #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Cloud Compute Instance Created By Previously Unseen User This search looks for cloud compute instances created by users who have not created them before. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 +- **Data Models**: Change +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-08-21
@@ -1997,15 +6680,29 @@ This search looks for cloud compute instances created by users who have not crea #### Search ``` -| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime, user, dest, count vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_by_previously_unseen_user_filter` + +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUser=min(firstTimeSeen) +| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count vendor_region +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_by_previously_unseen_user_filter` ``` #### Associated Analytic Story +* Cloud Cryptomining + + #### How To Implement You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2014,24 +6711,29 @@ You must be ingesting the appropriate cloud-infrastructure logs Run the "Previou #### Kill Chain Phases + #### Known False Positives It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Cloud Compute Instance Created In Previously Unused Region This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 +- **Data Models**: Change +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2020-09-02
@@ -2039,15 +6741,29 @@ This search looks at cloud-infrastructure events where an instance is created in #### Search ``` -| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") | table firstTime, user, dest, count , vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_in_previously_unused_region_filter` + +| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenRegion=min(firstTimeSeen) +| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count , vendor_region +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_in_previously_unused_region_filter` ``` #### Associated Analytic Story +* Cloud Cryptomining + + #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2055,25 +6771,31 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y | T1535 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Cloud Compute Instance Created With Previously Unseen Image This search looks for cloud compute instances being created with previously unseen image IDs. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Change - **ATT&CK**: - **Last Updated**: 2018-10-12 @@ -2082,15 +6804,31 @@ This search looks for cloud compute instances being created with previously unse #### Search ``` -| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenImage=min(firstTimeSeen) | where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") | table firstTime, user, image_id, count, dest | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter` + +| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| `drop_dm_object_name("Instance_Changes")` +| where image_id != "unknown" +| lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenImage=min(firstTimeSeen) +| where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") +| table firstTime, user, image_id, count, dest +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_with_previously_unseen_image_filter` ``` #### Associated Analytic Story +* Cloud Cryptomining + + #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2098,23 +6836,28 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y #### Kill Chain Phases + #### Known False Positives After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1 --- + ### Cloud Compute Instance Created With Previously Unseen Instance Type Find EC2 instances being created with previously unseen instance types. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Change - **ATT&CK**: - **Last Updated**: 2020-09-12 @@ -2123,15 +6866,31 @@ Find EC2 instances being created with previously unseen instance types. #### Search ``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where instance_type != "unknown" | lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenInstanceType=min(firstTimeSeen) | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") | table firstTime, user, dest, count, instance_type | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| `drop_dm_object_name("Instance_Changes")` +| where instance_type != "unknown" +| lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenInstanceType=min(firstTimeSeen) +| where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count, instance_type +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` ``` #### Associated Analytic Story +* Cloud Cryptomining + + #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2139,24 +6898,29 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y #### Kill Chain Phases + #### Known False Positives It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1 --- + ### Cloud Instance Modified By Previously Unseen User This search looks for cloud instances being modified by users who have not previously modified them. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.004 +- **Data Models**: Change +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-07-29
@@ -2164,15 +6928,29 @@ This search looks for cloud instances being modified by users who have not previ #### Search ``` -| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime user command object_id count | `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter` + +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUser=min(firstTimeSeen) +| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") +| table firstTime user command object_id count +| `security_content_ctime(firstTime)` +| `cloud_instance_modified_by_previously_unseen_user_filter` ``` #### Associated Analytic Story +* Suspicious Cloud Instance Activities + + #### How To Implement This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2181,18 +6959,23 @@ This search has a dependency on other searches to create and update a baseline o #### Kill Chain Phases + #### Known False Positives It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Cloud Network Access Control List Deleted Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate @@ -2206,40 +6989,54 @@ Enforcing network-access controls is one of the defensive mechanisms used by clo #### Search ``` -`cloudtrail` eventName=DeleteNetworkAcl|rename userIdentity.arn as arn | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `cloud_network_access_control_list_deleted_filter` +`cloudtrail` eventName=DeleteNetworkAcl +|rename userIdentity.arn as arn +| stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `cloud_network_access_control_list_deleted_filter` ``` #### Associated Analytic Story +* Cloud Network ACL Activity + + #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the `cloud_network_access_control_list_deleted_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It's possible that a user has legitimately deleted a network ACL. #### References + #### Test Dataset + _version_: 1 --- + ### Cloud Provisioning Activity From Previously Unseen City This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 +- **Data Models**: Change +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-10-09
@@ -2247,15 +7044,31 @@ This search looks for cloud provisioning activities from previously unseen citie #### Search ``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) | lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, City, user, object, command | `cloud_provisioning_activity_from_previously_unseen_city_filter` | `security_content_ctime(firstTime)` + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(City) +| lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenCity=min(firstTimeSeen) +| where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, City, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_city_filter` +| `security_content_ctime(firstTime)` ``` #### Associated Analytic Story +* Suspicious Cloud Provisioning Activities + + #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2264,25 +7077,30 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. #### Kill Chain Phases + #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Cloud Provisioning Activity From Previously Unseen Country This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 +- **Data Models**: Change +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-10-09
@@ -2290,15 +7108,31 @@ This search looks for cloud provisioning activities from previously unseen count #### Search ``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | table firstTime, src, Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter` | `security_content_ctime(firstTime)` + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(Country) +| lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenCountry=min(firstTimeSeen) +| where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") +| table firstTime, src, Country, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_country_filter` +| `security_content_ctime(firstTime)` ``` #### Associated Analytic Story +* Suspicious Cloud Provisioning Activities + + #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2307,25 +7141,30 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. #### Kill Chain Phases + #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Cloud Provisioning Activity From Previously Unseen IP Address This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 +- **Data Models**: Change +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-08-16
@@ -2333,15 +7172,29 @@ This search looks for cloud provisioning activities from previously unseen IP ad #### Search ``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, user, object_id, command | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` | `security_content_ctime(firstTime)` + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenSrc=min(firstTimeSeen) +| where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, user, object_id, command +| `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` +| `security_content_ctime(firstTime)` ``` #### Associated Analytic Story +* Suspicious Cloud Provisioning Activities + + #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2350,25 +7203,30 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. #### Kill Chain Phases + #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Cloud Provisioning Activity From Previously Unseen Region This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078 +- **Data Models**: Change +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-08-16
@@ -2376,15 +7234,31 @@ This search looks for cloud provisioning activities from previously unseen regio #### Search ``` -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region) | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, Region, user, object, command | `cloud_provisioning_activity_from_previously_unseen_region_filter` | `security_content_ctime(firstTime)` + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(Region) +| lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenRegion=min(firstTimeSeen) +| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, Region, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_region_filter` +| `security_content_ctime(firstTime)` ``` #### Associated Analytic Story +* Suspicious Cloud Provisioning Activities + + #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2393,25 +7267,30 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. #### Kill Chain Phases + #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Common Ransomware Extensions The search looks for file modifications with extensions commonly used by Ransomware - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1485 +- **Data Models**: Endpoint +- **ATT&CK**: [T1485](https://attack.mitre.org/techniques/T1485/) - **Last Updated**: 2020-11-09
@@ -2419,10 +7298,24 @@ The search looks for file modifications with extensions commonly used by Ransomw #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` | `common_ransomware_extensions_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| rex field=file_name "(?\.[^\.]+)$" +| `ransomware_extensions` +| `common_ransomware_extensions_filter` ``` #### Associated Analytic Story +* SamSam Ransomware + +* Ryuk Ransonware + +* Ransomware + + #### How To Implement You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\ This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ @@ -2432,6 +7325,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2439,26 +7333,32 @@ Detailed documentation on how to create a new field within Incident Review may b | T1485 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_extensions/windows-sysmon.log + _version_: 4
--- + ### Common Ransomware Notes The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1485 +- **Data Models**: Endpoint +- **ATT&CK**: [T1485](https://attack.mitre.org/techniques/T1485/) - **Last Updated**: 2020-11-09
@@ -2466,15 +7366,29 @@ The search looks for files created with names matching those typically used in r #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_notes` | `common_ransomware_notes_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `ransomware_notes` +| `common_ransomware_notes_filter` ``` #### Associated Analytic Story +* SamSam Ransomware + +* Ransomware + +* Ryuk Ransomware + + #### How To Implement You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2482,26 +7396,32 @@ You must be ingesting data that records file-system activity from your hosts to | T1485 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It's possible that a legitimate file could be created with the same name used by ransomware note files. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/windows-sysmon.log + _version_: 4
--- + ### Create Remote Thread into LSASS Detect remote thread creation into LSASS consistent with credential dumping. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1003.001 +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2019-12-06
@@ -2509,15 +7429,24 @@ Detect remote thread creation into LSASS consistent with credential dumping. #### Search ``` -`sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `create_remote_thread_into_lsass_filter` +`sysmon` EventID=8 TargetImage=*lsass.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `create_remote_thread_into_lsass_filter` ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2525,27 +7454,34 @@ This search needs Sysmon Logs with a Sysmon configuration, which includes EventC | T1003.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. #### References + * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Create local admin accounts using net exe This search looks for the creation of local administrator accounts using net.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1136.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1136.001](https://attack.mitre.org/techniques/T1136.001/) - **Last Updated**: 2020-07-21
@@ -2553,15 +7489,24 @@ This search looks for the creation of local administrator accounts using net.exe #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`create_local_admin_accounts_using_net_exe_filter` + +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|`create_local_admin_accounts_using_net_exe_filter` ``` #### Associated Analytic Story +* DHS Report TA18-074A + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2569,28 +7514,36 @@ You must be ingesting data that records process activity from your hosts to popu | T1136.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators often leverage net.exe to create admin accounts. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### Create or delete windows shares using net exe This search looks for the creation or deletion of hidden shares using net.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1070.005 +- **Data Models**: Endpoint +- **ATT&CK**: [T1070.005](https://attack.mitre.org/techniques/T1070.005/) - **Last Updated**: 2020-07-21
@@ -2598,15 +7551,25 @@ This search looks for the creation or deletion of hidden shares using net.exe. #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*share* | `create_or_delete_windows_shares_using_net_exe_filter` + +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search process=*share* +| `create_or_delete_windows_shares_using_net_exe_filter` ``` #### Associated Analytic Story +* Hidden Cobra Malware + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2614,27 +7577,34 @@ You must be ingesting data that records process activity from your hosts to popu | T1070.005 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. #### References + * https://attack.mitre.org/techniques/T1070/005 + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log + _version_: 5
--- + ### Creation of Shadow Copy Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2019-12-10
@@ -2642,15 +7612,24 @@ Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `creation_of_shadow_copy_filter` ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2658,27 +7637,34 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1003.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Legitimate administrator usage of Vssadmin or Wmic will create false positives. #### References + * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Creation of Shadow Copy with wmic and powershell This search detects the use of wmic and Powershell to create a shadow copy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2019-12-10
@@ -2686,15 +7672,24 @@ This search detects the use of wmic and Powershell to create a shadow copy. #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_with_wmic_and_powershell_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `creation_of_shadow_copy_with_wmic_and_powershell_filter` ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2702,27 +7697,34 @@ To successfully implement this search you need to be ingesting information on pr | T1003.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Legtimate administrator usage of wmic to create a shadow copy. #### References + * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Creation of lsass Dump with Taskmgr Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1003.001 +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2020-02-03
@@ -2730,15 +7732,24 @@ Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess #### Search ``` -`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp | stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `creation_of_lsass_dump_with_taskmgr_filter` +`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `creation_of_lsass_dump_with_taskmgr_filter` ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2746,29 +7757,38 @@ This search requires Sysmon Logs and a Sysmon configuration, which includes Even | T1003.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. #### References + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager + * https://attack.mitre.org/techniques/T1003/001/ + * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Credential Dumping via Copy Command from Shadow Copy This search detects credential dumping using copy command from a shadow copy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2019-12-10
@@ -2776,15 +7796,24 @@ This search detects credential dumping using copy command from a shadow copy. #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_copy_command_from_shadow_copy_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `credential_dumping_via_copy_command_from_shadow_copy_filter` ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2792,27 +7821,34 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1003.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives unknown #### References + * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Credential Dumping via Symlink to Shadow Copy This search detects the creation of a symlink to a shadow copy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2019-12-10
@@ -2820,15 +7856,24 @@ This search detects the creation of a symlink to a shadow copy. #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_symlink_to_shadow_copy_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `credential_dumping_via_symlink_to_shadow_copy_filter` ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -2836,27 +7881,34 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1003.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives unknown #### References + * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Credential Extraction indicative of FGDump and CacheDump with s option Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003 +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-18
@@ -2864,24 +7916,38 @@ Credential extraction is often an illegal recovery of credential material from s #### Search ``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND process_name != null AND parent_process_name != null AND match_regex(parent_process_name, /(?i)System32\\services.exe/)=true AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-s/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| where cmd_line != null AND process_name != null AND parent_process_name != null AND match_regex(parent_process_name, /(?i)System32\\services.exe/)=true AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-s/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * process_name + * parent_process_name + * _time + * process_path + * dest_user_id + * process + #### ATT&CK | ID | technique | Tactic | @@ -2889,25 +7955,30 @@ You must be ingesting Windows Security logs from devices of interest, including | T1003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + #### Test Dataset + _version_: 1
--- + ### Credential Extraction indicative of FGDump and CacheDump with v option Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003 +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-18
@@ -2915,23 +7986,36 @@ Credential extraction is often an illegal recovery of credential material from s #### Search ``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null) | where cmd_line != null AND process_name != null AND process_path != null AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-v/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null) +| where cmd_line != null AND process_name != null AND process_path != null AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-v/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * process_name + * _time + * process_path + * dest_user_id + * process + #### ATT&CK | ID | technique | Tactic | @@ -2939,25 +8023,30 @@ You must be ingesting Windows Security logs from devices of interest, including | T1003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + #### Test Dataset + _version_: 1
--- + ### Credential Extraction indicative of Lazagne command line options Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003, T1555 +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/), [T1555](https://attack.mitre.org/techniques/T1555/) - **Last Updated**: 2020-10-18
@@ -2965,21 +8054,32 @@ Credential extraction is often an illegal recovery of credential material from s #### Search ``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)all\s+\-oA\s+\-output/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND match_regex(cmd_line, /(?i)all\s+\-oA\s+\-output/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -2988,25 +8088,30 @@ You must be ingesting Windows Security logs from devices of interest, including | T1555 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + #### Test Dataset + _version_: 1
--- + ### Credential Extraction indicative of use of DSInternals credential conversion modules Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003 +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-21
@@ -3014,24 +8119,38 @@ Credential extraction is often an illegal recovery of credential material from s #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)ConvertFrom-ADManagedPasswordBlob/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-UnicodePassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-KerberosKey/)=true OR match_regex(cmd_line, /(?i)ConvertTo-LMHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-NTHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-OrgIdHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-UnicodePassword/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)ConvertFrom-ADManagedPasswordBlob/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-UnicodePassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-KerberosKey/)=true OR match_regex(cmd_line, /(?i)ConvertTo-LMHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-NTHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-OrgIdHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-UnicodePassword/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * process_name + * parent_process_name + * _time + * process_path + * dest_user_id + * process + #### ATT&CK | ID | technique | Tactic | @@ -3039,26 +8158,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/MichaelGrafnetter/DSInternals + #### Test Dataset + _version_: 1
--- + ### Credential Extraction indicative of use of DSInternals modules Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003 +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-21
@@ -3066,24 +8191,38 @@ Credential extraction is often an illegal recovery of credential material from s #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ADDBBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Get-ADDBKdsRootKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBSchemaAttribute/)=true OR match_regex(cmd_line, /(?i)Get-ADKeyCredential/)=true OR match_regex(cmd_line, /(?i)Get-ADReplAccount/)=true OR match_regex(cmd_line, /(?i)Get-ADReplBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADSIAccount/)=true OR match_regex(cmd_line, /(?i)Get-AzureADUserEx/)=true OR match_regex(cmd_line, /(?i)Get-BootKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaPolicyInformation/)=true OR match_regex(cmd_line, /(?i)Get-SamPasswordPolicy/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ADDBBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Get-ADDBKdsRootKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBSchemaAttribute/)=true OR match_regex(cmd_line, /(?i)Get-ADKeyCredential/)=true OR match_regex(cmd_line, /(?i)Get-ADReplAccount/)=true OR match_regex(cmd_line, /(?i)Get-ADReplBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADSIAccount/)=true OR match_regex(cmd_line, /(?i)Get-AzureADUserEx/)=true OR match_regex(cmd_line, /(?i)Get-BootKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaPolicyInformation/)=true OR match_regex(cmd_line, /(?i)Get-SamPasswordPolicy/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * process_name + * parent_process_name + * _time + * process_path + * dest_user_id + * process + #### ATT&CK | ID | technique | Tactic | @@ -3091,26 +8230,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/MichaelGrafnetter/DSInternals + #### Test Dataset + _version_: 1
--- + ### Credential Extraction indicative of use of Mimikatz modules Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003 +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-21
@@ -3118,21 +8263,32 @@ Credential extraction is often an illegal recovery of credential material from s #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)CRYPTO::Certificates/)=true OR match_regex(cmd_line, /(?i)CRYPTO::keys/)=true OR match_regex(cmd_line, /(?i)kerberos::list/)=true OR match_regex(cmd_line, /(?i)kerberos::tgt/)=true OR match_regex(cmd_line, /(?i)lsadump::sam/)=true OR match_regex(cmd_line, /(?i)lsadump::secrets/)=true OR match_regex(cmd_line, /(?i)lsadump::cache/)=true OR match_regex(cmd_line, /(?i)lsadump::lsa/)=true OR match_regex(cmd_line, /(?i)lsadump::trust/)=true OR match_regex(cmd_line, /(?i)lsadump::backupkeys/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)CRYPTO::Certificates/)=true OR match_regex(cmd_line, /(?i)CRYPTO::keys/)=true OR match_regex(cmd_line, /(?i)kerberos::list/)=true OR match_regex(cmd_line, /(?i)kerberos::tgt/)=true OR match_regex(cmd_line, /(?i)lsadump::sam/)=true OR match_regex(cmd_line, /(?i)lsadump::secrets/)=true OR match_regex(cmd_line, /(?i)lsadump::cache/)=true OR match_regex(cmd_line, /(?i)lsadump::lsa/)=true OR match_regex(cmd_line, /(?i)lsadump::trust/)=true OR match_regex(cmd_line, /(?i)lsadump::backupkeys/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -3140,26 +8296,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + #### Test Dataset + _version_: 1
--- + ### Credential Extraction indicative of use of PowerSploit modules Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003 +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-21
@@ -3167,21 +8329,32 @@ Credential extraction is often an illegal recovery of credential material from s #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ApplicationHost/)=true OR match_regex(cmd_line, /(?i)Get-CachedGPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-GPPAutologon/)=true OR match_regex(cmd_line, /(?i)Get-GPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-RegistryAutoLogon/)=true OR match_regex(cmd_line, /(?i)Get-SiteListPassword/)=true OR match_regex(cmd_line, /(?i)Get-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Request-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Get-VaultCredential/)=true OR match_regex(cmd_line, /(?i)Invoke-Kerberoast/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ApplicationHost/)=true OR match_regex(cmd_line, /(?i)Get-CachedGPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-GPPAutologon/)=true OR match_regex(cmd_line, /(?i)Get-GPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-RegistryAutoLogon/)=true OR match_regex(cmd_line, /(?i)Get-SiteListPassword/)=true OR match_regex(cmd_line, /(?i)Get-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Request-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Get-VaultCredential/)=true OR match_regex(cmd_line, /(?i)Invoke-Kerberoast/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -3189,26 +8362,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Credential Extraction native Microsoft debuggers peek into the kernel Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003 +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-18
@@ -3216,23 +8395,36 @@ Credential extraction is often an illegal recovery of credential material from s #### Search ``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND parent_process_name != null AND process_name != null AND ( match_regex(parent_process_name, /(?i)ntkd\.exe/)=true OR match_regex(parent_process_name, /(?i)livekd\.exe/)=true ) AND match_regex(process_name, /(?i)conhost\.exe/)=true AND match_regex(cmd_line, /(?i)0xffffffff/)=true AND match_regex(cmd_line, /(?i)\-ForceV1/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| where cmd_line != null AND parent_process_name != null AND process_name != null AND ( match_regex(parent_process_name, /(?i)ntkd\.exe/)=true OR match_regex(parent_process_name, /(?i)livekd\.exe/)=true ) AND match_regex(process_name, /(?i)conhost\.exe/)=true AND match_regex(cmd_line, /(?i)0xffffffff/)=true AND match_regex(cmd_line, /(?i)\-ForceV1/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * process_name + * parent_process_name + * _time + * dest_device_id + * dest_user_id + * process + #### ATT&CK | ID | technique | Tactic | @@ -3240,26 +8432,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. #### References + * https://medium.com/@clermont1050/covid-19-cyber-infection-c615ead7c29 + #### Test Dataset + _version_: 1
--- + ### Credential Extraction native Microsoft debuggers via z command line option Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003 +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-18
@@ -3267,22 +8465,34 @@ Credential extraction is often an illegal recovery of credential material from s #### Search ``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) | where cmd_line != null AND process_name != null AND ( match_regex(process_name, /^(?i)ntkd\.exe/)=true OR match_regex(process_name, /^(?i)kd\.exe/)=true ) AND match_regex(cmd_line, /(?i)\-z\s+/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) +| where cmd_line != null AND process_name != null AND ( match_regex(process_name, /^(?i)ntkd\.exe/)=true OR match_regex(process_name, /^(?i)kd\.exe/)=true ) AND match_regex(cmd_line, /(?i)\-z\s+/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * process_name + * _time + * dest_device_id + * dest_user_id + * process + #### ATT&CK | ID | technique | Tactic | @@ -3290,25 +8500,30 @@ You must be ingesting Windows Security logs from devices of interest, including | T1003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. #### References + #### Test Dataset + _version_: 1
--- + ### Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003 +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-18
@@ -3316,21 +8531,33 @@ Credential extraction is often an illegal recovery of credential material from s #### Search ``` - | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)Get-ADDBAccount/)=true AND match_regex(cmd_line, /(?i)\-dbpath[\s;:\.\|]+/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND match_regex(cmd_line, /(?i)Get-ADDBAccount/)=true AND match_regex(cmd_line, /(?i)\-dbpath[\s;:\.\ +|]+/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -3338,25 +8565,30 @@ You must be ingesting Windows Security logs from devices of interest, including | T1003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + #### Test Dataset + _version_: 1
--- + ### DNS Query Length Outliers - MLTK This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1071.004 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071.004/) - **Last Updated**: 2020-01-22
@@ -3364,10 +8596,29 @@ This search allows you to identify DNS requests that are unusually large for the #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` + +| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type +| search DNS.record_type=* +| `drop_dm_object_name(DNS)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval query_length = len(query) +| apply dns_query_pdfmodel threshold=0.01 +| rename "IsOutlier(query_length)" as isOutlier +| search isOutlier > 0 +| sort -query_length +| table start_time end_time query record_type count src dest query_length +| `dns_query_length_outliers___mltk_filter` ``` #### Associated Analytic Story +* Hidden Cobra Malware + +* Suspicious DNS Traffic + +* Command and Control + + #### How To Implement To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ @@ -3379,6 +8630,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3386,25 +8638,30 @@ Detailed documentation on how to create a new field within Incident Review may b | T1071.004 | x | x | #### Kill Chain Phases + * Command and Control + #### Known False Positives If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. #### References + #### Test Dataset + _version_: 2
--- + ### DNS Query Length With High Standard Deviation This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048.003 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) - **Last Updated**: 2021-01-18
@@ -3412,15 +8669,31 @@ This search allows you to identify DNS requests and compute the standard deviati #### Search ``` -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query | `drop_dm_object_name("DNS")` | eval query_length = len(query) | table query query_length record_type count | eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2) | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter` + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query +| `drop_dm_object_name("DNS")` +| eval query_length = len(query) +| table query query_length record_type count +| eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50 +| where query_length>(avg+stdev*2) +| eval z_score=(query_length-avg)/stdev +| `dns_query_length_with_high_standard_deviation_filter` ``` #### Associated Analytic Story +* Hidden Cobra Malware + +* Suspicious DNS Traffic + +* Command and Control + + #### How To Implement To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3428,26 +8701,32 @@ To successfully implement this search, you will need to ensure that DNS data is | T1048.003 | x | x | #### Kill Chain Phases + * Command and Control + #### Known False Positives It's possible there can be long domain names that are legitimate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log + _version_: 3
--- + ### DNS Query Requests Resolved by Unauthorized DNS Servers This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1071.004 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071.004/) - **Last Updated**: 2020-07-21
@@ -3455,15 +8734,28 @@ This search will detect DNS requests resolved by unauthorized DNS servers. Legit #### Search ``` -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` | `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest +| `drop_dm_object_name("DNS")` +| `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` ``` #### Associated Analytic Story +* DNS Hijacking + +* Command and Control + +* Suspicious DNS Traffic + +* Host Redirection + + #### How To Implement To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3471,25 +8763,30 @@ To successfully implement this search you will need to ensure that DNS data is p | T1071.004 | x | x | #### Kill Chain Phases + * Command and Control + #### Known False Positives Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. #### References + #### Test Dataset + _version_: 3
--- + ### DNS record changed The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1071.004 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071.004/) - **Last Updated**: 2020-07-21
@@ -3497,10 +8794,31 @@ The search takes the DNS records and their answers results of the discovered_dns #### Search ``` -| inputlookup discovered_dns_records | rename answer as discovered_answer | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter` + +| inputlookup discovered_dns_records +| rename answer as discovered_answer +| join domain[ +|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query +| rename DNS.query as query +| where query!="unknown" +| rex field=query "(?\w+\.\w+?)(?:$ +|/)"] +| makemv delim=" " answer +| makemv delim=" " type +| sort -count +| table count,src,domain,type,query,current_answer,discovered_answer +| makemv current_answer +| mvexpand current_answer +| makemv discovered_answer +| eval n=mvfind(discovered_answer, current_answer) +| where isnull(n) +| `dns_record_changed_filter` ``` #### Associated Analytic Story +* DNS Hijacking + + #### How To Implement To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ **Splunk>Phantom Playbook Integration**\ @@ -3510,6 +8828,7 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "DNS #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3517,25 +8836,30 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "DNS | T1071.004 | x | x | #### Kill Chain Phases + * Command and Control + #### Known False Positives Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. #### References + #### Test Dataset + _version_: 3
--- + ### Deleting Shadow Copies The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1490 +- **Data Models**: Endpoint +- **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/) - **Last Updated**: 2020-11-09
@@ -3543,15 +8867,28 @@ The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `deleting_shadow_copies_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `deleting_shadow_copies_filter` ``` #### Associated Analytic Story +* Windows Log Manipulation + +* SamSam Ransomware + +* Ransomware + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3559,20 +8896,26 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1490 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### Detect API activity from users without MFA This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. @@ -3586,10 +8929,21 @@ This search looks for CloudTrail events where a user logged into the AWS account #### Search ``` -`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity as user]| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_api_activity_from_users_without_mfa_filter` +`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false +| search NOT [ +| inputlookup aws_service_accounts +| fields identity +| rename identity as user] +| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_api_activity_from_users_without_mfa_filter` ``` #### Associated Analytic Story +* AWS User Monitoring + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ @@ -3601,6 +8955,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3608,23 +8963,27 @@ Detailed documentation on how to create a new field within Incident Review may b #### Kill Chain Phases + #### Known False Positives Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. #### References + #### Test Dataset + _version_: 1 --- + ### Detect ARP Poisoning By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1200, T1498, T1557.002 +- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557.002](https://attack.mitre.org/techniques/T1557.002/) - **Last Updated**: 2020-08-11
@@ -3632,15 +8991,24 @@ By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organiza #### Search ``` -`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_arp_poisoning_filter` +`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" +| eval src_interface=src_int_prefix_long+src_int_suffix +| stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_arp_poisoning_filter` ``` #### Associated Analytic Story +* Router and Infrastructure Security + + #### How To Implement This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3650,27 +9018,34 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | T1557.002 | x | x | #### Kill Chain Phases + * Reconnaissance + * Delivery + * Actions on Objectives + #### Known False Positives This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely). #### References + #### Test Dataset + _version_: 1
--- + ### Detect AWS API Activities From Unapproved Accounts This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.004 +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-07-21
@@ -3678,10 +9053,25 @@ This search looks for successful CloudTrail activity by user accounts that are n #### Search ``` -`cloudtrail` errorCode=success | rename userName as identity | search NOT [| inputlookup identity_lookup_expanded | fields identity] | search NOT [| inputlookup aws_service_accounts | fields identity] | rename identity as user | stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_aws_api_activities_from_unapproved_accounts_filter` +`cloudtrail` errorCode=success +| rename userName as identity +| search NOT [ +| inputlookup identity_lookup_expanded +| fields identity] +| search NOT [ +| inputlookup aws_service_accounts +| fields identity] +| rename identity as user +| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_aws_api_activities_from_unapproved_accounts_filter` ``` #### Associated Analytic Story +* AWS User Monitoring + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ @@ -3693,6 +9083,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3700,24 +9091,29 @@ Detailed documentation on how to create a new field within Incident Review may b | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. #### References + #### Test Dataset + _version_: 2
--- + ### Detect AWS Console Login by New User This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Authentication - **ATT&CK**: - **Last Updated**: 2020-05-28 @@ -3726,41 +9122,60 @@ This search looks for CloudTrail events wherein a console login event by a user #### Search ``` -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)` | inputlookup append=t previously_seen_users_console_logins | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") |where userStatus="First Time Logging into AWS Console" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `detect_aws_console_login_by_new_user_filter` + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user +| `drop_dm_object_name(Authentication)` +| inputlookup append=t previously_seen_users_console_logins +| stats min(firstTime) as firstTime max(lastTime) as lastTime by user +| eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") +|where userStatus="First Time Logging into AWS Console" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_aws_console_login_by_new_user_filter` ``` #### Associated Analytic Story +* Suspicious Cloud Authentication Activities + + #### How To Implement You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1 --- + ### Detect AWS Console Login by User from New City This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 +- **Data Models**: Authentication +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2020-10-07
@@ -3768,15 +9183,36 @@ This search looks for CloudTrail events wherein a console login event by a user #### Search ``` -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user City | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user City | fields earliestseen user City] | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCity = "New City" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user City userStatus userCity | `detect_aws_console_login_by_user_from_new_city_filter` + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user City +| join user type=outer [ +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user City +| fields earliestseen user City] +| eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") +| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userCity = "New City" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user City userStatus userCity +| `detect_aws_console_login_by_user_from_new_city_filter` ``` #### Associated Analytic Story +* Suspicious AWS Login Activities + +* Suspicious Cloud Authentication Activities + + #### How To Implement You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3784,26 +9220,32 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later | T1535 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Detect AWS Console Login by User from New Country This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 +- **Data Models**: Authentication +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2020-10-07
@@ -3811,15 +9253,36 @@ This search looks for CloudTrail events wherein a console login event by a user #### Search ``` -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Country | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Country | fields earliestseen user Country] | eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") | eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCountry = "New Country" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Country userStatus userCountry | `detect_aws_console_login_by_user_from_new_country_filter` + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user Country +| join user type=outer [ +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user Country +| fields earliestseen user Country] +| eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") +| eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userCountry = "New Country" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user Country userStatus userCountry +| `detect_aws_console_login_by_user_from_new_country_filter` ``` #### Associated Analytic Story +* Suspicious AWS Login Activities + +* Suspicious Cloud Authentication Activities + + #### How To Implement You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3827,26 +9290,32 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later | T1535 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Detect AWS Console Login by User from New Region This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1535 +- **Data Models**: Authentication +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2020-10-07
@@ -3854,15 +9323,36 @@ This search looks for CloudTrail events wherein a console login event by a user #### Search ``` -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Region | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Region | fields earliestseen user Region] | eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userRegion = "New Region" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Region userStatus userRegion | `detect_aws_console_login_by_user_from_new_region_filter` + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user Region +| join user type=outer [ +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user Region +| fields earliestseen user Region] +| eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") +| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userRegion = "New Region" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user Region userStatus userRegion +| `detect_aws_console_login_by_user_from_new_region_filter` ``` #### Associated Analytic Story +* Suspicious AWS Login Activities + +* Suspicious Cloud Authentication Activities + + #### How To Implement You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3870,26 +9360,32 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later | T1535 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + _version_: 1
--- + ### Detect Activity Related to Pass the Hash Attacks This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1550.002 +- **ATT&CK**: [T1550.002](https://attack.mitre.org/techniques/T1550.002/) - **Last Updated**: 2020-10-15
@@ -3897,15 +9393,24 @@ This search looks for specific authentication events from the Windows Security E #### Search ``` -`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_activity_related_to_pass_the_hash_attacks_filter` +`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_activity_related_to_pass_the_hash_attacks_filter` ``` #### Associated Analytic Story +* Lateral Movement + + #### How To Implement To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3913,26 +9418,32 @@ To successfully implement this search, you must ingest your Windows Security Eve | T1550.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.002/atomic_red_team/windows-security.log + _version_: 5
--- + ### Detect Baron Samedit CVE-2021-3156 This search detects the heap-based buffer overflow of sudoedit - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1068 +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) - **Last Updated**: 2021-01-27
@@ -3940,15 +9451,21 @@ This search detects the heap-based buffer overflow of sudoedit #### Search ``` -`linux_hosts` | search "sudoedit -s \\" | `detect_baron_samedit_cve_2021_3156_filter` +`linux_hosts` +| search "sudoedit -s \\" +| `detect_baron_samedit_cve_2021_3156_filter` ``` #### Associated Analytic Story +* Baron Samedit CVE-2021-3156 + + #### How To Implement Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3956,26 +9473,32 @@ Splunk Universal Forwarder running on Linux systems, capturing logs from the /va | T1068 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives unknown #### References + * https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + #### Test Dataset + _version_: 1
--- + ### Detect Baron Samedit CVE-2021-3156 Segfault This search detects the heap-based buffer overflow of sudoedit - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1068 +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) - **Last Updated**: 2021-01-29
@@ -3983,15 +9506,23 @@ This search detects the heap-based buffer overflow of sudoedit #### Search ``` -`linux_hosts` | search sudoedit segfault | stats count min(_time) as firstTime max(_time) as lastTime by host | search count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter` +`linux_hosts` +| search sudoedit segfault +| stats count min(_time) as firstTime max(_time) as lastTime by host +| search count > 5 +| `detect_baron_samedit_cve_2021_3156_segfault_filter` ``` #### Associated Analytic Story +* Baron Samedit CVE-2021-3156 + + #### How To Implement Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged. This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -3999,26 +9530,32 @@ Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu | T1068 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives If sudoedit is throwing segfaults for other reasons this will pick those up too. #### References + * https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + #### Test Dataset + _version_: 1
--- + ### Detect Baron Samedit CVE-2021-3156 via OSQuery This search detects the heap-based buffer overflow of sudoedit - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1068 +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) - **Last Updated**: 2021-01-28
@@ -4026,15 +9563,21 @@ This search detects the heap-based buffer overflow of sudoedit #### Search ``` -`osquery_process` | search "columns.cmdline"="sudoedit -s \\*" | `detect_baron_samedit_cve_2021_3156_via_osquery_filter` +`osquery_process` +| search "columns.cmdline"="sudoedit -s \\*" +| `detect_baron_samedit_cve_2021_3156_via_osquery_filter` ``` #### Associated Analytic Story +* Baron Samedit CVE-2021-3156 + + #### How To Implement OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4042,26 +9585,32 @@ OSQuery installed and configured to pick up process events (info at https://osqu | T1068 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives unknown #### References + * https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + #### Test Dataset + _version_: 1
--- + ### Detect Computer Changed with Anonymous Account This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1210 +- **ATT&CK**: [T1210](https://attack.mitre.org/techniques/T1210/) - **Last Updated**: 2020-09-18
@@ -4069,15 +9618,21 @@ This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An ac #### Search ``` -`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 | stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter` +`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 +| stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user +| `detect_computer_changed_with_anonymous_account_filter` ``` #### Associated Analytic Story +* Detect Zerologon Attack + + #### How To Implement This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event Logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4085,26 +9640,32 @@ This search requires audit computer account management to be enabled on the syst | T1210 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None thus far found #### References + * https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/ + #### Test Dataset + _version_: 1
--- + ### Detect Credential Dumping through LSASS access This search looks for reading lsass memory consistent with credential dumping. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1003.001 +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2019-12-03
@@ -4112,15 +9673,26 @@ This search looks for reading lsass memory consistent with credential dumping. #### Search ``` -`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_credential_dumping_through_lsass_access_filter` +`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_credential_dumping_through_lsass_access_filter` ``` #### Associated Analytic Story +* Credential Dumping + +* Detect Zerologon Attack + + #### How To Implement This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4128,26 +9700,32 @@ This search needs Sysmon Logs and a sysmon configuration, which includes EventCo | T1003.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + _version_: 3
--- + ### Detect DNS requests to Phishing Sites leveraging EvilGinx2 This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1566.003 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1566.003](https://attack.mitre.org/techniques/T1566.003/) - **Last Updated**: 2020-07-21
@@ -4155,10 +9733,29 @@ This search looks for DNS requests for phishing domains that are leveraging Evil #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host +| `drop_dm_object_name(DNS)` +| rex field=query ".*?(?[^./:]+\.(\S{2,3} +|\S{2,3}.\S{2,3}))$" +| stats count values(query) as query by domain dest src answer +| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` +| search NOT [ inputlookup legit_domains.csv +| fields domain] +| join domain type=outer [ +| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site +| rename "Web.*" as * +| rex field=site ".*?(?[^./:]+\.(\S{2,3} +|\S{2,3}.\S{2,3}))$" +| table dest domain url] +| table count src dest query answer domain url +| `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter` ``` #### Associated Analytic Story +* Common Phishing Frameworks + + #### How To Implement You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ **Splunk>Phantom Playbook Integration**\ @@ -4168,6 +9765,7 @@ If Splunk>Phantom is also configured in your environment, a Playbook called `Let #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4175,26 +9773,32 @@ If Splunk>Phantom is also configured in your environment, a Playbook called `Let | T1566.003 | x | x | #### Kill Chain Phases + * Delivery + * Command and Control + #### Known False Positives If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. #### References + #### Test Dataset + _version_: 2
--- + ### Detect Dump LSASS Memory using comsvcs This search detects the memory of lsass.exe being dumped for offline credential theft attack. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1003.003 +- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2020-09-15
@@ -4202,20 +9806,34 @@ This search detects the memory of lsass.exe being dumped for offline credential #### Search ``` -| from read_ssa_enriched_events() | eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)) | where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true | eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)) +| where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including Windows command line logging. You can see how we test this with [Event Code 4688](https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4688a) on the [attack_range](https://github.com/splunk/attack_range/blob/develop/ansible/roles/windows_common/tasks/windows-enable-4688-cmd-line-audit.yml). #### Required fields + * process_name + * _tenant + * _time + * dest_device_id + * process + #### ATT&CK | ID | technique | Tactic | @@ -4223,26 +9841,32 @@ You must be ingesting endpoint data that tracks process activity, including Wind | T1003.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + #### Test Dataset + _version_: 1
--- + ### Detect Excessive Account Lockouts From Endpoint This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.002 +- **Data Models**: Change +- **ATT&CK**: [T1078.002](https://attack.mitre.org/techniques/T1078.002/) - **Last Updated**: 2020-11-09
@@ -4250,10 +9874,20 @@ This search identifies endpoints that have caused a relatively high number of ac #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result +|`drop_dm_object_name("All_Changes")` +|`drop_dm_object_name("Account_Management")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search count > 5 +| `detect_excessive_account_lockouts_from_endpoint_filter` ``` #### Associated Analytic Story +* Account Monitoring and Controls + + #### How To Implement You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \ **Splunk>Phantom Playbook Integration**\ @@ -4263,6 +9897,7 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "Exc #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4271,25 +9906,31 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "Exc #### Kill Chain Phases + #### Known False Positives It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log + _version_: 5
--- + ### Detect Excessive User Account Lockouts This search detects user accounts that have been locked out a relatively high number of times in a short period. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1078.003 +- **Data Models**: Change +- **ATT&CK**: [T1078.003](https://attack.mitre.org/techniques/T1078.003/) - **Last Updated**: 2020-07-21
@@ -4297,15 +9938,26 @@ This search detects user accounts that have been locked out a relatively high nu #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_user_account_lockouts_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result +|`drop_dm_object_name("All_Changes")` +|`drop_dm_object_name("Account_Management")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search count > 5 +| `detect_excessive_user_account_lockouts_filter` ``` #### Associated Analytic Story +* Account Monitoring and Controls + + #### How To Implement ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4314,25 +9966,31 @@ ou must ingest your Windows security event logs in the `Change` datamodel under #### Kill Chain Phases + #### Known False Positives It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log + _version_: 3
--- + ### Detect F5 TMUI RCE CVE-2020-5902 This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1190 +- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/) - **Last Updated**: 2020-08-02
@@ -4340,15 +9998,22 @@ This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traff #### Search ``` -`f5_bigip_rogue` | regex _raw="(hsqldb;|.*\\.\\.;.*)" | search `detect_f5_tmui_rce_cve_2020_5902_filter` +`f5_bigip_rogue` +| regex _raw="(hsqldb; +|.*\\.\\.;.*)" +| search `detect_f5_tmui_rce_cve_2020_5902_filter` ``` #### Associated Analytic Story +* F5 TMUI RCE CVE-2020-5902 + + #### How To Implement To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog. As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it. I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;). #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4356,28 +10021,36 @@ To consistently detect exploit attempts on F5 devices using the vulnerabilities | T1190 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives unknown #### References + * https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/ + * https://support.f5.com/csp/article/K52145254 + * https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/ + #### Test Dataset + _version_: 1
--- + ### Detect GCP Storage access from a new IP This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1530 +- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2020-08-10
@@ -4385,15 +10058,37 @@ This search looks at GCP Storage bucket-access logs and detects new or previousl #### Search ``` -`google_gcp_pubsub_message` | multikv | rename sc_status_ as status | rename cs_object_ as bucket_name | rename c_ip_ as remote_ip | rename cs_uri_ as request_uri | rename cs_method_ as operation | search status="\"200\"" | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri | table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri | inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri | outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) | where newIP=1 | eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") | table first_time last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter` +`google_gcp_pubsub_message` +| multikv +| rename sc_status_ as status +| rename cs_object_ as bucket_name +| rename c_ip_ as remote_ip +| rename cs_uri_ as request_uri +| rename cs_method_ as operation +| search status="\"200\"" +| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri +| table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri +| inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri +| outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv +| eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) +| where newIP=1 +| eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") +| eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") +| table first_time last_time bucket_name remote_ip operation request_uri +| `detect_gcp_storage_access_from_a_new_ip_filter` ``` #### Associated Analytic Story +* Suspicious GCP Storage Activities + + #### How To Implement This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour. It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4401,25 +10096,30 @@ This search relies on the Splunk Add-on for Google Cloud Platform, setting up a | T1530 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours. #### References + #### Test Dataset + _version_: 1
--- + ### Detect HTML Help Renamed The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1218.001 +- **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218.001/) - **Last Updated**: 2021-02-11
@@ -4427,15 +10127,24 @@ The following analytic identifies a renamed instance of hh.exe (HTML Help) execu #### Search ``` -`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_renamed_filter` +`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_renamed_filter` ``` #### Associated Analytic Story +* Suspicious Compiled HTML Activity + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed hh.exe may be used. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4443,29 +10152,38 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1218.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed. #### References + * https://attack.mitre.org/techniques/T1218/001/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + * https://lolbas-project.github.io/lolbas/Binaries/Hh/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect HTML Help Spawn Child Process The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218.001/) - **Last Updated**: 2021-02-11
@@ -4473,15 +10191,24 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_spawn_child_process_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_spawn_child_process_filter` ``` #### Associated Analytic Story +* Suspicious Compiled HTML Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4489,31 +10216,42 @@ To successfully implement this search you need to be ingesting information on pr | T1218.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed. #### References + * https://attack.mitre.org/techniques/T1218/001/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + * https://lolbas-project.github.io/lolbas/Binaries/Hh/ + * https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 + * https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect HTML Help URL in Command Line The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218.001/) - **Last Updated**: 2021-02-11
@@ -4521,15 +10259,24 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_url_in_command_line_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_url_in_command_line_filter` ``` #### Associated Analytic Story +* Suspicious Compiled HTML Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4537,32 +10284,44 @@ To successfully implement this search you need to be ingesting information on pr | T1218.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed. #### References + * https://attack.mitre.org/techniques/T1218/001/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + * https://lolbas-project.github.io/lolbas/Binaries/Hh/ + * https://blog.sevagas.com/?Hacking-around-HTA-files + * https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 + * https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect HTML Help Using InfoTech Storage Handlers The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218.001/) - **Last Updated**: 2021-02-11
@@ -4570,15 +10329,24 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_using_infotech_storage_handlers_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_using_infotech_storage_handlers_filter` ``` #### Associated Analytic Story +* Suspicious Compiled HTML Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4586,32 +10354,44 @@ To successfully implement this search you need to be ingesting information on pr | T1218.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed. #### References + * https://attack.mitre.org/techniques/T1218/001/ + * https://www.kb.cert.org/vuls/id/851869 + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + * https://lolbas-project.github.io/lolbas/Binaries/Hh/ + * https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 + * https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect IPv6 Network Infrastructure Threats By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1200, T1498, T1557.002 +- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557.002](https://attack.mitre.org/techniques/T1557.002/) - **Last Updated**: 2020-10-28
@@ -4619,15 +10399,26 @@ By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organiz #### Search ``` -`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") | eval src_interface=src_int_prefix_long+src_int_suffix | eval dest_interface=dest_int_prefix_long+dest_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface | table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detect_ipv6_network_infrastructure_threats_filter` +`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") +| eval src_interface=src_int_prefix_long+src_int_suffix +| eval dest_interface=dest_int_prefix_long+dest_int_suffix +| stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface +| table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_ipv6_network_infrastructure_threats_filter` ``` #### Associated Analytic Story +* Router and Infrastructure Security + + #### How To Implement This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4637,35 +10428,50 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | T1557.002 | x | x | #### Kill Chain Phases + * Reconnaissance + * Delivery + * Actions on Objectives + #### Known False Positives None currently known #### References + * https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf + * https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html + * https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html + * https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html + * https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html + * https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html + * https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html + * https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html + #### Test Dataset + _version_: 1
--- + ### Detect Kerberoasting This search detects a potential kerberoasting attack via service principal name requests - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1558.003 +- **ATT&CK**: [T1558.003](https://attack.mitre.org/techniques/T1558.003/) - **Last Updated**: 2020-10-21
@@ -4673,21 +10479,37 @@ This search detects a potential kerberoasting attack via service principal name #### Search ``` - | from read_ssa_enriched_events() | eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id") | where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" | first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] | where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID | eval start_time=_time, end_time=_time, body="TBD", entities="TBD" | select start_time, end_time, entities, body | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id") +| where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" +| first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] +| where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID +| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" +| select start_time, end_time, entities, body +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2 #### Required fields + * service_name + * _time + * event_code + * ticket_encryption_type + * service_id + * ticket_options + #### ATT&CK | ID | technique | Tactic | @@ -4695,26 +10517,32 @@ The test data is converted from Windows Security Event logs generated from Attac | T1558.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Older systems that support kerberos RC4 by default NetApp may generate false positives #### References + * Initial ESCU implementation by Jose Hernandez and Patrick Bareiss + #### Test Dataset + _version_: 1
--- + ### Detect Large Outbound ICMP Packets This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1095 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1095](https://attack.mitre.org/techniques/T1095/) - **Last Updated**: 2018-06-01
@@ -4722,15 +10550,25 @@ This search looks for outbound ICMP packets with a packet size larger than 1,000 #### Search ``` -| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter` + +| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip +| `drop_dm_object_name("All_Traffic")` +| search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_large_outbound_icmp_packets_filter` ``` #### Associated Analytic Story +* Command and Control + + #### How To Implement In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4738,25 +10576,30 @@ In order to run this search effectively, we highly recommend that you leverage t | T1095 | x | x | #### Kill Chain Phases + * Command and Control + #### Known False Positives ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list. #### References + #### Test Dataset + _version_: 2
--- + ### Detect Long DNS TXT Record Response This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048.003 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) - **Last Updated**: 2020-07-21
@@ -4764,15 +10607,30 @@ This search is used to detect attempts to use DNS tunneling, by calculating the #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" | `detect_long_dns_txt_record_response_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type +| `drop_dm_object_name("DNS")` +| eval anslen=len(answer) +| search anslen>100 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count +| table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" +| `detect_long_dns_txt_record_response_filter` ``` #### Associated Analytic Story +* Suspicious DNS Traffic + +* Command and Control + + #### How To Implement To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4780,25 +10638,30 @@ To successfully implement this search you need to ingest data from your DNS logs | T1048.003 | x | x | #### Kill Chain Phases + * Command and Control + #### Known False Positives It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. #### References + #### Test Dataset + _version_: 2
--- + ### Detect MSHTA Url in Command Line This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.005 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-20
@@ -4806,15 +10669,24 @@ This analytic identifies when Microsoft HTML Application Host (mshta.exe) utilit #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mshta_url_in_command_line_filter` ``` #### Associated Analytic Story +* Suspicious MSHTA Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4822,29 +10694,38 @@ To successfully implement this search you need to be ingesting information on pr | T1218.005 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives It is possible legitimate applications may perform this behavior and will need to be filtered. #### References + * https://github.com/redcanaryco/AtomicTestHarnesses + * https://redcanary.com/blog/introducing-atomictestharnesses/ + * https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect Mimikatz Using Loaded Images This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1003.001 +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2019-12-03
@@ -4852,15 +10733,29 @@ This search looks for reading loaded Images unique to credential dumping with Mi #### Search ``` -`sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_using_loaded_images_filter` +`sysmon` EventCode=7 +| stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image +| search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mimikatz_using_loaded_images_filter` ``` #### Associated Analytic Story +* Credential Dumping + +* Detect Zerologon Attack + +* Cloud Federated Credential Abuse + + #### How To Implement This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4868,26 +10763,32 @@ This search needs Sysmon Logs and a sysmon configuration, which includes EventCo | T1003.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Other tools can import the same DLLs. These tools should be part of a whitelist. #### References + * https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html + #### Test Dataset + _version_: 1
--- + ### Detect Mimikatz Via PowerShell And EventCode 4703 This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1003.001 +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2019-02-27
@@ -4895,15 +10796,27 @@ This search looks for PowerShell requesting privileges consistent with credentia #### Search ``` -`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_via_powershell_and_eventcode_4703_filter` +`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe +| rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" +| where privs="SeDebugPrivilege" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message +| rename privs as "Enabled Privilege" +| rename Process_Name as process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mimikatz_via_powershell_and_eventcode_4703_filter` ``` #### Associated Analytic Story +* Cloud Federated Credential Abuse + + #### How To Implement You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4911,25 +10824,30 @@ You must be ingesting Windows Security logs. You must also enable the account ch | T1003.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. #### References + #### Test Dataset + _version_: 2
--- + ### Detect New Local Admin account This search looks for newly created accounts that have been elevated to local administrators. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1136.001 +- **ATT&CK**: [T1136.001](https://attack.mitre.org/techniques/T1136.001/) - **Last Updated**: 2020-07-08
@@ -4937,15 +10855,25 @@ This search looks for newly created accounts that have been elevated to local ad #### Search ``` -`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) | transaction member_id connected=false maxspan=180m | rename member_id as user | stats count min(_time) as firstTime max(_time) as lastTime by user dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_local_admin_account_filter` +`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) +| transaction member_id connected=false maxspan=180m +| rename member_id as user +| stats count min(_time) as firstTime max(_time) as lastTime by user dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_new_local_admin_account_filter` ``` #### Associated Analytic Story +* DHS Report TA18-074A + + #### How To Implement You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732 #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -4953,28 +10881,37 @@ You must be ingesting Windows event logs using the Splunk Windows TA and collect | T1136.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + * Command and Control + #### Known False Positives The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + _version_: 2
--- + ### Detect New Login Attempts to Routers The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Authentication - **ATT&CK**: - **Last Updated**: 2017-09-12 @@ -4983,40 +10920,56 @@ The search queries the authentication logs for assets that are categorized as ro #### Search ``` -| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter` + +| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user +| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) +| where isOutlier=1 +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| `drop_dm_object_name("Authentication")` +| `detect_new_login_attempts_to_routers_filter` ``` #### Associated Analytic Story +* Router and Infrastructure Security + + #### How To Implement To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Legitimate router connections may appear as new connections #### References + #### Test Dataset + _version_: 1 --- + ### Detect New Open GCP Storage Buckets This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1530 +- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2020-08-05
@@ -5024,15 +10977,29 @@ This search looks for GCP PubSub events where a user has created an open/public #### Search ``` -`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions | spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action | spath output=user path=data.protoPayload.authenticationInfo.principalEmail | spath output=location path=data.protoPayload.resourceLocation.currentLocations{} | spath output=src path=data.protoPayload.requestMetadata.callerIp | spath output=bucketName path=data.protoPayload.resourceName | spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role | spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member | search (member=allUsers AND action=ADD) | table _time, bucketName, src, user, location, action, role, member | search `detect_new_open_gcp_storage_buckets_filter` +`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions +| spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action +| spath output=user path=data.protoPayload.authenticationInfo.principalEmail +| spath output=location path=data.protoPayload.resourceLocation.currentLocations{} +| spath output=src path=data.protoPayload.requestMetadata.callerIp +| spath output=bucketName path=data.protoPayload.resourceName +| spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role +| spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member +| search (member=allUsers AND action=ADD) +| table _time, bucketName, src, user, location, action, role, member +| search `detect_new_open_gcp_storage_buckets_filter` ``` #### Associated Analytic Story +* Suspicious GCP Storage Activities + + #### How To Implement This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5040,25 +11007,30 @@ This search relies on the Splunk Add-on for Google Cloud Platform, setting up a | T1530 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. #### References + #### Test Dataset + _version_: 1
--- + ### Detect New Open S3 Buckets over AWS CLI This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1530 +- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2021-01-12
@@ -5066,15 +11038,25 @@ This search looks for CloudTrail events where a user has created an open/public #### Search ``` -`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") | rename requestParameters.bucketName AS bucketName | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_over_aws_cli_filter` +`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") +| rename requestParameters.bucketName AS bucketName +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_new_open_s3_buckets_over_aws_cli_filter` ``` #### Associated Analytic Story +* Suspicious AWS S3 Activities + + #### How To Implement #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5082,26 +11064,32 @@ This search looks for CloudTrail events where a user has created an open/public | T1530 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + _version_: 1
--- + ### Detect New Open S3 buckets This search looks for CloudTrail events where a user has created an open/public S3 bucket. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1530 +- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2021-01-12
@@ -5109,15 +11097,32 @@ This search looks for CloudTrail events where a user has created an open/public #### Search ``` -`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex field=_raw "(?{.+})" | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} | search grantees=* | mvexpand grantees | spath input=grantees output=uri path=Grantee.URI | spath input=grantees output=permission path=Permission | search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") | search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") | rename requestParameters.bucketName AS bucketName | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_filter` +`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl +| rex field=_raw "(?{.+})" +| spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} +| search grantees=* +| mvexpand grantees +| spath input=grantees output=uri path=Grantee.URI +| spath input=grantees output=permission path=Permission +| search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") +| search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") +| rename requestParameters.bucketName AS bucketName +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission bucketName +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_new_open_s3_buckets_filter` ``` #### Associated Analytic Story +* Suspicious AWS S3 Activities + + #### How To Implement You must install the AWS App for Splunk. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5125,26 +11130,32 @@ You must install the AWS App for Splunk. | T1530 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + _version_: 2
--- + ### Detect Oulook exe writing a zip file This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1566.001 +- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/) - **Last Updated**: 2020-07-21
@@ -5152,15 +11163,35 @@ This search looks for execution of process `outlook.exe` where the process is wr #### Search ``` -| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| rename parent_process_id as outlook_id| join malicious_id type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] | table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path | where file_name != "" | `detect_oulook_exe_writing_a__zip_file_filter` + +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename process_id as malicious_id +| rename parent_process_id as outlook_id +| join malicious_id type=inner[ +| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename process_id as malicious_id +| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] +| table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path +| where file_name != "" +| `detect_oulook_exe_writing_a__zip_file_filter` ``` #### Associated Analytic Story +* Phishing Payloads + + #### How To Implement You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5168,26 +11199,32 @@ You must be ingesting data that records filesystem and process activity from you | T1566.001 | x | x | #### Kill Chain Phases + * Installation + * Actions on Objectives + #### Known False Positives It is not uncommon for outlook to write legitimate zip files to the disk. #### References + #### Test Dataset + _version_: 3
--- + ### Detect Outbound SMB Traffic This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1071.002 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1071.002](https://attack.mitre.org/techniques/T1071.002/) - **Last Updated**: 2020-07-21
@@ -5195,15 +11232,28 @@ This search looks for outbound SMB connections made by hosts within your network #### Search ``` -| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(start_time)` | `security_content_ctime(end_time)` | `detect_outbound_smb_traffic_filter` + +| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip +| `drop_dm_object_name("All_Traffic")` +| `security_content_ctime(start_time)` +| `security_content_ctime(end_time)` +| `detect_outbound_smb_traffic_filter` ``` #### Associated Analytic Story +* Hidden Cobra Malware + +* DHS Report TA18-074A + +* Sunburst Malware + + #### How To Implement In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5211,26 +11261,32 @@ In order to run this search effectively, we highly recommend that you leverage t | T1071.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + * Command and Control + #### Known False Positives It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. #### References + #### Test Dataset + _version_: 3
--- + ### Detect Pass the Hash This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1550.002 +- **ATT&CK**: [T1550.002](https://attack.mitre.org/techniques/T1550.002/) - **Last Updated**: 2020-10-21
@@ -5238,21 +11294,37 @@ This search looks for specific authentication events from the Windows Security E #### Search ``` - | from read_ssa_enriched_events() | eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), LogonType=map_get(input_event, "logon_type"), LogonProcess=map_get(input_event, "logon_process"), ComputerName=map_get(input_event, "dest_ip_primary_artifact"), AccountName=map_get(input_event, "dest_user_primary_artifact") | where (LogonType="3" AND LogonProcess="NtLmSsp" AND AccountName IS NOT NULL) OR (LogonType="9" AND LogonProcess="seclogo") | first_time_event input_columns=["EventCode","LogonProcess","ComputerName"] | where first_time_EventCode_LogonProcess_ComputerName | eval start_time=_time, end_time=_time, body="TBD", entities="TBD" | select start_time, end_time, entities, body | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), LogonType=map_get(input_event, "logon_type"), LogonProcess=map_get(input_event, "logon_process"), ComputerName=map_get(input_event, "dest_ip_primary_artifact"), AccountName=map_get(input_event, "dest_user_primary_artifact") +| where (LogonType="3" AND LogonProcess="NtLmSsp" AND AccountName IS NOT NULL) OR (LogonType="9" AND LogonProcess="seclogo") +| first_time_event input_columns=["EventCode","LogonProcess","ComputerName"] +| where first_time_EventCode_LogonProcess_ComputerName +| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" +| select start_time, end_time, entities, body +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2 #### Required fields + * logon_process + * dest_user_primary_artifact + * _time + * event_code + * dest_ip_primary_artifact + * logon_type + #### ATT&CK | ID | technique | Tactic | @@ -5260,26 +11332,32 @@ The test data is converted from Windows Security Event logs generated from Attac | T1550.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. #### References + * Initial ESCU implementation by Bhavin Patel and Patrick Bareiss + #### Test Dataset + _version_: 1
--- + ### Detect Path Interception By Creation Of program exe The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1574.009 +- **Data Models**: Endpoint +- **ATT&CK**: [T1574.009](https://attack.mitre.org/techniques/T1574.009/) - **Last Updated**: 2020-07-03
@@ -5287,15 +11365,31 @@ The detection Detect Path Interception By Creation Of program exe is detecting t #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest index | `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?[^\\\\]*\.(?:exe|bat|com|ps1))" | eval process_name = lower(process_name) | eval service_process = lower(service_process) | where process_name != service_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest index +| `drop_dm_object_name(Processes)` +| rex field=process "^.*?\\\\(?[^\\\\]*\.(?:exe +|bat +|com +|ps1))" +| eval process_name = lower(process_name) +| eval service_process = lower(service_process) +| where process_name != service_process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_path_interception_by_creation_of_program_exe_filter` ``` #### Associated Analytic Story +* Windows Persistence Techniques + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5303,27 +11397,34 @@ You must be ingesting data that records process activity from your hosts to popu | T1574.009 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives unknown #### References + * https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log + _version_: 3
--- + ### Detect Port Security Violation By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1200, T1498, T1557.002 +- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557.002](https://attack.mitre.org/techniques/T1557.002/) - **Last Updated**: 2020-10-28
@@ -5331,15 +11432,24 @@ By enabling Port Security on a Cisco switch you can restrict input to an interfa #### Search ``` -`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_port_security_violation_filter` +`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") +| eval src_interface=src_int_prefix_long+src_int_suffix +| stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_port_security_violation_filter` ``` #### Associated Analytic Story +* Router and Infrastructure Security + + #### How To Implement This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5349,28 +11459,36 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | T1557.002 | x | x | #### Kill Chain Phases + * Reconnaissance + * Delivery + * Exploitation + * Actions on Objectives + #### Known False Positives This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network. #### References + #### Test Dataset + _version_: 1
--- + ### Detect Prohibited Applications Spawning cmd exe This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059.003/) - **Last Updated**: 2020-11-10
@@ -5378,15 +11496,31 @@ This search looks for executions of cmd.exe spawned by a process that is often a #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] | `detect_prohibited_applications_spawning_cmd_exe_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|search [`prohibited_apps_launching_cmd`] +| `detect_prohibited_applications_spawning_cmd_exe_filter` ``` #### Associated Analytic Story +* Suspicious Command-Line Executions + +* Suspicious MSHTA Activity + +* Suspicious Zoom Child Processes + +* Sunburst Malware + + #### How To Implement You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5394,26 +11528,32 @@ You must be ingesting data that records process activity from your hosts and pop | T1059.003 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/powershell_spawn_cmd/windows-sysmon.log + _version_: 5
--- + ### Detect Prohibited Applications Spawning cmd exe This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. This is a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe` by @bpatel. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1059 +- **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/) - **Last Updated**: 2020-7-13
@@ -5421,23 +11561,38 @@ This search looks for executions of cmd.exe spawned by a process that is often a #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) -| where process_name="cmd.exe" | rex field=parent_process "(?[^\\\\]+)$" | where field0="winword.exe" OR field0="excel.exe" OR field0="outlook.exe" OR field0="powerpnt.exe" OR field0="visio.exe" OR field0="mspub.exe" OR field0="acrobat.exe" OR field0="acrord32.exe" OR field0="chrome.exe" OR field0="iexplore.exe" OR field0="opera.exe" OR field0="firefox.exe" OR field0="java.exe" OR field0="powershell.exe" -| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body="TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) + +| where process_name="cmd.exe" +| rex field=parent_process "(?[^\\\\]+)$" +| where field0="winword.exe" OR field0="excel.exe" OR field0="outlook.exe" OR field0="powerpnt.exe" OR field0="visio.exe" OR field0="mspub.exe" OR field0="acrobat.exe" OR field0="acrord32.exe" OR field0="chrome.exe" OR field0="iexplore.exe" OR field0="opera.exe" OR field0="firefox.exe" OR field0="java.exe" OR field0="powershell.exe" + +| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body="TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting sysmon logs. This search has been modified to process raw sysmon data from attack_range's nxlogs on DSP. #### Required fields + * process_name + * parent_process_name + * _time + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -5445,25 +11600,30 @@ You must be ingesting sysmon logs. This search has been modified to process raw | T1059 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. #### References + #### Test Dataset + _version_: 1
--- + ### Detect PsExec With accepteula Flag This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1021.002 +- **Data Models**: Endpoint +- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/) - **Last Updated**: 2020-11-10
@@ -5471,15 +11631,26 @@ This search looks for events where `PsExec.exe` is run with the `accepteula` fla #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest Processes.parent_process_name +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_psexec_with_accepteula_flag_filter` ``` #### Associated Analytic Story +* SamSam Ransomware + +* DHS Report TA18-074A + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5487,25 +11658,31 @@ You must be ingesting data that records process activity from your hosts to popu | T1021.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/windows-sysmon.log + _version_: 3
--- + ### Detect Rare Executables This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Endpoint - **ATT&CK**: - **Last Updated**: 2020-03-16 @@ -5514,42 +11691,69 @@ This search will return a table of rare processes, the names of the systems runn #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | rex field=user "(?.*)\\\\(?.*)" | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_allow_list`| table process ] | `detect_rare_executables_filter` + +| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name +| rename Processes.process_name as process +| rex field=user "(?.*)\\\\(?.*)" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search [ +| tstats count from datamodel=Endpoint.Processes by Processes.process_name +| rare Processes.process_name limit=30 +| rename Processes.process_name as process +| `filter_rare_process_allow_list` +| table process ] +| `detect_rare_executables_filter` ``` #### Associated Analytic Story +* Emotet Malware DHS Report TA18-201A + +* Unusual Processes + +* Cloud Federated Credential Abuse + + #### How To Implement To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_allow_list` searches two lookup files for allowed processes. These consist of `rare_process_allow_list_default.csv` and `rare_process_allow_list_local.csv`. To add your own processes to the allow list, add them to `rare_process_allow_list_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the allow_list value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Installation + * Command and Control + * Actions on Objectives + #### Known False Positives Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_allow_list_local.csv` to filter them out of your search results. #### References + #### Test Dataset + _version_: 5 --- + ### Detect Regasm Spawning a Process The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.009 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-12
@@ -5557,15 +11761,24 @@ The following analytic identifies regasm.exe spawning a process. This particular #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_spawning_a_process_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regasm_spawning_a_process_filter` ``` #### Associated Analytic Story +* Suspicious Regsvcs Regasm Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5573,30 +11786,40 @@ To successfully implement this search you need to be ingesting information on pr | T1218.009 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. #### References + * https://attack.mitre.org/techniques/T1218/009/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + * https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + * https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect Regasm with Network Connection The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1218.009 +- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-16
@@ -5604,15 +11827,24 @@ The following analytic identifies regasm.exe with a network connection to a publ #### Search ``` -`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_network_connection_filter` +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regasm_with_network_connection_filter` ``` #### Associated Analytic Story +* Suspicious Regsvcs Regasm Activity + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5620,29 +11852,38 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1218.009 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. #### References + * https://attack.mitre.org/techniques/T1218/009/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + * https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect Regasm with no Command Line Arguments The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1218.009 +- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-12
@@ -5650,15 +11891,25 @@ The following analytic identifies regasm.exe with no command line arguments. Thi #### Search ``` -`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) | regex CommandLine="(regasm\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_no_command_line_arguments_filter` +`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) +| regex CommandLine="(regasm\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regasm_with_no_command_line_arguments_filter` ``` #### Associated Analytic Story +* Suspicious Regsvcs Regasm Activity + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5666,29 +11917,38 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1218.009 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. #### References + * https://attack.mitre.org/techniques/T1218/009/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + * https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect Regsvcs Spawning a Process The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.009 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-12
@@ -5696,15 +11956,24 @@ The following analytic identifies regsvcs.exe spawning a process. This particula #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_spawning_a_process_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regsvcs_spawning_a_process_filter` ``` #### Associated Analytic Story +* Suspicious Regsvcs Regasm Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5712,29 +11981,38 @@ To successfully implement this search you need to be ingesting information on pr | T1218.009 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. #### References + * https://attack.mitre.org/techniques/T1218/009/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + * https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect Regsvcs with Network Connection The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1218.009 +- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-16
@@ -5742,15 +12020,24 @@ The following analytic identifies Regsvcs.exe with a network connection to a pub #### Search ``` -`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_network_connection_filter` +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regsvcs_with_network_connection_filter` ``` #### Associated Analytic Story +* Suspicious Regsvcs Regasm Activity + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5758,29 +12045,38 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1218.009 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. #### References + * https://attack.mitre.org/techniques/T1218/009/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + * https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect Regsvcs with No Command Line Arguments The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1218.009 +- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-12
@@ -5788,15 +12084,25 @@ The following analytic identifies regsvcs.exe with no command line arguments. Th #### Search ``` -`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) | regex CommandLine="(regsvcs\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_no_command_line_arguments_filter` +`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) +| regex CommandLine="(regsvcs\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regsvcs_with_no_command_line_arguments_filter` ``` #### Associated Analytic Story +* Suspicious Regsvcs Regasm Activity + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5804,30 +12110,39 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1218.009 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. #### References + * https://attack.mitre.org/techniques/T1218/009/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + * https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect Regsvr32 Application Control Bypass Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.010 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.010](https://attack.mitre.org/techniques/T1218.010/) - **Last Updated**: 2021-01-28
@@ -5835,15 +12150,24 @@ Upon investigating, look for network connections to remote destinations (interna #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_regsvr32_application_control_bypass_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regsvr32_application_control_bypass_filter` ``` #### Associated Analytic Story +* Suspicious Regsvr32 Activity + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5851,30 +12175,40 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1218.010 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Limited false positives related to third party software registering .DLL's. #### References + * https://attack.mitre.org/techniques/T1218/010/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md + * https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ + * https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect Rogue DHCP Server By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1200, T1498, T1557 +- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557](https://attack.mitre.org/techniques/T1557/) - **Last Updated**: 2020-08-11
@@ -5882,15 +12216,23 @@ By enabling DHCP Snooping as a Layer 2 Security measure on the organization's ne #### Search ``` -`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_rogue_dhcp_server_filter` +`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" +| stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_rogue_dhcp_server_filter` ``` #### Associated Analytic Story +* Router and Infrastructure Security + + #### How To Implement This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5900,27 +12242,34 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | T1557 | x | x | #### Kill Chain Phases + * Reconnaissance + * Delivery + * Actions on Objectives + #### Known False Positives This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface. #### References + #### Test Dataset + _version_: 1
--- + ### Detect Rundll32 Application Control Bypass - advpack The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-04
@@ -5928,15 +12277,24 @@ The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___advpack_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_rundll32_application_control_bypass___advpack_filter` ``` #### Associated Analytic Story +* Suspicious Rundll32 Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5944,31 +12302,42 @@ To successfully implement this search you need to be ingesting information on pr | T1218.011 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive. #### References + * https://attack.mitre.org/techniques/T1218/011/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + * https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + * https://lolbas-project.github.io/lolbas/Libraries/Advpack/ + * https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect Rundll32 Application Control Bypass - setupapi The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-04
@@ -5976,15 +12345,24 @@ The following analytic identifies rundll32.exe loading setupapi.dll and iesetupa #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___setupapi_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_rundll32_application_control_bypass___setupapi_filter` ``` #### Associated Analytic Story +* Suspicious Rundll32 Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -5992,31 +12370,42 @@ To successfully implement this search you need to be ingesting information on pr | T1218.011 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, some legitimate applications may use setupapi triggering a false positive. #### References + * https://attack.mitre.org/techniques/T1218/011/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + * https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + * https://lolbas-project.github.io/lolbas/Libraries/Setupapi/ + * https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect Rundll32 Application Control Bypass - syssetup The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-04
@@ -6024,15 +12413,24 @@ The following analytic identifies rundll32.exe loading syssetup.dll by calling t #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___syssetup_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_rundll32_application_control_bypass___syssetup_filter` ``` #### Associated Analytic Story +* Suspicious Rundll32 Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6040,31 +12438,42 @@ To successfully implement this search you need to be ingesting information on pr | T1218.011 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive. #### References + * https://attack.mitre.org/techniques/T1218/011/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + * https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + * https://lolbas-project.github.io/lolbas/Libraries/Syssetup/ + * https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect Rundll32 Inline HTA Execution The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.005 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-20
@@ -6072,15 +12481,24 @@ The following analytic identifies "rundll32.exe" execution with inline protocol #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_rundll32_inline_hta_execution_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_rundll32_inline_hta_execution_filter` ``` #### Associated Analytic Story +* Suspicious MSHTA Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6088,29 +12506,38 @@ To successfully implement this search you need to be ingesting information on pr | T1218.005 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. #### References + * https://github.com/redcanaryco/AtomicTestHarnesses + * https://redcanary.com/blog/introducing-atomictestharnesses/ + * https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Detect S3 access from a new IP This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1530 +- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2018-06-28
@@ -6118,15 +12545,32 @@ This search looks at S3 bucket-access logs and detects new or previously unseen #### Search ``` -`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter` +`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 +| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip +| inputlookup append=t previously_seen_S3_access_from_remote_ip.csv +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip +| outputlookup previously_seen_S3_access_from_remote_ip.csv +| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newIP=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table bucket_name remote_ip] +| iplocation remote_ip +|rename remote_ip as src_ip +| table _time bucket_name src_ip City Country operation request_uri +| `detect_s3_access_from_a_new_ip_filter` ``` #### Associated Analytic Story +* Suspicious AWS S3 Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6134,25 +12578,30 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1530 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour #### References + #### Test Dataset + _version_: 1
--- + ### Detect SNICat SNI Exfiltration This search looks for commands that the SNICat tool uses in the TLS SNI field. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1041 +- **ATT&CK**: [T1041](https://attack.mitre.org/techniques/T1041/) - **Last Updated**: 2020-10-21
@@ -6160,15 +12609,34 @@ This search looks for commands that the SNICat tool uses in the TLS SNI field. #### Search ``` -`zeek_ssl` | rex field=server_name "(?(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)" | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter` +`zeek_ssl` +| rex field=server_name "(?(LIST +|LS +|SIZE +|LD +|CB +|CD +|EX +|ALIVE +|EXIT +|WHERE +|finito)-[A-Za-z0-9]{16}\.)" +| stats count by src_ip dest_ip server_name snicat +| where count>0 +| table src_ip dest_ip server_name snicat +| `detect_snicat_sni_exfiltration_filter` ``` #### Associated Analytic Story +* Data Exfiltration + + #### How To Implement You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6176,28 +12644,36 @@ You must be ingesting Zeek SSL data into Splunk. Zeek data should also be gettin | T1041 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Unknown #### References + * https://www.mnemonic.no/blog/introducing-snicat/ + * https://github.com/mnemonic-no/SNIcat + * https://attack.mitre.org/techniques/T1041/ + #### Test Dataset + _version_: 1
--- + ### Detect Software Download To Network Device Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1542.005 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1542.005](https://attack.mitre.org/techniques/T1542.005/) - **Last Updated**: 2020-10-28
@@ -6205,15 +12681,24 @@ Adversaries may abuse netbooting to load an unauthorized network device operatin #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| `drop_dm_object_name("All_Traffic")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_software_download_to_network_device_filter` ``` #### Associated Analytic Story +* Router and Infrastructure Security + + #### How To Implement This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6221,25 +12706,30 @@ This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from | T1542.005 | x | x | #### Kill Chain Phases + * Delivery + #### Known False Positives This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices. #### References + #### Test Dataset + _version_: 1
--- + ### Detect Spike in AWS API Activity This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.004 +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-07-21
@@ -6247,10 +12737,32 @@ This search will detect users creating spikes of API activity in your AWS enviro #### Search ``` -`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_aws_api_activity_filter` +`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup api_call_by_user_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup api_call_by_user_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_aws_api_activity_filter` ``` #### Associated Analytic Story +* AWS User Monitoring + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ @@ -6262,6 +12774,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6269,19 +12782,24 @@ Detailed documentation on how to create a new field within Incident Review may b | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives #### References + #### Test Dataset + _version_: 2
--- + ### Detect Spike in AWS Security Hub Alerts for EC2 Instance This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals @@ -6295,15 +12813,27 @@ This search looks for a spike in number of of AWS security Hub alerts for an EC2 #### Search ``` -`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket span=4h _time | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest | eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev | eval threshold_value = 3 | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` +`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance +| bucket span=4h _time +| stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest +| eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev +| eval threshold_value = 3 +| eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) +| search isOutlier=1 +| table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg +| `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` ``` #### Associated Analytic Story +* AWS Security Hub Alerts + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6311,18 +12841,23 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives None #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json + _version_: 3 --- + ### Detect Spike in AWS Security Hub Alerts for User This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. @@ -6336,15 +12871,28 @@ This search looks for a spike in number of of AWS security Hub alerts for an AWS #### Search ``` -`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename findings{}.Resources{}.Id as user | bucket span=4h _time | stats count AS alerts by _time user | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev | eval threshold_value = 2 | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter` +`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser +| rename findings{}.Resources{}.Id as user +| bucket span=4h _time +| stats count AS alerts by _time user +| eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev +| eval threshold_value = 2 +| eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) +| search isOutlier=1 +| table _time user alerts +|`detect_spike_in_aws_security_hub_alerts_for_user_filter` ``` #### Associated Analytic Story +* AWS Security Hub Alerts + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6352,23 +12900,27 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives None #### References + #### Test Dataset + _version_: 3 --- + ### Detect Spike in Network ACL Activity This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1562.007 +- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562.007/) - **Last Updated**: 2018-05-21
@@ -6376,15 +12928,38 @@ This search will detect users creating spikes in API activity related to network #### Search ``` -`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup network_acl_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_network_acl_activity_filter` +`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup network_acl_activity_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup network_acl_activity_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_network_acl_activity_filter` ``` #### Associated Analytic Story +* AWS Network ACL Activity + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6392,25 +12967,30 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1562.007 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. #### References + #### Test Dataset + _version_: 1
--- + ### Detect Spike in S3 Bucket deletion This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1530 +- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2018-11-27
@@ -6418,15 +12998,39 @@ This search detects users creating spikes in API activity related to deletion of #### Search ``` -`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter` +`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup s3_deletion_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup s3_deletion_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| spath output=bucketName path=requestParameters.bucketName +| stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_s3_bucket_deletion_filter` ``` #### Associated Analytic Story +* Suspicious AWS S3 Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6434,25 +13038,30 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1530 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. #### References + #### Test Dataset + _version_: 1
--- + ### Detect Spike in Security Group Activity This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.004 +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2018-04-18
@@ -6460,15 +13069,38 @@ This search will detect users creating spikes in API activity related to securit #### Search ``` -`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup security_group_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_security_group_activity_filter` +`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup security_group_activity_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup security_group_activity_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_security_group_activity_filter` ``` #### Associated Analytic Story +* AWS User Monitoring + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6476,19 +13108,24 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. #### References + #### Test Dataset + _version_: 1
--- + ### Detect Spike in blocked Outbound Traffic from your AWS This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. @@ -6502,41 +13139,71 @@ This search will detect spike in blocked outbound network connections originatin #### Search ``` -`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` +`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) +| stats count as numberOfBlockedConnections by src_ip +| inputlookup baseline_blocked_outbound_connections append=t +| fields - latestCount +| stats values(*) as * by src_ip +| rename numberOfBlockedConnections as latestCount +| eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 +| eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) +| eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections +| outputlookup baseline_blocked_outbound_connections +| eval dataPointThreshold = 5, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| table src_ip] +| stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip +| `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` ``` #### Associated Analytic Story +* AWS Network ACL Activity + +* Suspicious AWS Traffic + +* Command and Control + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Actions on Objectives + * Command and Control + #### Known False Positives The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. #### References + #### Test Dataset + _version_: 1 --- + ### Detect Traffic Mirroring Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1200, T1498, T1020.001 +- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1020.001](https://attack.mitre.org/techniques/T1020.001/) - **Last Updated**: 2020-10-28
@@ -6544,15 +13211,23 @@ Adversaries may leverage traffic mirroring in order to automate data exfiltratio #### Search ``` -`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_traffic_mirroring_filter` +`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") +| stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_traffic_mirroring_filter` ``` #### Associated Analytic Story +* Router and Infrastructure Security + + #### How To Implement This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6562,25 +13237,31 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | T1020.001 | x | x | #### Kill Chain Phases + * Delivery + * Actions on Objectives + #### Known False Positives This search will return false positives for any legitimate traffic captures by network administrators. #### References + #### Test Dataset + _version_: 1
--- + ### Detect USB device insertion The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Change_Analysis - **ATT&CK**: - **Last Updated**: 2017-11-27 @@ -6589,40 +13270,55 @@ The search is used to detect hosts that generate Windows Event ID 4663 for succe #### Search ``` -| tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `detect_usb_device_insertion_filter` + +| tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest +| `drop_dm_object_name("All_Changes")` +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| `detect_usb_device_insertion_filter` ``` #### Associated Analytic Story +* Data Protection + + #### How To Implement To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Installation + * Actions on Objectives + #### Known False Positives Legitimate USB activity will also be detected. Please verify and investigate as appropriate. #### References + #### Test Dataset + _version_: 1 --- + ### Detect Unauthorized Assets by MAC address By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Network_Sessions - **ATT&CK**: - **Last Updated**: 2017-09-13 @@ -6631,42 +13327,62 @@ By populating the organization's assets within the assets_by_str.csv, we will be #### Search ``` -| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac | dedup All_Sessions.dest_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` | search NOT [| inputlookup asset_lookup_by_str |rename mac as dest_mac | fields + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter` + +| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac +| dedup All_Sessions.dest_mac +| `drop_dm_object_name("Network_Sessions")` +|`drop_dm_object_name("All_Sessions")` +| search NOT [ +| inputlookup asset_lookup_by_str +|rename mac as dest_mac +| fields + dest_mac] +| `detect_unauthorized_assets_by_mac_address_filter` ``` #### Associated Analytic Story +* Asset Tracking + + #### How To Implement This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Reconnaissance + * Delivery + * Actions on Objectives + #### Known False Positives This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. #### References + #### Test Dataset + _version_: 1 --- + ### Detect Use of cmd exe to Launch Script Interpreters This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059.003/) - **Last Updated**: 2020-07-21
@@ -6674,15 +13390,26 @@ This search looks for the execution of the cscript.exe or wscript.exe processes, #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` ``` #### Associated Analytic Story +* Emotet Malware DHS Report TA18-201A + +* Suspicious Command-Line Executions + + #### How To Implement To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6690,26 +13417,32 @@ To successfully implement this search, you must be ingesting data that records p | T1059.003 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Some legitimate applications may exhibit this behavior. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/cmd_spawns_cscript/windows-sysmon.log + _version_: 4
--- + ### Detect Windows DNS SIGRed via Splunk Stream This search detects SIGRed via Splunk Stream. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1203 +- **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/) - **Last Updated**: 2020-07-28
@@ -6717,15 +13450,28 @@ This search detects SIGRed via Splunk Stream. #### Search ``` -`stream_dns` | spath "query_type{}" | search "query_type{}" IN (SIG,KEY) | spath protocol_stack | search protocol_stack="ip:tcp:dns" | append [search `stream_tcp` bytes_out>65000] | `detect_windows_dns_sigred_via_splunk_stream_filter` | stats count by flow_id | where count>1 | fields - count +`stream_dns` +| spath "query_type{}" +| search "query_type{}" IN (SIG,KEY) +| spath protocol_stack +| search protocol_stack="ip:tcp:dns" +| append [search `stream_tcp` bytes_out>65000] +| `detect_windows_dns_sigred_via_splunk_stream_filter` +| stats count by flow_id +| where count>1 +| fields - count ``` #### Associated Analytic Story +* Windows DNS SIGRed CVE-2020-1350 + + #### How To Implement You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6733,26 +13479,32 @@ You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting | T1203 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives unknown #### References + * https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + #### Test Dataset + _version_: 1
--- + ### Detect Windows DNS SIGRed via Zeek This search detects SIGRed via Zeek DNS and Zeek Conn data. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1203 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/) - **Last Updated**: 2020-07-28
@@ -6760,15 +13512,28 @@ This search detects SIGRed via Zeek DNS and Zeek Conn data. #### Search ``` -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id | rename DNS.flow_id as flow_id | append [| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id | rename All_Traffic.flow_id as flow_id] | `detect_windows_dns_sigred_via_zeek_filter` | stats count by flow_id | where count>1 | fields - count + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id +| rename DNS.flow_id as flow_id +| append [ +| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id +| rename All_Traffic.flow_id as flow_id] +| `detect_windows_dns_sigred_via_zeek_filter` +| stats count by flow_id +| where count>1 +| fields - count ``` #### Associated Analytic Story +* Windows DNS SIGRed CVE-2020-1350 + + #### How To Implement You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The Network Resolution and Network Traffic datamodels are in use for this search. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6776,26 +13541,32 @@ You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should | T1203 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives unknown #### References + * https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + #### Test Dataset + _version_: 1
--- + ### Detect Zerologon via Zeek This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1190 +- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/) - **Last Updated**: 2020-09-15
@@ -6803,15 +13574,23 @@ This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vul #### Search ``` -`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) | bin span=5m _time | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip | search opscount=3 authcount>4 passcount>0 | search `detect_zerologon_via_zeek_filter` +`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) +| bin span=5m _time +| stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip +| search opscount=3 authcount>4 passcount>0 +| search `detect_zerologon_via_zeek_filter` ``` #### Associated Analytic Story +* Detect Zerologon Attack + + #### How To Implement You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json. These three operations are then correlated on the Zeek UID field. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6819,28 +13598,36 @@ You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be ge | T1190 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives unknown #### References + * https://www.secura.com/blog/zero-logon + * https://github.com/SecuraBV/CVE-2020-1472 + * https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472 + #### Test Dataset + _version_: 1
--- + ### Detect attackers scanning for vulnerable JBoss servers This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1082 +- **Data Models**: Web +- **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/) - **Last Updated**: 2017-09-23
@@ -6848,15 +13635,26 @@ This search looks for specific GET or HEAD requests to web servers that are indi #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` ``` #### Associated Analytic Story +* JBoss Vulnerability + +* SamSam Ransomware + + #### How To Implement You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6864,25 +13662,30 @@ You must be ingesting data from the web server or network traffic that contains | T1082 | x | x | #### Kill Chain Phases + * Reconnaissance + #### Known False Positives It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. #### References + #### Test Dataset + _version_: 1
--- + ### Detect hosts connecting to dynamic domain providers Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1189 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1189](https://attack.mitre.org/techniques/T1189/) - **Last Updated**: 2021-01-14
@@ -6890,10 +13693,28 @@ Malicious actors often abuse legitimate Dynamic DNS services to host malicious p #### Search ``` -| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter` + +| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host +| `drop_dm_object_name("DNS")` +| `security_content_ctime(firstTime)` +| `dynamic_dns_providers` +| `detect_hosts_connecting_to_dynamic_domain_providers_filter` ``` #### Associated Analytic Story +* Data Protection + +* Prohibited Traffic Allowed or Protocol Mismatch + +* DNS Hijacking + +* Suspicious DNS Traffic + +* Dynamic DNS + +* Command and Control + + #### How To Implement First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ @@ -6905,6 +13726,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6912,26 +13734,33 @@ Detailed documentation on how to create a new field within Incident Review may b | T1189 | x | x | #### Kill Chain Phases + * Command and Control + * Actions on Objectives + #### Known False Positives Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log + _version_: 3
--- + ### Detect malicious requests to exploit JBoss servers This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Web - **ATT&CK**: - **Last Updated**: 2017-09-23 @@ -6940,40 +13769,58 @@ This search is used to detect malicious HTTP requests crafted to exploit jmx-con #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest +| search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table src, dest_ip, http_method, url, firstTime, lastTime +| `detect_malicious_requests_to_exploit_jboss_servers_filter` ``` #### Associated Analytic Story +* JBoss Vulnerability + +* SamSam Ransomware + + #### How To Implement You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Delivery + #### Known False Positives No known false positives for this detection. #### References + #### Test Dataset + _version_: 1 --- + ### Detect mshta inline hta execution The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.005 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-20
@@ -6981,15 +13828,24 @@ The following analytic identifies "mshta.exe" execution with inline protocol han #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_inline_hta_execution_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mshta_inline_hta_execution_filter` ``` #### Associated Analytic Story +* Suspicious MSHTA Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -6997,29 +13853,38 @@ To successfully implement this search you need to be ingesting information on pr | T1218.005 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. #### References + * https://github.com/redcanaryco/AtomicTestHarnesses + * https://redcanary.com/blog/introducing-atomictestharnesses/ + * https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + _version_: 5
--- + ### Detect mshta renamed The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1218.005 +- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-20
@@ -7027,15 +13892,24 @@ The following analytic identifies renamed instances of mshta.exe executing. Msht #### Search ``` -`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `detect_mshta_renamed_filter` +`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mshta_renamed_filter` ``` #### Associated Analytic Story +* Suspicious MSHTA Activity + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7043,28 +13917,36 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1218.005 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive. #### References + * https://github.com/redcanaryco/AtomicTestHarnesses + * https://redcanary.com/blog/introducing-atomictestharnesses/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + _version_: 1
--- + ### Detect new API calls from user roles This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.004 +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2018-04-16
@@ -7072,15 +13954,33 @@ This search detects new API calls that have either never been seen before or tha #### Search ``` -`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | table eventName userName] |rename userName as user| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | `detect_new_api_calls_from_user_roles_filter` +`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole +| stats earliest(_time) as earliest latest(_time) as latest by userName eventName +| inputlookup append=t previously_seen_api_calls_from_user_roles +| stats min(earliest) as earliest, max(latest) as latest by userName eventName +| outputlookup previously_seen_api_calls_from_user_roles +| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) +| where newApiCallfromUserRole=1 +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| table eventName userName] +|rename userName as user +| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| `detect_new_api_calls_from_user_roles_filter` ``` #### Associated Analytic Story +* AWS User Monitoring + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7089,23 +13989,27 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. #### References + #### Test Dataset + _version_: 1
--- + ### Detect new user AWS Console Login This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.004 +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-07-21
@@ -7113,15 +14017,28 @@ This search looks for CloudTrail events wherein a console login event by a user #### Search ``` -`cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | stats earliest(_time) as firstTime latest(_time) as lastTime by user | inputlookup append=t previously_seen_users_console_logins_cloudtrail | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| where userStatus ="First Time Logging into AWS Console" | `detect_new_user_aws_console_login_filter` +`cloudtrail` eventName=ConsoleLogin +| rename userIdentity.arn as user +| stats earliest(_time) as firstTime latest(_time) as lastTime by user +| inputlookup append=t previously_seen_users_console_logins_cloudtrail +| stats min(firstTime) as firstTime max(lastTime) as lastTime by user +| eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| where userStatus ="First Time Logging into AWS Console" +| `detect_new_user_aws_console_login_filter` ``` #### Associated Analytic Story +* Suspicious AWS Login Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7129,25 +14046,30 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1078.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. #### References + #### Test Dataset + _version_: 2
--- + ### Detect processes used for System Network Configuration Discovery This search looks for fast execution of processes used for system network configuration discovery on the endpoint. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1016 +- **Data Models**: Endpoint +- **ATT&CK**: [T1016](https://attack.mitre.org/techniques/T1016/) - **Last Updated**: 2020-11-10
@@ -7155,15 +14077,28 @@ This search looks for fast execution of processes used for system network config #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime lastTime dest user process_name process parent_process eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| search `system_network_configuration_discovery_tools` +| transaction dest connected=false maxpause=5m +|where eventcount>=5 +| table firstTime lastTime dest user process_name process parent_process eventcount +| `detect_processes_used_for_system_network_configuration_discovery_filter` ``` #### Associated Analytic Story +* Unusual Processes + + #### How To Implement You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7171,28 +14106,36 @@ You must be ingesting data that records registry activity from your hosts to pop | T1016 | x | x | #### Kill Chain Phases + * Installation + * Command and Control + * Actions on Objectives + #### Known False Positives It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/discovery_commands/windows-sysmon.log + _version_: 2
--- + ### Detect web traffic to dynamic domain providers This search looks for web connections to dynamic DNS providers. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1071.001 +- **Data Models**: Web +- **ATT&CK**: [T1071.001](https://attack.mitre.org/techniques/T1071.001/) - **Last Updated**: 2020-07-21
@@ -7200,10 +14143,18 @@ This search looks for web connections to dynamic DNS providers. #### Search ``` -| tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic` | `detect_web_traffic_to_dynamic_domain_providers_filter` + +| tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `dynamic_dns_web_traffic` +| `detect_web_traffic_to_dynamic_domain_providers_filter` ``` #### Associated Analytic Story +* Dynamic DNS + + #### How To Implement This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ @@ -7211,6 +14162,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7218,26 +14170,32 @@ Detailed documentation on how to create a new field within Incident Review may b | T1071.001 | x | x | #### Kill Chain Phases + * Command and Control + * Actions on Objectives + #### Known False Positives It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. #### References + #### Test Dataset + _version_: 2
--- + ### Detection of DNS Tunnels This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048.003 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) - **Last Updated**: 2017-09-18
@@ -7245,15 +14203,35 @@ This search is used to detect DNS tunneling, by calculating the sum of the lengt #### Search ``` -| tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" | rename "DNS.src" as src "DNS.query" as message | eval length=len(message) | stats sum(length) as length by src | append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" | rename "DNS.src" as src "DNS.answer" as message | eval message=if(message=="unknown","", message) | eval length=len(message) | stats sum(length) as length by src ] | stats sum(length) as length by src | where length > 10000 | `detection_of_dns_tunnels_filter` + +| tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" +| rename "DNS.src" as src "DNS.query" as message +| eval length=len(message) +| stats sum(length) as length by src +| append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" +| rename "DNS.src" as src "DNS.answer" as message +| eval message=if(message=="unknown","", message) +| eval length=len(message) +| stats sum(length) as length by src ] +| stats sum(length) as length by src +| where length > 10000 +| `detection_of_dns_tunnels_filter` ``` #### Associated Analytic Story +* Data Protection + +* Suspicious DNS Traffic + +* Command and Control + + #### How To Implement To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7261,26 +14239,32 @@ To successfully implement this search, we must ensure that DNS data is being ing | T1048.003 | x | x | #### Kill Chain Phases + * Command and Control + * Actions on Objectives + #### Known False Positives It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. #### References + #### Test Dataset + _version_: 2
--- + ### Detection of tools built by NirSoft This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1072 +- **Data Models**: Endpoint +- **ATT&CK**: [T1072](https://attack.mitre.org/techniques/T1072/) - **Last Updated**: 2020-07-21
@@ -7288,15 +14272,24 @@ This search looks for specific command-line arguments that may indicate the exec #### Search ``` -| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter` + +| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detection_of_tools_built_by_nirsoft_filter` ``` #### Associated Analytic Story +* Emotet Malware DHS Report TA18-201A + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7304,26 +14297,32 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1072 | x | x | #### Kill Chain Phases + * Installation + * Actions on Objectives + #### Known False Positives While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. #### References + #### Test Dataset + _version_: 3
--- + ### Disabling Remote User Account Control The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1548.002 +- **ATT&CK**: [T1548.002](https://attack.mitre.org/techniques/T1548.002/) - **Last Updated**: 2020-11-18
@@ -7331,15 +14330,24 @@ The search looks for modifications to registry keys that control the enforcement #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action +| `drop_dm_object_name(Registry)` +| `disabling_remote_user_account_control_filter` ``` #### Associated Analytic Story +* Windows Defense Evasion Tactics + +* Suspicious Windows Registry Activities + + #### How To Implement To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7347,26 +14355,32 @@ To successfully implement this search, you must be ingesting data that records r | T1548.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### Dump LSASS via comsvcs DLL Detect the usage of comsvcs.dll for dumping the lsass process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2020-02-21
@@ -7374,15 +14388,26 @@ Detect the usage of comsvcs.dll for dumping the lsass process. #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_comsvcs_dll_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `dump_lsass_via_comsvcs_dll_filter` ``` #### Associated Analytic Story +* Credential Dumping + +* Suspicious Rundll32 Activity + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7390,29 +14415,37 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1003.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/ + * https://twitter.com/SBousseaden/status/1167417096374050817 + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Dump LSASS via procdump Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\ During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2021-02-01
@@ -7420,15 +14453,24 @@ During triage, confirm this is procdump.exe executing. If it is the first time a #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `dump_lsass_via_procdump_filter` ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7436,30 +14478,39 @@ To successfully implement this search you need to be ingesting information on pr | T1003.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://attack.mitre.org/techniques/T1003/001/ + * https://docs.microsoft.com/en-us/sysinternals/downloads/procdump + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Dump LSASS via procdump Rename Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\ During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1003.001 +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2021-02-01
@@ -7467,15 +14518,24 @@ During triage, confirm this is procdump.exe executing. If it is the first time a #### Search ``` -`sysmon` OriginalFileName=procdump process_name!=procdump*.exe EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_rename_filter` +`sysmon` OriginalFileName=procdump process_name!=procdump*.exe EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `dump_lsass_via_procdump_rename_filter` ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7483,29 +14543,38 @@ To successfully implement this search you need to be ingesting information on pr | T1003.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://attack.mitre.org/techniques/T1003/001/ + * https://docs.microsoft.com/en-us/sysinternals/downloads/procdump + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### EC2 Instance Modified With Previously Unseen User This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.004 +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-07-21
@@ -7513,15 +14582,34 @@ This search looks for EC2 instances being modified by users who have not previou #### Search ``` -`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest | `ec2_instance_modified_with_previously_unseen_user_filter` +`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success +| stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn +| rename userIdentity.arn as arn +| inputlookup append=t previously_seen_ec2_modifications_by_user +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn +| outputlookup previously_seen_ec2_modifications_by_user +| eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newUser=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=dest responseElements.instancesSet.items{}.instanceId +| spath output=user userIdentity.arn +| table _time, user, dest +| `ec2_instance_modified_with_previously_unseen_user_filter` ``` #### Associated Analytic Story +* Unusual AWS EC2 Modifications + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7530,23 +14618,27 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. #### References + #### Test Dataset + _version_: 3
--- + ### EC2 Instance Started In Previously Unseen Region This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1535 +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2018-02-23
@@ -7554,15 +14646,30 @@ This search looks for CloudTrail events where an instance is started in a partic #### Search ``` -`cloudtrail` earliest=-1h StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | inputlookup append=t previously_seen_aws_regions.csv | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where regionStatus="Instance Started in a New Region" | `ec2_instance_started_in_previously_unseen_region_filter` +`cloudtrail` earliest=-1h StartInstances +| stats earliest(_time) as earliest latest(_time) as latest by awsRegion +| inputlookup append=t previously_seen_aws_regions.csv +| stats min(earliest) as earliest max(latest) as latest by awsRegion +| outputlookup previously_seen_aws_regions.csv +| eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| where regionStatus="Instance Started in a New Region" +| `ec2_instance_started_in_previously_unseen_region_filter` ``` #### Associated Analytic Story +* AWS Cryptomining + +* Suspicious AWS EC2 Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7570,19 +14677,24 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- | T1535 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. #### References + #### Test Dataset + _version_: 1
--- + ### EC2 Instance Started With Previously Unseen AMI This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -7596,15 +14708,33 @@ This search looks for EC2 instances being created with previously unseen AMIs. #### Search ``` -`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID | table firstTime, lastTime, arn, amiID, dest, instanceType | `ec2_instance_started_with_previously_unseen_ami_filter` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success +| stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId +| rename requestParameters.instancesSet.items{}.imageId as amiID +| inputlookup append=t previously_seen_ec2_amis.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID +| outputlookup previously_seen_ec2_amis.csv +| eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| where newAMI=1 +| rename amiID as requestParameters.instancesSet.items{}.imageId +| table requestParameters.instancesSet.items{}.imageId] +| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID +| table firstTime, lastTime, arn, amiID, dest, instanceType +| `ec2_instance_started_with_previously_unseen_ami_filter` ``` #### Associated Analytic Story +* AWS Cryptomining + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7612,17 +14742,21 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. #### References + #### Test Dataset + _version_: 1 --- + ### EC2 Instance Started With Previously Unseen Instance Type This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. @@ -7636,15 +14770,35 @@ This search looks for EC2 instances being created with previously unseen instanc #### Search ``` -`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_instance_type_filter` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success +| fillnull value="m1.small" requestParameters.instanceType +| stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType +| rename requestParameters.instanceType as instanceType +| inputlookup append=t previously_seen_ec2_instance_types.csv +| stats min(earliest) as earliest max(latest) as latest by instanceType +| outputlookup previously_seen_ec2_instance_types.csv +| eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| where newType=1 +| rename instanceType as requestParameters.instanceType +| table requestParameters.instanceType] +| spath output=user userIdentity.arn +| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest +| table _time, user, dest, instanceType +| `ec2_instance_started_with_previously_unseen_instance_type_filter` ``` #### Associated Analytic Story +* AWS Cryptomining + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7652,23 +14806,27 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. #### References + #### Test Dataset + _version_: 2 --- + ### EC2 Instance Started With Previously Unseen User This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.004 +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-07-21
@@ -7676,15 +14834,35 @@ This search looks for EC2 instances being created by users who have not created #### Search ``` -`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_user_filter` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success +| stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn +| rename userIdentity.arn as arn +| inputlookup append=t previously_seen_ec2_launches_by_user.csv +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn +| outputlookup previously_seen_ec2_launches_by_user.csv +| eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newUser=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename arn as userIdentity.arn +| table userIdentity.arn] +| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user +| table _time, user, dest, instanceType +| `ec2_instance_started_with_previously_unseen_user_filter` ``` #### Associated Analytic Story +* AWS Cryptomining + +* Suspicious AWS EC2 Activities + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7693,22 +14871,26 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. #### References + #### Test Dataset + _version_: 2
--- + ### Email Attachments With Lots Of Spaces Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Email - **ATT&CK**: - **Last Updated**: 2017-09-19 @@ -7717,10 +14899,23 @@ Attackers often use spaces as a means to obfuscate an attachment's file extensio #### Search ``` -| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address "(?.*)@" | `email_attachments_with_lots_of_spaces_filter` + +| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Email")` +| eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) +| search space_ratio >= 0.1 +| rex field=recipient_address "(?.*)@" +| `email_attachments_with_lots_of_spaces_filter` ``` #### Associated Analytic Story +* Emotet Malware DHS Report TA18-201A + +* Suspicious Emails + + #### How To Implement You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ **Splunk Phantom Playbook Integration**\ @@ -7728,31 +14923,37 @@ If Splunk Phantom is also configured in your environment, a playbook called "Sus #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Delivery + #### Known False Positives None at this time #### References + #### Test Dataset + _version_: 2 --- + ### Email files written outside of the Outlook directory The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1114.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1114.001](https://attack.mitre.org/techniques/T1114.001/) - **Last Updated**: 2020-07-21
@@ -7760,15 +14961,24 @@ The search looks at the change-analysis data model and detects email files creat #### Search ``` -| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_written_outside_of_the_outlook_directory_filter` + +| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest +| `drop_dm_object_name("Filesystem")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `email_files_written_outside_of_the_outlook_directory_filter` ``` #### Associated Analytic Story +* Collection and Staging + + #### How To Implement To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7776,25 +14986,30 @@ To successfully implement this search, you must be ingesting data that records t | T1114.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. #### References + #### Test Dataset + _version_: 3
--- + ### Email servers sending high volume traffic to hosts This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1114.002 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1114.002](https://attack.mitre.org/techniques/T1114.002/) - **Last Updated**: 2020-07-21
@@ -7802,15 +15017,28 @@ This search looks for an increase of data transfers from your email server to yo #### Search ``` -| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter` + +| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d +| `drop_dm_object_name("All_Traffic")` +| eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out +| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip +| eval minimum_data_samples = 4, deviation_threshold = 3 +| where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") +| eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) +| table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average +| `email_servers_sending_high_volume_traffic_to_hosts_filter` ``` #### Associated Analytic Story +* Collection and Staging + + #### How To Implement This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7818,25 +15046,30 @@ This search requires you to be ingesting your network traffic and populating the | T1114.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. #### References + #### Test Dataset + _version_: 2
--- + ### Excessive DNS Failures This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1071.004 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071.004/) - **Last Updated**: 2020-07-21
@@ -7844,15 +15077,31 @@ This search identifies DNS query failures by counting the number of DNS response #### Search ``` -| tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query"| `drop_dm_object_name("DNS")`| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain| where isnull(domain)| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank| where isnull(rank)| stats sum(count) as count mode(queries) as queries by src| `get_asset(src)`| where count>50 | `excessive_dns_failures_filter` + +| tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query" +| `drop_dm_object_name("DNS")` +| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain +| where isnull(domain) +| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank +| where isnull(rank) +| stats sum(count) as count mode(queries) as queries by src +| `get_asset(src)` +| where count>50 +| `excessive_dns_failures_filter` ``` #### Associated Analytic Story +* Suspicious DNS Traffic + +* Command and Control + + #### How To Implement To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7860,25 +15109,30 @@ To successfully implement this search you must ensure that DNS data is populatin | T1071.004 | x | x | #### Kill Chain Phases + * Command and Control + #### Known False Positives It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. #### References + #### Test Dataset + _version_: 2
--- + ### Execution of File With Spaces Before Extension This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1036.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2020-11-19
@@ -7886,15 +15140,24 @@ This search looks for processes launched from files with at least five spaces in #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_spaces_before_extension_filter` + +| tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `execution_of_file_with_spaces_before_extension_filter` ``` #### Associated Analytic Story +* Windows File Extension and Association Abuse + + #### How To Implement To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7902,25 +15165,30 @@ To successfully implement this search, you must be ingesting data that records p | T1036.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + #### Test Dataset + _version_: 3
--- + ### Execution of File with Multiple Extensions This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1036.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2020-11-18
@@ -7928,15 +15196,24 @@ This search looks for processes launched from files that have double extensions #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_multiple_extensions_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `execution_of_file_with_multiple_extensions_filter` ``` #### Associated Analytic Story +* Windows File Extension and Association Abuse + + #### How To Implement To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7944,20 +15221,26 @@ To successfully implement this search, you must be ingesting data that records p | T1036.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + _version_: 3
--- + ### Extended Period Without Successful Netbackup Backups This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. @@ -7971,15 +15254,26 @@ This search returns a list of hosts that have not successfully completed a backu #### Search ``` -`netbackup` MESSAGE="Disk/Partition backup completed successfully." | stats latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest | `extended_period_without_successful_netbackup_backups_filter` +`netbackup` MESSAGE="Disk/Partition backup completed successfully." +| stats latest(_time) as latestTime by COMPUTERNAME +| `security_content_ctime(latestTime)` +| rename COMPUTERNAME as dest +| eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) +| search isOutlier=1 +| table latestTime, dest +| `extended_period_without_successful_netbackup_backups_filter` ``` #### Associated Analytic Story +* Monitor Backup Solution + + #### How To Implement To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -7987,22 +15281,26 @@ To successfully implement this search you need to first obtain data from your ba #### Kill Chain Phases + #### Known False Positives None identified #### References + #### Test Dataset + _version_: 1 --- + ### File with Samsam Extension The search looks for file writes with extensions consistent with a SamSam ransomware attack. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Endpoint - **ATT&CK**: - **Last Updated**: 2018-12-14 @@ -8011,41 +15309,58 @@ The search looks for file writes with extensions consistent with a SamSam ransom #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml | `file_with_samsam_extension_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| rex field=file_name "(?\.[^\.]+)$" +| search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml +| `file_with_samsam_extension_filter` ``` #### Associated Analytic Story +* SamSam Ransomware + + #### How To Implement You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Installation + #### Known False Positives Because these extensions are not typically used in normal operations, you should investigate all results. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/samsam_extension/windows-sysmon.log + _version_: 1 --- + ### First Time Seen Child Process of Zoom This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1068 +- **Data Models**: Endpoint +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) - **Last Updated**: 2020-05-20
@@ -8053,15 +15368,26 @@ This search looks for child processes spawned by zoom.exe or zoom.us that has no #### Search ``` -| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest | `drop_dm_object_name(Processes)` | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter` + +| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest +| `drop_dm_object_name(Processes)` +| lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen +| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") +| `security_content_ctime(firstTime)` +| table firstTime dest, process_id, process_name, parent_process_id, parent_process_name +|`first_time_seen_child_process_of_zoom_filter` ``` #### Associated Analytic Story +* Suspicious Zoom Child Processes + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8069,26 +15395,32 @@ You must be ingesting data that records process activity from your hosts to popu | T1068 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/zoom_child_process/windows-sysmon.log + _version_: 1
--- + ### First Time Seen Running Windows Service This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1569.002 +- **ATT&CK**: [T1569.002](https://attack.mitre.org/techniques/T1569.002/) - **Last Updated**: 2020-07-21
@@ -8096,15 +15428,29 @@ This search looks for the first and last time a Windows service is seen running #### Search ``` -`wineventlog_system` EventCode=7036 | rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | where state="running" | lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) | table _time dest service | `first_time_seen_running_windows_service_filter` +`wineventlog_system` EventCode=7036 +| rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" +| where state="running" +| lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen +| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) +| table _time dest service +| `first_time_seen_running_windows_service_filter` ``` #### Associated Analytic Story +* Windows Service Abuse + +* Orangeworm Attack Group + +* Sunburst Malware + + #### How To Implement While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8112,26 +15458,32 @@ While this search does not require you to adhere to Splunk CIM, you must be inge | T1569.002 | x | x | #### Kill Chain Phases + * Installation + * Actions on Objectives + #### Known False Positives A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. #### References + #### Test Dataset + _version_: 4
--- + ### First time seen command line argument This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1059, T1117, T1202 +- **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/), [T1117](https://attack.mitre.org/techniques/T1117/), [T1202](https://attack.mitre.org/techniques/T1202/) - **Last Updated**: 2021-2-1
@@ -8139,20 +15491,38 @@ This search looks for command-line arguments that use a `/c` parameter to execut #### Search ``` -| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), cmd_line_norm=lower(cmd_line), cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/, "GUID"), cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/, "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N") | where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true | select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id | first_time_event input_columns=["cmd_line_norm"] | where first_time_cmd_line_norm | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), cmd_line_norm=lower(cmd_line), cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/, "GUID"), cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s +|$)+)/, "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s +|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N") +| where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true +| select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id +| first_time_event input_columns=["cmd_line_norm"] +| where first_time_cmd_line_norm +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be populating the endpoint data model for SSA and specifically the process_name and the process fields #### Required fields + * process_name + * _time + * dest_device_id + * dest_user_id + * process + #### ATT&CK | ID | technique | Tactic | @@ -8162,26 +15532,32 @@ You must be populating the endpoint data model for SSA and specifically the proc | T1202 | x | x | #### Kill Chain Phases + * Command and Control + * Actions on Objectives + #### Known False Positives Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name #### References + #### Test Dataset + _version_: 2
--- + ### First time seen command line argument This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001, T1059.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/), [T1059.003](https://attack.mitre.org/techniques/T1059.003/) - **Last Updated**: 2020-07-21
@@ -8189,15 +15565,43 @@ This search looks for command-line arguments that use a `/c` parameter to execut #### Search ``` -| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest| `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table process] | `first_time_seen_command_line_argument_filter` + +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search [ +| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process +| `drop_dm_object_name(Processes)` +| inputlookup append=t previously_seen_cmd_line_arguments +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by process +| outputlookup previously_seen_cmd_line_arguments +| eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newCmdLineArgument=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table process] +| `first_time_seen_command_line_argument_filter` ``` #### Associated Analytic Story +* DHS Report TA18-074A + +* Suspicious Command-Line Executions + +* Orangeworm Attack Group + +* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + +* Hidden Cobra Malware + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8206,26 +15610,32 @@ You must be ingesting data that records process activity from your hosts to popu | T1059.003 | x | x | #### Kill Chain Phases + * Command and Control + * Actions on Objectives + #### Known False Positives Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name #### References + #### Test Dataset + _version_: 5
--- + ### GCP Detect accounts with high risk roles by project This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-10-09
@@ -8233,15 +15643,21 @@ This search provides detection of accounts with high risk roles by projects. Com #### Search ``` -`google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}.role=roles/owner OR roles/editor OR roles/iam.serviceAccountUser OR roles/iam.serviceAccountAdmin OR roles/iam.serviceAccountTokenCreator OR roles/dataflow.developer OR roles/dataflow.admin OR roles/composer.admin OR roles/dataproc.admin OR roles/dataproc.editor | table data.resource.type data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.authorizationInfo{}.resource data.protoPayload.response.bindings{}.role data.protoPayload.response.bindings{}.members{} | `gcp_detect_accounts_with_high_risk_roles_by_project_filter` +`google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}.role=roles/owner OR roles/editor OR roles/iam.serviceAccountUser OR roles/iam.serviceAccountAdmin OR roles/iam.serviceAccountTokenCreator OR roles/dataflow.developer OR roles/dataflow.admin OR roles/composer.admin OR roles/dataproc.admin OR roles/dataproc.editor +| table data.resource.type data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.authorizationInfo{}.resource data.protoPayload.response.bindings{}.role data.protoPayload.response.bindings{}.members{} +| `gcp_detect_accounts_with_high_risk_roles_by_project_filter` ``` #### Associated Analytic Story +* GCP Cross Account Activity + + #### How To Implement You must install splunk GCP add-on. This search works with gcp:pubsub:message logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8249,28 +15665,36 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo | T1078 | x | x | #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Accounts with high risk roles should be reduced to the minimum number needed, however specific tasks and setups may be simply expected behavior within organization #### References + * https://github.com/dxa4481/gcploit + * https://www.youtube.com/watch?v=Ml09R38jpok + * https://cloud.google.com/iam/docs/understanding-roles + #### Test Dataset + _version_: 1
--- + ### GCP Detect gcploit framework This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-10-08
@@ -8278,15 +15702,21 @@ This search provides detection of GCPloit exploitation framework. This framework #### Search ``` -`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s | table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent | `gcp_detect_gcploit_framework_filter` +`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s +| table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent +| `gcp_detect_gcploit_framework_filter` ``` #### Associated Analytic Story +* GCP Cross Account Activity + + #### How To Implement You must install splunk GCP add-on. This search works with gcp:pubsub:message logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8294,27 +15724,34 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo | T1078 | x | x | #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects #### References + * https://github.com/dxa4481/gcploit + * https://www.youtube.com/watch?v=Ml09R38jpok + #### Test Dataset + _version_: 1
--- + ### GCP Detect high risk permissions by resource and account This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-10-09
@@ -8322,15 +15759,21 @@ This search provides detection of high risk permissions by resource and accounts #### Search ``` -`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.permission=iam.serviceAccounts.getaccesstoken OR iam.serviceAccounts.setIamPolicy OR iam.serviceAccounts.actas OR dataflow.jobs.create OR composer.environments.create OR dataproc.clusters.create |table data.protoPayload.requestMetadata.callerIp data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.response.bindings{}.members{} data.resource.labels.project_id | `gcp_detect_high_risk_permissions_by_resource_and_account_filter` +`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.permission=iam.serviceAccounts.getaccesstoken OR iam.serviceAccounts.setIamPolicy OR iam.serviceAccounts.actas OR dataflow.jobs.create OR composer.environments.create OR dataproc.clusters.create +|table data.protoPayload.requestMetadata.callerIp data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.response.bindings{}.members{} data.resource.labels.project_id +| `gcp_detect_high_risk_permissions_by_resource_and_account_filter` ``` #### Associated Analytic Story +* GCP Cross Account Activity + + #### How To Implement You must install splunk GCP add-on. This search works with gcp:pubsub:message logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8338,28 +15781,36 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo | T1078 | x | x | #### Kill Chain Phases + * Lateral Movement + #### Known False Positives High risk permissions are part of any GCP environment, however it is important to track resource and accounts usage, this search may produce false positives. #### References + * https://github.com/dxa4481/gcploit + * https://www.youtube.com/watch?v=Ml09R38jpok + * https://cloud.google.com/iam/docs/permissions-reference + #### Test Dataset + _version_: 1
--- + ### GCP GCR container uploaded This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1525 +- **ATT&CK**: [T1525](https://attack.mitre.org/techniques/T1525/) - **Last Updated**: 2020-02-20
@@ -8367,15 +15818,22 @@ This search show information on uploaded containers including source user, accou #### Search ``` -|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path | `drop_dm_object_name("Storage")` | `gcp_gcr_container_uploaded_filter` + +|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path +| `drop_dm_object_name("Storage")` +| `gcp_gcr_container_uploaded_filter` ``` #### Associated Analytic Story +* Container Implantation Monitoring and Investigation + + #### How To Implement You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8384,23 +15842,27 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure #### Kill Chain Phases + #### Known False Positives Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. #### References + #### Test Dataset + _version_: 1
--- + ### GCP Kubernetes cluster pod scan detection This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1526 +- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) - **Last Updated**: 2020-07-17
@@ -8408,15 +15870,23 @@ This search provides information of unauthenticated requests via user agent, and #### Search ``` -`google_gcp_pubsub_message` category=kube-audit |spath input=properties.log |search responseStatus.code=401 |table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod | `gcp_kubernetes_cluster_pod_scan_detection_filter` +`google_gcp_pubsub_message` category=kube-audit +|spath input=properties.log +|search responseStatus.code=401 +|table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod +| `gcp_kubernetes_cluster_pod_scan_detection_filter` ``` #### Associated Analytic Story +* Kubernetes Scanning Activity + + #### How To Implement You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8424,25 +15894,30 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure | T1526 | x | x | #### Kill Chain Phases + * Reconnaissance + #### Known False Positives Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context. #### References + #### Test Dataset + _version_: 1
--- + ### GCP Kubernetes cluster scan detection This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1526 +- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) - **Last Updated**: 2020-04-15
@@ -8450,15 +15925,25 @@ This search provides information of unauthenticated requests via user agent, and #### Search ``` -`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `gcp_kubernetes_cluster_scan_detection_filter` +`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" +| rename data.protoPayload.requestMetadata.callerIp as src_ip +| stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name +| rename data.resource.labels.cluster_name as cluster_name +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `gcp_kubernetes_cluster_scan_detection_filter` ``` #### Associated Analytic Story +* Kubernetes Scanning Activity + + #### How To Implement You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8466,25 +15951,30 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure | T1526 | x | x | #### Kill Chain Phases + * Reconnaissance + #### Known False Positives Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. #### References + #### Test Dataset + _version_: 1
--- + ### Hiding Files And Directories With Attrib exe Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1222.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1222.001](https://attack.mitre.org/techniques/T1222.001/) - **Last Updated**: 2020-07-21
@@ -8492,15 +15982,26 @@ Attackers leverage an existing Windows binary, attrib.exe, to mark specific as h #### Search ``` -| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `hiding_files_and_directories_with_attrib_exe_filter` + +| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `hiding_files_and_directories_with_attrib_exe_filter` ``` #### Associated Analytic Story +* Windows Defense Evasion Tactics + +* Windows Persistence Techniques + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8508,26 +16009,32 @@ You must be ingesting data that records process activity from your hosts to popu | T1222.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Some applications and users may legitimately use attrib.exe to interact with the files. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### High Number of Login Failures from a single source This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1110.001 +- **ATT&CK**: [T1110.001](https://attack.mitre.org/techniques/T1110.001/) - **Last Updated**: 2020-12-16
@@ -8535,15 +16042,22 @@ This search will detect more than 5 login failures in Office365 Azure Active Dir #### Search ``` -`o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory | stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app | search accounts_locked >= 5| `high_number_of_login_failures_from_a_single_source_filter` +`o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory +| stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app +| search accounts_locked >= 5 +| `high_number_of_login_failures_from_a_single_source_filter` ``` #### Associated Analytic Story +* Office 365 Detections + + #### How To Implement #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8551,25 +16065,30 @@ This search will detect more than 5 login failures in Office365 Azure Active Dir | T1110.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives unknown #### References + #### Test Dataset + _version_: 1
--- + ### Hosts receiving high volume of network traffic from email server This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1114.002 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1114.002](https://attack.mitre.org/techniques/T1114.002/) - **Last Updated**: 2020-07-21
@@ -8577,15 +16096,28 @@ This search looks for an increase of data transfers from your email server to yo #### Search ``` -| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` + +| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d +| `drop_dm_object_name("All_Traffic")` +| eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in +| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip +| eval minimum_data_samples = 4, deviation_threshold = 3 +| where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") +| eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) +| table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average +| `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` ``` #### Associated Analytic Story +* Collection and Staging + + #### How To Implement This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8593,25 +16125,30 @@ This search requires you to be ingesting your network traffic and populating the | T1114.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. #### References + #### Test Dataset + _version_: 2
--- + ### Identify New User Accounts This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.002 +- **ATT&CK**: [T1078.002](https://attack.mitre.org/techniques/T1078.002/) - **Last Updated**: 2017-09-12
@@ -8619,15 +16156,26 @@ This detection search will help profile user accounts in your environment by ide #### Search ``` -| from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity empStatus endDate startDate | `identify_new_user_accounts_filter` + +| from datamodel Identity_Management.All_Identities +| eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") +| search empStatus="Accounts created in last week" +| `security_content_ctime(endDate)` +| `security_content_ctime(startDate)` +| table identity empStatus endDate startDate +| `identify_new_user_accounts_filter` ``` #### Associated Analytic Story +* Account Monitoring and Controls + + #### How To Implement To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -8636,23 +16184,27 @@ To successfully implement this search, you need to be populating the Enterprise #### Kill Chain Phases + #### Known False Positives If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. #### References + #### Test Dataset + _version_: 1
--- + ### Illegal Access To User Content via PowerSploit modules This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1021, T1113, T1123, T1563 +- **ATT&CK**: [T1021](https://attack.mitre.org/techniques/T1021/), [T1113](https://attack.mitre.org/techniques/T1113/), [T1123](https://attack.mitre.org/techniques/T1123/), [T1563](https://attack.mitre.org/techniques/T1563/) - **Last Updated**: 2020-11-09
@@ -8660,21 +16212,32 @@ This detection identifies access to PowerSploit modules that enable illegaly acc #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-HttpStatus/)=true OR match_regex(cmd_line, /(?i)Get-Keystrokes/)=true OR match_regex(cmd_line, /(?i)Get-MicrophoneAudio/)=true OR match_regex(cmd_line, /(?i)Get-NetRDPSession/)=true OR match_regex(cmd_line, /(?i)Get-TimedScreenshot/)=true OR match_regex(cmd_line, /(?i)Get-WebConfig/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-HttpStatus/)=true OR match_regex(cmd_line, /(?i)Get-Keystrokes/)=true OR match_regex(cmd_line, /(?i)Get-MicrophoneAudio/)=true OR match_regex(cmd_line, /(?i)Get-NetRDPSession/)=true OR match_regex(cmd_line, /(?i)Get-TimedScreenshot/)=true OR match_regex(cmd_line, /(?i)Get-WebConfig/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -8685,26 +16248,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1563 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Illegal Account Creation via PowerSploit modules This detection identifies access to PowerSploit modules that create accounts illegaly. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1585 +- **ATT&CK**: [T1585](https://attack.mitre.org/techniques/T1585/) - **Last Updated**: 2020-11-09
@@ -8712,21 +16281,32 @@ This detection identifies access to PowerSploit modules that create accounts ill #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)New-DomainUser/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)New-DomainUser/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -8734,26 +16314,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1585 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Illegal Deletion of Logs via Mimikatz modules This detection identifies access to PowerSploit modules that delete event logs. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1070 +- **ATT&CK**: [T1070](https://attack.mitre.org/techniques/T1070/) - **Last Updated**: 2020-11-09
@@ -8761,21 +16347,32 @@ This detection identifies access to PowerSploit modules that delete event logs. #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)event::drop/)=true OR match_regex(cmd_line, /(?i)event::clear/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)event::drop/)=true OR match_regex(cmd_line, /(?i)event::clear/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -8783,26 +16380,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1070 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + #### Test Dataset + _version_: 1
--- + ### Illegal Enabling or Disabling of Accounts via DSInternals modules This detection identifies use of DSInternals modules that enable or disable accounts illegaly. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1078, T1098 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-09
@@ -8810,21 +16413,32 @@ This detection identifies use of DSInternals modules that enable or disable acco #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Disable-ADDBAccount/)=true OR match_regex(cmd_line, /(?i)Enable-ADDBAccount/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Disable-ADDBAccount/)=true OR match_regex(cmd_line, /(?i)Enable-ADDBAccount/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -8833,26 +16447,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1098 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/MichaelGrafnetter/DSInternals + #### Test Dataset + _version_: 1
--- + ### Illegal Management of Active Directory Elements and Policies via DSInternals modules This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1098, T1207, T1484 +- **ATT&CK**: [T1098](https://attack.mitre.org/techniques/T1098/), [T1207](https://attack.mitre.org/techniques/T1207/), [T1484](https://attack.mitre.org/techniques/T1484/) - **Last Updated**: 2020-11-09
@@ -8860,21 +16480,32 @@ This detection identifies use of DSInternals modules for illegal management of A #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Remove-ADDBObject/)=true OR match_regex(cmd_line, /(?i)Set-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Set-ADDBPrimaryGroup/)=true OR match_regex(cmd_line, /(?i)Set-LsaPolicyInformation/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Remove-ADDBObject/)=true OR match_regex(cmd_line, /(?i)Set-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Set-ADDBPrimaryGroup/)=true OR match_regex(cmd_line, /(?i)Set-LsaPolicyInformation/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -8884,26 +16515,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1484 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/MichaelGrafnetter/DSInternals + #### Test Dataset + _version_: 1
--- + ### Illegal Management of Computers and Active Directory Elements via PowerSploit modules This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1098, T1207, T1484 +- **ATT&CK**: [T1098](https://attack.mitre.org/techniques/T1098/), [T1207](https://attack.mitre.org/techniques/T1207/), [T1484](https://attack.mitre.org/techniques/T1484/) - **Last Updated**: 2020-11-09
@@ -8911,22 +16548,33 @@ This detection identifies access to PowerSploit modules that enable illegal mana #### Search ``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainObject/)=true OR match_regex(cmd_line, /(?i)Set-ADObject/)=true OR match_regex(cmd_line, /(?i)Set-DomainObjectOwner/)=true OR match_regex(cmd_line, /(?i)Set-MasterBootRecord/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainObject/)=true OR match_regex(cmd_line, /(?i)Set-ADObject/)=true OR match_regex(cmd_line, /(?i)Set-DomainObjectOwner/)=true OR match_regex(cmd_line, /(?i)Set-MasterBootRecord/)=true ) + + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -8936,26 +16584,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1484 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Illegal Privilege Elevation and Persistence via PowerSploit modules This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1053, T1134, T1548 +- **ATT&CK**: [T1053](https://attack.mitre.org/techniques/T1053/), [T1134](https://attack.mitre.org/techniques/T1134/), [T1548](https://attack.mitre.org/techniques/T1548/) - **Last Updated**: 2020-11-09
@@ -8963,21 +16617,32 @@ This detection identifies access to PowerSploit modules that illegaly elevate ge #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Add-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Enable-Privilege/)=true OR match_regex(cmd_line, /(?i)New-ElevatedPersistenceOption/)=true OR match_regex(cmd_line, /(?i)New-UserPersistenceOption/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Add-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Enable-Privilege/)=true OR match_regex(cmd_line, /(?i)New-ElevatedPersistenceOption/)=true OR match_regex(cmd_line, /(?i)New-UserPersistenceOption/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -8987,26 +16652,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1548 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Illegal Privilege Elevation via Mimikatz modules This detection identifies use of Mimikatz modules for illegal privilege elevation. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1134, T1548 +- **ATT&CK**: [T1134](https://attack.mitre.org/techniques/T1134/), [T1548](https://attack.mitre.org/techniques/T1548/) - **Last Updated**: 2020-11-09
@@ -9014,21 +16685,32 @@ This detection identifies use of Mimikatz modules for illegal privilege elevatio #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)privilege::debug/)=true OR match_regex(cmd_line, /(?i)token::elevate/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)privilege::debug/)=true OR match_regex(cmd_line, /(?i)token::elevate/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -9037,26 +16719,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1548 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + #### Test Dataset + _version_: 1
--- + ### Illegal Service and Process Control via Mimikatz modules This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1055, T1106, T1569 +- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1106](https://attack.mitre.org/techniques/T1106/), [T1569](https://attack.mitre.org/techniques/T1569/) - **Last Updated**: 2020-11-09
@@ -9064,21 +16752,32 @@ This detection identifies use of Mimikatz modules for illegal control over servi #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::start/)=true OR match_regex(cmd_line, /(?i)service::\+/)=true OR match_regex(cmd_line, /(?i)service::\-/)=true OR match_regex(cmd_line, /(?i)service::start/)=true OR match_regex(cmd_line, /(?i)service::stop/)=true OR match_regex(cmd_line, /(?i)service::suspend/)=true OR match_regex(cmd_line, /(?i)misc::memssp/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::start/)=true OR match_regex(cmd_line, /(?i)service::\+/)=true OR match_regex(cmd_line, /(?i)service::\-/)=true OR match_regex(cmd_line, /(?i)service::start/)=true OR match_regex(cmd_line, /(?i)service::stop/)=true OR match_regex(cmd_line, /(?i)service::suspend/)=true OR match_regex(cmd_line, /(?i)misc::memssp/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -9088,26 +16787,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1569 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + #### Test Dataset + _version_: 1
--- + ### Illegal Service and Process Control via PowerSploit modules This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1055, T1106, T1569 +- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1106](https://attack.mitre.org/techniques/T1106/), [T1569](https://attack.mitre.org/techniques/T1569/) - **Last Updated**: 2020-11-09
@@ -9115,22 +16820,33 @@ This detection identifies access to PowerSploit modules that enable illegal cont #### Search ``` -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Install-SSP/)=true OR match_regex(cmd_line, /(?i)Set-CriticalProcess/)=true OR match_regex(cmd_line, /(?i)Install-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Restore-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Write-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Set-ServiceBinaryPath/)=true OR match_regex(cmd_line, /(?i)Invoke-ReflectivePEInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-DllInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-ServiceAbuse/)=true OR match_regex(cmd_line, /(?i)Invoke-Shellcode/)=true OR match_regex(cmd_line, /(?i)Invoke-WScriptUACBypass/)=true OR match_regex(cmd_line, /(?i)Invoke-WmiCommand/)=true OR match_regex(cmd_line, /(?i)Write-HijackDll/)=true OR match_regex(cmd_line, /(?i)Add-ServiceDacl/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Install-SSP/)=true OR match_regex(cmd_line, /(?i)Set-CriticalProcess/)=true OR match_regex(cmd_line, /(?i)Install-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Restore-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Write-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Set-ServiceBinaryPath/)=true OR match_regex(cmd_line, /(?i)Invoke-ReflectivePEInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-DllInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-ServiceAbuse/)=true OR match_regex(cmd_line, /(?i)Invoke-Shellcode/)=true OR match_regex(cmd_line, /(?i)Invoke-WScriptUACBypass/)=true OR match_regex(cmd_line, /(?i)Invoke-WmiCommand/)=true OR match_regex(cmd_line, /(?i)Write-HijackDll/)=true OR match_regex(cmd_line, /(?i)Add-ServiceDacl/)=true ) + + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -9140,26 +16856,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1569 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Kerberoasting spn request with RC4 encryption This search detects a potential kerberoasting attack via service principal name requests - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1558.003 +- **ATT&CK**: [T1558.003](https://attack.mitre.org/techniques/T1558.003/) - **Last Updated**: 2020-10-16
@@ -9167,15 +16889,23 @@ This search detects a potential kerberoasting attack via service principal name #### Search ``` -`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 | stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kerberoasting_spn_request_with_rc4_encryption_filter` +`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 +| stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `kerberoasting_spn_request_with_rc4_encryption_filter` ``` #### Associated Analytic Story +* Lateral Movement + + #### How To Implement You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -9183,22 +16913,30 @@ You must be ingesting endpoint data that tracks process activity, and include th | T1558.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Older systems that support kerberos RC4 by default NetApp may generate false positives #### References + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md + * https://www.trimarcsecurity.com/post/trimarcresearch-detecting-kerberoasting-activity + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-security.log + _version_: 3
--- + ### Kubernetes AWS detect RBAC authorization by account This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences @@ -9212,34 +16950,47 @@ This search provides information on Kubernetes RBAC authorizations by accounts, #### Search ``` -`aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason | stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_aws_detect_rbac_authorization_by_account_filter` +`aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* +| table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason +| stats count by user.username annotations.authorization.k8s.io/reason +| rare user.username annotations.authorization.k8s.io/reason +|`kubernetes_aws_detect_rbac_authorization_by_account_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Role Activity + + #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes AWS detect most active service accounts by pod This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision @@ -9253,34 +17004,46 @@ This search provides information on Kubernetes service accounts,accessing pods b #### Search ``` -`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts objectRef.resource=pods | table sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision | top sourceIPs{} user.username verb annotations.authorization.k8s.io/decision |`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter` +`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts objectRef.resource=pods +| table sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision +| top sourceIPs{} user.username verb annotations.authorization.k8s.io/decision +|`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Role Activity + + #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes AWS detect sensitive role access This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets @@ -9294,34 +17057,46 @@ This search provides information on Kubernetes accounts accessing sensitve objec #### Search ``` -`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_aws_detect_sensitive_role_access_filter` +`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 +| table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason +| dedup user.username user.groups{} +|`kubernetes_aws_detect_sensitive_role_access_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Role Activity + + #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes AWS detect service accounts forbidden failure access This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI @@ -9335,34 +17110,45 @@ This search provides information on Kubernetes service accounts with failure or #### Search ``` -`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure | table sourceIPs{} user.username userAgent verb responseStatus.status requestURI | `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter` +`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure +| table sourceIPs{} user.username userAgent verb responseStatus.status requestURI +| `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Object Access Activity + + #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives This search can give false positives as there might be inherent issues with authentications and permissions at cluster. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes AWS detect suspicious kubectl calls This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context @@ -9376,34 +17162,46 @@ This search provides information on anonymous Kubectl calls with IP, verb namesp #### Search ``` -`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous | table src_ip src_user verb userAgent requestURI | stats count by src_ip src_user verb userAgent requestURI |`kubernetes_aws_detect_suspicious_kubectl_calls_filter` +`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous +| table src_ip src_user verb userAgent requestURI +| stats count by src_ip src_user verb userAgent requestURI +|`kubernetes_aws_detect_suspicious_kubectl_calls_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Object Access Activity + + #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes Azure detect RBAC authorization by account This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences @@ -9417,34 +17215,49 @@ This search provides information on Kubernetes RBAC authorizations by accounts, #### Search ``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | search annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason |stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_azure_detect_rbac_authorization_by_account_filter` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search annotations.authorization.k8s.io/reason=* +| table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason +|stats count by user.username annotations.authorization.k8s.io/reason +| rare user.username annotations.authorization.k8s.io/reason +|`kubernetes_azure_detect_rbac_authorization_by_account_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Role Activity + + #### How To Implement You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes Azure detect most active service accounts by pod namespace This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb @@ -9458,34 +17271,48 @@ This search provides information on Kubernetes service accounts,accessing pods a #### Search ``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace | top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow +| table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace +| top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace +|`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Role Activity + + #### How To Implement You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes Azure detect sensitive object access This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets @@ -9499,34 +17326,48 @@ This search provides information on Kubernetes accounts accessing sensitve objec #### Search ``` -`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_object_access_filter` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow +|table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason +|dedup user.username user.groups{} +|`kubernetes_azure_detect_sensitive_object_access_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Object Access Activity + + #### How To Implement You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes Azure detect sensitive role access This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets @@ -9540,34 +17381,48 @@ This search provides information on Kubernetes accounts accessing sensitve objec #### Search ``` -`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=clusterroles OR clusterrolebindings | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access_filter` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search objectRef.resource=clusterroles OR clusterrolebindings +| table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason +| dedup user.username user.groups{} +|`kubernetes_azure_detect_sensitive_role_access_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Role Activity + + #### How To Implement You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes Azure detect service accounts forbidden failure access This search provides information on Kubernetes service accounts with failure or forbidden access status @@ -9581,34 +17436,47 @@ This search provides information on Kubernetes service accounts with failure or #### Search ``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden +| table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace +|`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Object Access Activity + + #### How To Implement You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives This search can give false positives as there might be inherent issues with authentications and permissions at cluster. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes Azure detect suspicious kubectl calls This search provides information on rare Kubectl calls with IP, verb namespace and object access context @@ -9622,34 +17490,49 @@ This search provides information on rare Kubectl calls with IP, verb namespace a #### Search ``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration | search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 | table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI | rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI |`kubernetes_azure_detect_suspicious_kubectl_calls_filter` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration +| search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 +| table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI +| rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI +|`kubernetes_azure_detect_suspicious_kubectl_calls_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Object Access Activity + + #### How To Implement You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes Azure pod scan fingerprint This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure @@ -9663,40 +17546,53 @@ This search provides information of unauthenticated requests via source IP user #### Search ``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod |`kubernetes_azure_pod_scan_fingerprint_filter` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search responseStatus.code=401 +| table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod +|`kubernetes_azure_pod_scan_fingerprint_filter` ``` #### Associated Analytic Story +* Kubernetes Scanning Activity + + #### How To Implement You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Reconnaissance + #### Known False Positives Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes Azure scan fingerprint This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1526 +- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) - **Last Updated**: 2020-05-19
@@ -9704,15 +17600,23 @@ This search provides information of unauthenticated requests via source IP user #### Search ``` -`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason |`kubernetes_azure_scan_fingerprint_filter` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search responseStatus.code=401 +| table sourceIPs{} userAgent verb requestURI responseStatus.reason +|`kubernetes_azure_scan_fingerprint_filter` ``` #### Associated Analytic Story +* Kubernetes Scanning Activity + + #### How To Implement You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -9720,19 +17624,24 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi | T1526 | x | x | #### Kill Chain Phases + * Reconnaissance + #### Known False Positives Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. #### References + #### Test Dataset + _version_: 1
--- + ### Kubernetes GCP detect RBAC authorizations by account This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences @@ -9746,34 +17655,46 @@ This search provides information on Kubernetes RBAC authorizations by accounts, #### Search ``` -`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole | table src_ip src_user data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason | rare src_user data.labels.authorization.k8s.io/reason |`kubernetes_gcp_detect_rbac_authorizations_by_account_filter` +`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole +| table src_ip src_user data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason +| rare src_user data.labels.authorization.k8s.io/reason +|`kubernetes_gcp_detect_rbac_authorizations_by_account_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Role Activity + + #### How To Implement You must install splunk AWS add on for GCP. This search works with pubsub messaging service logs #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes GCP detect most active service accounts by pod This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision @@ -9787,34 +17708,46 @@ This search provides information on Kubernetes service accounts,accessing pods b #### Search ``` -`google_gcp_pubsub_message` data.protoPayload.request.spec.group{}=system:serviceaccounts | table src_ip src_user http_user_agent data.protoPayload.request.spec.nonResourceAttributes.verb data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource | top src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource |`kubernetes_gcp_detect_most_active_service_accounts_by_pod_filter` +`google_gcp_pubsub_message` data.protoPayload.request.spec.group{}=system:serviceaccounts +| table src_ip src_user http_user_agent data.protoPayload.request.spec.nonResourceAttributes.verb data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource +| top src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource +|`kubernetes_gcp_detect_most_active_service_accounts_by_pod_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Role Activity + + #### How To Implement You must install splunk GCP add on. This search works with pubsub messaging service logs #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes GCP detect sensitive object access This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets @@ -9828,34 +17761,46 @@ This search provides information on Kubernetes accounts accessing sensitve objec #### Search ``` -`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.resource=configmaps OR secrets | table data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name data.protoPayload.request.metadata.namespace data.labels.authorization.k8s.io/decision | dedup data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name |`kubernetes_gcp_detect_sensitive_object_access_filter` +`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.resource=configmaps OR secrets +| table data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name data.protoPayload.request.metadata.namespace data.labels.authorization.k8s.io/decision +| dedup data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name +|`kubernetes_gcp_detect_sensitive_object_access_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Object Access Activity + + #### How To Implement You must install splunk add on for GCP . This search works with pubsub messaging service logs. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes GCP detect sensitive role access This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets @@ -9869,34 +17814,46 @@ This search provides information on Kubernetes accounts accessing sensitve objec #### Search ``` -`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole dest=apis/rbac.authorization.k8s.io/v1 src_ip!=::1 | table src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason | dedup src_ip src_user |`kubernetes_gcp_detect_sensitive_role_access_filter` +`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole dest=apis/rbac.authorization.k8s.io/v1 src_ip!=::1 +| table src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason +| dedup src_ip src_user +|`kubernetes_gcp_detect_sensitive_role_access_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Role Activity + + #### How To Implement You must install splunk add on for GCP. This search works with pubsub messaging servicelogs. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Sensitive role resource access is necessary for cluster operation, however source IP, user agent, decision and reason may indicate possible malicious use. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes GCP detect service accounts forbidden failure access This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI @@ -9910,34 +17867,46 @@ This search provides information on Kubernetes service accounts with failure or #### Search ``` -`google_gcp_pubsub_message` system:serviceaccounts data.protoPayload.response.status.allowed!=* | table src_ip src_user http_user_agent data.protoPayload.response.spec.resourceAttributes.namespace data.resource.labels.cluster_name data.protoPayload.response.spec.resourceAttributes.verb data.protoPayload.request.status.allowed data.protoPayload.response.status.reason data.labels.authorization.k8s.io/decision | dedup src_ip src_user | `kubernetes_gcp_detect_service_accounts_forbidden_failure_access_filter` +`google_gcp_pubsub_message` system:serviceaccounts data.protoPayload.response.status.allowed!=* +| table src_ip src_user http_user_agent data.protoPayload.response.spec.resourceAttributes.namespace data.resource.labels.cluster_name data.protoPayload.response.spec.resourceAttributes.verb data.protoPayload.request.status.allowed data.protoPayload.response.status.reason data.labels.authorization.k8s.io/decision +| dedup src_ip src_user +| `kubernetes_gcp_detect_service_accounts_forbidden_failure_access_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Object Access Activity + + #### How To Implement You must install splunk add on for GCP. This search works with pubsub messaging service logs. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives This search can give false positives as there might be inherent issues with authentications and permissions at cluster. #### References + #### Test Dataset + _version_: 1 --- + ### Kubernetes GCP detect suspicious kubectl calls This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context @@ -9951,40 +17920,52 @@ This search provides information on anonymous Kubectl calls with IP, verb namesp #### Search ``` -`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous | table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path |dedup src_ip src_user |`kubernetes_gcp_detect_suspicious_kubectl_calls_filter` +`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous +| table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path +|dedup src_ip src_user +|`kubernetes_gcp_detect_suspicious_kubectl_calls_filter` ``` #### Associated Analytic Story +* Kubernetes Sensitive Object Access Activity + + #### How To Implement You must install splunk add on for GCP. This search works with pubsub messaging logs. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets #### References + #### Test Dataset + _version_: 1 --- + ### Large Volume of DNS ANY Queries The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1498.002 +- **Data Models**: Network_Resolution +- **ATT&CK**: [T1498.002](https://attack.mitre.org/techniques/T1498.002/) - **Last Updated**: 2017-09-20
@@ -9992,15 +17973,23 @@ The search is used to identify attempts to use your DNS Infrastructure for DDoS #### Search ``` -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter` + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" +| `drop_dm_object_name("DNS")` +| where count>200 +| `large_volume_of_dns_any_queries_filter` ``` #### Associated Analytic Story +* DNS Amplification Attacks + + #### How To Implement To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10008,24 +17997,29 @@ To successfully implement this search you must ensure that DNS data is populatin | T1498.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. #### References + #### Test Dataset + _version_: 1
--- + ### MacOS - Re-opened Applications This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Endpoint - **ATT&CK**: - **Last Updated**: 2020-02-07 @@ -10034,41 +18028,54 @@ This search looks for processes referencing the plist files that determine which #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `macos___re_opened_applications_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `macos___re_opened_applications_filter` ``` #### Associated Analytic Story + #### How To Implement In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Installation + * Command and Control + #### Known False Positives At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list. #### References + #### Test Dataset + _version_: 1 --- + ### Malicious PowerShell Process - Connect To Internet With Hidden Window This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/) - **Last Updated**: 2020-11-20
@@ -10076,15 +18083,26 @@ This search looks for PowerShell processes started with parameters to modify the #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` ``` #### Associated Analytic Story +* Malicious PowerShell + +* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10092,27 +18110,34 @@ You must be ingesting data that records process activity from your hosts to popu | T1059.001 | x | x | #### Kill Chain Phases + * Command and Control + * Actions on Objectives + #### Known False Positives Legitimate process can have this combination of command-line options, but it's not common. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/hidden_powershell/windows-sysmon.log + _version_: 5
--- + ### Malicious PowerShell Process - Encoded Command This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1027 +- **Data Models**: Endpoint +- **ATT&CK**: [T1027](https://attack.mitre.org/techniques/T1027/) - **Last Updated**: 2020-07-21
@@ -10120,15 +18145,26 @@ This search looks for PowerShell processes that have encoded the script within t #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `malicious_powershell_process___encoded_command_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `malicious_powershell_process___encoded_command_filter` ``` #### Associated Analytic Story +* Malicious PowerShell + +* Sunburst Malware + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10136,27 +18172,34 @@ You must be ingesting data that records process activity from your hosts to popu | T1027 | x | x | #### Kill Chain Phases + * Command and Control + * Actions on Objectives + #### Known False Positives System administrators may use this option, but it's not common. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### Malicious PowerShell Process - Execution Policy Bypass This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/) - **Last Updated**: 2020-07-21
@@ -10164,15 +18207,24 @@ This search looks for PowerShell processes started with parameters used to bypas #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter` + +| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `malicious_powershell_process___execution_policy_bypass_filter` ``` #### Associated Analytic Story +* DHS Report TA18-074A + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10180,27 +18232,34 @@ You must be ingesting data that records process activity from your hosts to popu | T1059.001 | x | x | #### Kill Chain Phases + * Command and Control + * Actions on Objectives + #### Known False Positives There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/windows-sysmon.log + _version_: 4
--- + ### Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/) - **Last Updated**: 2021-01-19
@@ -10208,15 +18267,25 @@ This search looks for PowerShell processes started with a base64 encoded command #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* | `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* +| `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter` ``` #### Associated Analytic Story +* Malicious PowerShell + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10224,26 +18293,32 @@ You must be ingesting data that records process activity from your hosts to popu | T1059.001 | x | x | #### Kill Chain Phases + * Command and Control + * Actions on Objectives + #### Known False Positives Legitimate process can have this combination of command-line options, but it's not common. #### References + #### Test Dataset + _version_: 6
--- + ### Malicious PowerShell Process With Obfuscation Techniques This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1059.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/) - **Last Updated**: 2021-01-19
@@ -10251,15 +18326,26 @@ This search looks for PowerShell processes launched with arguments that have cha #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) | `malicious_powershell_process_with_obfuscation_techniques_filter` | search num_obfuscation > 10 + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) +| `malicious_powershell_process_with_obfuscation_techniques_filter` +| search num_obfuscation > 10 ``` #### Associated Analytic Story +* Malicious PowerShell + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10267,26 +18353,33 @@ You must be ingesting data that records process activity from your hosts to popu | T1059.001 | x | x | #### Kill Chain Phases + * Command and Control + * Actions on Objectives + #### Known False Positives These characters might be legitimately on the command-line, but it is not common. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/obfuscated_powershell/windows-sysmon.log + _version_: 4
--- + ### Monitor DNS For Brand Abuse This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Network_Resolution - **ATT&CK**: - **Last Updated**: 2017-09-23 @@ -10295,40 +18388,55 @@ This search looks for DNS requests for faux domains similar to the domains that #### Search ``` -| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)`| `brand_abuse_dns` | `monitor_dns_for_brand_abuse_filter` + +| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query +| `drop_dm_object_name("DNS")` +| `security_content_ctime(firstTime)` +| `brand_abuse_dns` +| `monitor_dns_for_brand_abuse_filter` ``` #### Associated Analytic Story +* Brand Monitoring + + #### How To Implement You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Delivery + * Actions on Objectives + #### Known False Positives None at this time #### References + #### Test Dataset + _version_: 1 --- + ### Monitor Email For Brand Abuse This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Email - **ATT&CK**: - **Last Updated**: 2018-01-05 @@ -10337,40 +18445,61 @@ This search looks for emails claiming to be sent from a domain similar to one th #### Search ``` -| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter` + +| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id +| `drop_dm_object_name("All_Email")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval temp=split(src_user, "@") +| eval email_domain=mvindex(temp, 1) +| lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse +| search domain_abuse=true +| table message_id, src_user, email_domain, recipients, firstTime, lastTime +| `monitor_email_for_brand_abuse_filter` ``` #### Associated Analytic Story +* Brand Monitoring + +* Suspicious Emails + + #### How To Implement You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Delivery + #### Known False Positives None at this time #### References + #### Test Dataset + _version_: 2 --- + ### Monitor Registry Keys for Print Monitors This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1547.010 +- **ATT&CK**: [T1547.010](https://attack.mitre.org/techniques/T1547.010/) - **Last Updated**: 2020-11-23
@@ -10378,15 +18507,24 @@ This search looks for registry activity associated with modifications to the reg #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `monitor_registry_keys_for_print_monitors_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action +| `drop_dm_object_name(Registry)` +| `monitor_registry_keys_for_print_monitors_filter` ``` #### Associated Analytic Story +* Suspicious Windows Registry Activities + +* Windows Persistence Techniques + + #### How To Implement To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10394,25 +18532,31 @@ To successfully implement this search, you must be ingesting data that records r | T1547.010 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives You will encounter noise from legitimate print-monitor registry entries. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.010/atomic_red_team/windows-sysmon.log + _version_: 2
--- + ### Monitor Web Traffic For Brand Abuse This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Web - **ATT&CK**: - **Last Updated**: 2017-09-23 @@ -10421,40 +18565,54 @@ This search looks for Web requests to faux domains similar to the one that you w #### Search ``` -| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter` + +| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `brand_abuse_web` +| `monitor_web_traffic_for_brand_abuse_filter` ``` #### Associated Analytic Story +* Brand Monitoring + + #### How To Implement You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Delivery + #### Known False Positives None at this time #### References + #### Test Dataset + _version_: 1 --- + ### More than usual number of LOLBAS applications in short time period Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1059, T1053 +- **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/), [T1053](https://attack.mitre.org/techniques/T1053/) - **Last Updated**: 2020-08-25
@@ -10462,18 +18620,33 @@ Attacker activity may compromise executing several LOLBAS applications in conjun #### Search ``` - | from read_ssa_enriched_events() | eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" | stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) | eval lolbas_counter=lolbas_counter*1.0 | rename window_end as timestamp | adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L | where label AND quantile>0.99 | eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" +| stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) +| eval lolbas_counter=lolbas_counter*1.0 +| rename window_end as timestamp +| adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L +| where label AND quantile>0.99 +| eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement Collect endpoint data such as sysmon or 4688 events. #### Required fields + * dest_device_id + * _time + * process_name + #### ATT&CK | ID | technique | Tactic | @@ -10482,27 +18655,33 @@ Collect endpoint data such as sysmon or 4688 events. | T1053 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Some administrative tasks may involve multiple use of LOLBAS applications in a short period of time. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. #### References + * https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries + #### Test Dataset + _version_: 1
--- + ### Multiple Okta Users With Invalid Credentials From The Same IP This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.001 +- **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078.001/) - **Last Updated**: 2020-07-21
@@ -10510,15 +18689,25 @@ This search detects Okta login failures due to bad credentials for multiple user #### Search ``` -`okta` outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search distinct_users > 5| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` +`okta` outcome.reason=INVALID_CREDENTIALS +| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city +| stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search distinct_users > 5 +| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` ``` #### Associated Analytic Story +* Suspicious Okta Activity + + #### How To Implement This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10527,23 +18716,27 @@ This search is specific to Okta and requires Okta logs are being ingested in you #### Kill Chain Phases + #### Known False Positives A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search. #### References + #### Test Dataset + _version_: 2
--- + ### NLTest Domain Trust Discovery This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1482 +- **Data Models**: Endpoint +- **ATT&CK**: [T1482](https://attack.mitre.org/techniques/T1482/) - **Last Updated**: 2021-01-25
@@ -10551,15 +18744,24 @@ This search looks for the execution of `nltest.exe` with command-line arguments #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `nltest_domain_trust_discovery_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `nltest_domain_trust_discovery_filter` ``` #### Associated Analytic Story +* Ryuk Ransomware + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10567,33 +18769,46 @@ To successfully implement this search you need to be ingesting information on pr | T1482 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Administrators may use nltest for troubleshooting purposes, otherwise, rarely used. #### References + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md + * https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104 + * https://attack.mitre.org/techniques/T1482/ + * https://www.owasp.org/images/4/4b/Red_Team_Operating_in_a_Modern_Environment.pdf + * https://ss64.com/nt/nltest.html + * https://redcanary.com/threat-detection-report/techniques/domain-trust-discovery/ + * https://thedfirreport.com/2020/10/08/ryuks-return/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### New container uploaded to AWS ECR This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1525 +- **ATT&CK**: [T1525](https://attack.mitre.org/techniques/T1525/) - **Last Updated**: 2020-02-20
@@ -10601,15 +18816,22 @@ This searches show information on uploaded containers including source user, ima #### Search ``` -| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")` | `new_container_uploaded_to_aws_ecr_filter` + +| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type +| `drop_dm_object_name("Compute")` +| `new_container_uploaded_to_aws_ecr_filter` ``` #### Associated Analytic Story +* Container Implantation Monitoring and Investigation + + #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10618,22 +18840,26 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Kill Chain Phases + #### Known False Positives Uploading container is a normal behavior from developers or users with access to container registry. #### References + #### Test Dataset + _version_: 1
--- + ### No Windows Updates in a time frame This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Updates - **ATT&CK**: - **Last Updated**: 2017-09-15 @@ -10642,15 +18868,29 @@ This search looks for Windows endpoints that have not generated an event indicat #### Search ``` -| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) | `security_content_ctime(lastTime)` | search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter` + +| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product +| rename Updates.dest as Host +| rename Updates.status as "Update Status" +| rename Updates.vendor_product as Product +| eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) +| `security_content_ctime(lastTime)` +| search isOutlier=1 +| rename lastTime as "Last Update Time", +| table Host, "Update Status", Product, "Last Update Time" +| `no_windows_updates_in_a_time_frame_filter` ``` #### Associated Analytic Story +* Monitor for Updates + + #### How To Implement To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10658,25 +18898,29 @@ To successfully implement this search, it requires that the 'Update' data model #### Kill Chain Phases + #### Known False Positives None identified #### References + #### Test Dataset + _version_: 1 --- + ### Ntdsutil export ntds Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \ ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1003.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2021-01-28
@@ -10684,15 +18928,24 @@ This technique uses "Install from Media" (IFM), which will extract a copy of the #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `ntdsutil_export_ntds_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `ntdsutil_export_ntds_filter` ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10700,30 +18953,40 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1003.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives. #### References + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil + * https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11) + * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + * https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### O365 Add App Role Assignment Grant User This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1136.003 +- **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136.003/) - **Last Updated**: 2021-01-26
@@ -10731,15 +18994,25 @@ This search detects the creation of a new Federation setting by alerting about a #### Search ``` -`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." | stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_add_app_role_assignment_grant_user_filter` +`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." +| stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_add_app_role_assignment_grant_user_filter` ``` #### Associated Analytic Story +* Office 365 Detections + +* Cloud Federated Credential Abuse + + #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10747,28 +19020,36 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 | T1136.003 | x | x | #### Kill Chain Phases + * Actions on Objective + #### Known False Positives The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. #### References + * https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + * https://us-cert.cisa.gov/ncas/alerts/aa21-008a + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json + _version_: 1
--- + ### O365 Added Service Principal This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1136.003 +- **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136.003/) - **Last Updated**: 2021-01-26
@@ -10776,15 +19057,25 @@ This search detects the creation of a new Federation setting by alerting about a #### Search ``` -`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." | stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_added_service_principal_filter` +`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." +| stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_added_service_principal_filter` ``` #### Associated Analytic Story +* Office 365 Detections + +* Cloud Federated Credential Abuse + + #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10792,30 +19083,40 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 | T1136.003 | x | x | #### Kill Chain Phases + * Actions on Objective + #### Known False Positives The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. #### References + * https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + * https://us-cert.cisa.gov/ncas/alerts/aa21-008a + * https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + * https://www.sygnia.co/golden-saml-advisory + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json + _version_: 1
--- + ### O365 Bypass MFA via Trusted IP This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1562.007 +- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562.007/) - **Last Updated**: 2021-01-12
@@ -10823,15 +19124,28 @@ This search detects newly added IP addresses/CIDR blocks to the list of MFA Trus #### Search ``` -`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy | rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") | mvexpand ip_addresses_new_added | where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) |stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account status user_id action | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `o365_bypass_mfa_via_trusted_ip_filter` +`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy +| rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" +| rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" +| eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") +| mvexpand ip_addresses_new_added +| where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) +|stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account status user_id action +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_bypass_mfa_via_trusted_ip_filter` ``` #### Associated Analytic Story +* Office 365 Detections + + #### How To Implement You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10839,28 +19153,36 @@ You must install Splunk Microsoft Office 365 add-on. This search works with o365 | T1562.007 | x | x | #### Kill Chain Phases + * Actions on Objective + #### Known False Positives Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration. #### References + * https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf + * https://attack.mitre.org/techniques/T1562/007/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json + _version_: 1
--- + ### O365 Disable MFA This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1556 +- **ATT&CK**: [T1556](https://attack.mitre.org/techniques/T1556/) - **Last Updated**: 2020-12-16
@@ -10868,15 +19190,23 @@ This search detects when multi factor authentication has been disabled, what ent #### Search ``` -`o365_management_activity` Operation="Disable Strong Authentication." | stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_disable_mfa_filter` +`o365_management_activity` Operation="Disable Strong Authentication." +| stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `o365_disable_mfa_filter` ``` #### Associated Analytic Story +* Office 365 Detections + + #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10884,27 +19214,34 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 | T1556 | x | x | #### Kill Chain Phases + * Actions on Objective + #### Known False Positives Unless it is a special case, it is uncommon to disable MFA or Strong Authentication #### References + * https://attack.mitre.org/techniques/T1556/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json + _version_: 1
--- + ### O365 Excessive Authentication Failures Alert This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1110 +- **ATT&CK**: [T1110](https://attack.mitre.org/techniques/T1110/) - **Last Updated**: 2020-12-16
@@ -10912,15 +19249,24 @@ This search detects when an excessive number of authentication failures occur th #### Search ``` -`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed | stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user | where count > 10 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_excessive_authentication_failures_alert_filter` +`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed +| stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user +| where count > 10 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `o365_excessive_authentication_failures_alert_filter` ``` #### Associated Analytic Story +* Office 365 Detections + + #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10928,27 +19274,34 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 | T1110 | x | x | #### Kill Chain Phases + * Not Applicable + #### Known False Positives The threshold for alert is above 10 attempts and this should reduce the number of false positives. #### References + * https://attack.mitre.org/techniques/T1110/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json + _version_: 1
--- + ### O365 Excessive SSO logon errors This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1556 +- **ATT&CK**: [T1556](https://attack.mitre.org/techniques/T1556/) - **Last Updated**: 2021-01-26
@@ -10956,15 +19309,26 @@ This search detects accounts with high number of Single Sign ON (SSO) logon erro #### Search ``` -`o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired | stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId | where count > 5 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_excessive_sso_logon_errors_filter` +`o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired +| stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId +| where count > 5 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_excessive_sso_logon_errors_filter` ``` #### Associated Analytic Story +* Office 365 Detections + +* Cloud Federated Credential Abuse + + #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -10972,27 +19336,34 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 | T1556 | x | x | #### Kill Chain Phases + * Actions on Objective + #### Known False Positives Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack. #### References + * https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json + _version_: 1
--- + ### O365 New Federated Domain Added This search detects the addition of a new Federated domain. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1136.003 +- **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136.003/) - **Last Updated**: 2021-01-26
@@ -11000,15 +19371,25 @@ This search detects the addition of a new Federated domain. #### Search ``` -`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" | stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_federated_domain_added_filter` +`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" +| stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_new_federated_domain_added_filter` ``` #### Associated Analytic Story +* Office 365 Detections + +* Cloud Federated Credential Abuse + + #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11016,31 +19397,42 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 | T1136.003 | x | x | #### Kill Chain Phases + * Actions on Objective + #### Known False Positives The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider. #### References + * https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + * https://us-cert.cisa.gov/ncas/alerts/aa21-008a + * https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + * https://www.sygnia.co/golden-saml-advisory + * https://o365blog.com/post/aadbackdoor/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json + _version_: 1
--- + ### O365 PST export alert This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1114 +- **ATT&CK**: [T1114](https://attack.mitre.org/techniques/T1114/) - **Last Updated**: 2020-12-16
@@ -11048,15 +19440,23 @@ This search detects when a user has performed an Ediscovery search or exported a #### Search ``` -`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" | stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_pst_export_alert_filter` +`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" +| stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `o365_pst_export_alert_filter` ``` #### Associated Analytic Story +* Office 365 Detections + + #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11064,27 +19464,34 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 | T1114 | x | x | #### Kill Chain Phases + * Actions on Objective + #### Known False Positives PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored. #### References + * https://attack.mitre.org/techniques/T1114/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json + _version_: 1
--- + ### O365 Suspicious Admin Email Forwarding This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1114.003 +- **ATT&CK**: [T1114.003](https://attack.mitre.org/techniques/T1114.003/) - **Last Updated**: 2020-12-16
@@ -11092,15 +19499,27 @@ This search detects when an admin configured a forwarding rule for multiple mail #### Search ``` -`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_admin_email_forwarding_filter` +`o365_management_activity` Operation=Set-Mailbox +| spath input=Parameters +| rename Identity AS src_user +| search ForwardingAddress=* +| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress +| where count_src_user > 1 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`o365_suspicious_admin_email_forwarding_filter` ``` #### Associated Analytic Story +* Office 365 Detections + + #### How To Implement #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11108,26 +19527,32 @@ This search detects when an admin configured a forwarding rule for multiple mail | T1114.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives unknown #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json + _version_: 1
--- + ### O365 Suspicious Rights Delegation This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1114.002 +- **ATT&CK**: [T1114.002](https://attack.mitre.org/techniques/T1114.002/) - **Last Updated**: 2020-12-15
@@ -11135,15 +19560,26 @@ This search detects the assignment of rights to accesss content from another mai #### Search ``` -`o365_management_activity` Operation=Add-MailboxPermission | spath input=Parameters | rename User AS src_user, Identity AS dest_user | search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf | stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_rights_delegation_filter` +`o365_management_activity` Operation=Add-MailboxPermission +| spath input=Parameters +| rename User AS src_user, Identity AS dest_user +| search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf +| stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`o365_suspicious_rights_delegation_filter` ``` #### Associated Analytic Story +* Office 365 Detections + + #### How To Implement #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11151,26 +19587,32 @@ This search detects the assignment of rights to accesss content from another mai | T1114.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Service Accounts #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json + _version_: 1
--- + ### O365 Suspicious User Email Forwarding This search detects when multiple user configured a forwarding rule to the same destination. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1114.003 +- **ATT&CK**: [T1114.003](https://attack.mitre.org/techniques/T1114.003/) - **Last Updated**: 2020-12-16
@@ -11178,15 +19620,27 @@ This search detects when multiple user configured a forwarding rule to the same #### Search ``` -`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingSmtpAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_user_email_forwarding_filter` +`o365_management_activity` Operation=Set-Mailbox +| spath input=Parameters +| rename Identity AS src_user +| search ForwardingSmtpAddress=* +| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress +| where count_src_user > 1 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`o365_suspicious_user_email_forwarding_filter` ``` #### Associated Analytic Story +* Office 365 Detections + + #### How To Implement #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11194,26 +19648,32 @@ This search detects when multiple user configured a forwarding rule to the same | T1114.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives unknown #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json + _version_: 1
--- + ### Okta Account Lockout Events Detect Okta user lockout events - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.001 +- **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078.001/) - **Last Updated**: 2020-07-21
@@ -11221,15 +19681,22 @@ Detect Okta user lockout events #### Search ``` -`okta` displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, country, state, city, src_ip | `okta_account_lockout_events_filter` +`okta` displayMessage="Max sign in attempts exceeded" +| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city +| table _time, user, country, state, city, src_ip +| `okta_account_lockout_events_filter` ``` #### Associated Analytic Story +* Suspicious Okta Activity + + #### How To Implement This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11238,23 +19705,27 @@ This search is specific to Okta and requires Okta logs are being ingested in you #### Kill Chain Phases + #### Known False Positives None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. #### References + #### Test Dataset + _version_: 2
--- + ### Okta Failed SSO Attempts Detect failed Okta SSO events - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.001 +- **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078.001/) - **Last Updated**: 2020-07-21
@@ -11262,15 +19733,23 @@ Detect failed Okta SSO events #### Search ``` -`okta` displayMessage="User attempted unauthorized access to app" | stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_failed_sso_attempts_filter` +`okta` displayMessage="User attempted unauthorized access to app" +| stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `okta_failed_sso_attempts_filter` ``` #### Associated Analytic Story +* Suspicious Okta Activity + + #### How To Implement This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11279,23 +19758,27 @@ This search is specific to Okta and requires Okta logs are being ingested in you #### Kill Chain Phases + #### Known False Positives There may be a faulty config preventing legitmate users from accessing apps they should have access to. #### References + #### Test Dataset + _version_: 2
--- + ### Okta User Logins From Multiple Cities This search detects logins from the same user from different cities in a 24 hour period. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078.001 +- **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078.001/) - **Last Updated**: 2020-07-21
@@ -11303,15 +19786,24 @@ This search detects logins from the same user from different cities in a 24 hour #### Search ``` -`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `okta_user_logins_from_multiple_cities_filter` | search locations > 1 +`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null +| stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `okta_user_logins_from_multiple_cities_filter` +| search locations > 1 ``` #### Associated Analytic Story +* Suspicious Okta Activity + + #### How To Implement This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11320,17 +19812,21 @@ This search is specific to Okta and requires Okta logs are being ingested in you #### Kill Chain Phases + #### Known False Positives Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. #### References + #### Test Dataset + _version_: 2
--- + ### Open Redirect in Splunk Web This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. @@ -11344,34 +19840,44 @@ This search allows you to look for evidence of exploitation for CVE-2016-4859, t #### Search ``` -index=_internal sourcetype=splunk_web_access return_to="/%09/*" | `open_redirect_in_splunk_web_filter` +index=_internal sourcetype=splunk_web_access return_to="/%09/*" +| `open_redirect_in_splunk_web_filter` ``` #### Associated Analytic Story +* Splunk Enterprise Vulnerability + + #### How To Implement No extra steps needed to implement this search. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Delivery + #### Known False Positives None identified #### References + #### Test Dataset + _version_: 1 --- + ### Osquery pack - ColdRoot detection This search looks for ColdRoot events from the osx-attacks osquery pack. @@ -11385,41 +19891,57 @@ This search looks for ColdRoot events from the osx-attacks osquery pack. #### Search ``` -| from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path | bucket _time span=30s | stats count(path) by _time, host, user, path | `osquery_pack___coldroot_detection_filter` + +| from datamodel Alerts.Alerts +| search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) +| rename columns.path as path +| bucket _time span=30s +| stats count(path) by _time, host, user, path +| `osquery_pack___coldroot_detection_filter` ``` #### Associated Analytic Story +* ColdRoot MacOS RAT + + #### How To Implement In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Installation + * Command and Control + #### Known False Positives There are no known false positives. #### References + #### Test Dataset + _version_: 1 --- + ### Overwriting Accessibility Binaries Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1546.008 +- **Data Models**: Endpoint +- **ATT&CK**: [T1546.008](https://attack.mitre.org/techniques/T1546.008/) - **Last Updated**: 2020-07-21
@@ -11427,15 +19949,24 @@ Microsoft Windows contains accessibility features that can be launched with a ke #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `overwriting_accessibility_binaries_filter` ``` #### Associated Analytic Story +* Windows Privilege Escalation + + #### How To Implement You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11443,26 +19974,32 @@ You must be ingesting data that records the filesystem activity from your hosts | T1546.008 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.008/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### Phishing Email Detection by Machine Learning Method - SSA Malicious mails can conduct phishing that induces readers to open attachment, click links or trigger third party service. This detect uses Natural Language Processing (NLP) approach to analyze an email message's content (Sender, Subject and Body) and judge whether it is a phishing email. The detection adopts a deep learning (neural network) model that employs character level embeddings plus LSTM layers to perform classification. The model is pre-trained and then published as ONNX format. Current sample model is trained using the dataset published at https://github.com/splunk/attack_data/tree/master/datasets/T1566_Phishing_Email/splunk_train.json User are expected to re-train the model by combining with their own training data for better accuracy using the provided model file (SMLE notebook). DSP pipeline then processes the email message and passes it as an event to Apply ML Models function, which returns the probability of a phishing email. Current implementation assumes the email is fed to DSP in JSON format contains at least email's sender, subject and its message body, including reply content, if any. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1566 +- **ATT&CK**: [T1566](https://attack.mitre.org/techniques/T1566/) - **Last Updated**: 2020-08-25
@@ -11470,15 +20007,28 @@ Malicious mails can conduct phishing that induces readers to open attachment, cl #### Search ``` -| from read_ssa_enriched_events() | eval eventLine=concat(ucast(map_get(input_event, "From"), "string", " "), " ", ucast(map_get(input_event, "Subject"), "string", " "), " ", ucast(map_get(input_event, "Content"), "string", " "), " "), _time=map_get(input_event, "_time") | where eventLine IS NOT NULL | eval mapC={" ": 32, "!": 33, "\"": 34, "#": 35, "$": 36, "%": 37, "&": 38, "`": 39, "(": 40, ")": 41, "*": 42, "+": 43, ",": 44, "-": 45, ".": 46, "/": 47, "0": 48, "1": 49, "2": 50, "3": 51, "4": 52, "5": 53, "6": 54, "7": 55, "8": 56, "9": 57, ":": 58, ";": 59, "<": 60, "=": 61, ">": 62, "?": 63, "@": 64, "A": 65, "B": 66, "C": 67, "D": 68, "E": 69, "F": 70, "G": 71, "H": 72, "I": 73, "J": 74, "K": 75, "L": 76, "M": 77, "N": 78, "O": 79, "P": 80, "Q": 81, "R": 82, "S": 83, "T": 84, "U": 85, "V": 86, "W": 87, "X": 88, "Y": 89, "Z": 90, "[": 91, "\\": 92, "]": 93, "^": 94, "_": 95, "`": 96, "a": 97, "b": 98, "c": 99, "d": 100, "e": 101, "f": 102, "g": 103, "h": 104, "i": 105, "j": 106, "k": 107, "l": 108, "m": 109, "n": 110, "o": 111, "p": 112, "q": 113, "r": 114, "s": 115, "t": 116, "u": 117, "v": 118, "w": 119, "x": 120, "y": 121, "z": 122, "{": 123, "|": 124, "}": 125, "~": 126}, ml_in = for_each(iterator(mvrange(1,129), "i"), cast(map_get(mapC, substr(eventLine, i, 1)), "float") ) | apply_model connection_id="YOUR_S3_ONNX_CONNECTOR_ID" name="phishing_email_v8" path="s3://smle-experiments/models/phishing_email" | eval probability = mvindex(ml_out, 0) | where probability > 0.5 | eval start_time=_time, end_time=_time, entities="TBD", body="TBD" | select probability, body, entities, start_time, end_time | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval eventLine=concat(ucast(map_get(input_event, "From"), "string", " "), " ", ucast(map_get(input_event, "Subject"), "string", " "), " ", ucast(map_get(input_event, "Content"), "string", " "), " "), _time=map_get(input_event, "_time") +| where eventLine IS NOT NULL +| eval mapC={" ": 32, "!": 33, "\"": 34, "#": 35, "$": 36, "%": 37, "&": 38, "`": 39, "(": 40, ")": 41, "*": 42, "+": 43, ",": 44, "-": 45, ".": 46, "/": 47, "0": 48, "1": 49, "2": 50, "3": 51, "4": 52, "5": 53, "6": 54, "7": 55, "8": 56, "9": 57, ":": 58, ";": 59, "<": 60, "=": 61, ">": 62, "?": 63, "@": 64, "A": 65, "B": 66, "C": 67, "D": 68, "E": 69, "F": 70, "G": 71, "H": 72, "I": 73, "J": 74, "K": 75, "L": 76, "M": 77, "N": 78, "O": 79, "P": 80, "Q": 81, "R": 82, "S": 83, "T": 84, "U": 85, "V": 86, "W": 87, "X": 88, "Y": 89, "Z": 90, "[": 91, "\\": 92, "]": 93, "^": 94, "_": 95, "`": 96, "a": 97, "b": 98, "c": 99, "d": 100, "e": 101, "f": 102, "g": 103, "h": 104, "i": 105, "j": 106, "k": 107, "l": 108, "m": 109, "n": 110, "o": 111, "p": 112, "q": 113, "r": 114, "s": 115, "t": 116, "u": 117, "v": 118, "w": 119, "x": 120, "y": 121, "z": 122, "{": 123, " +|": 124, "}": 125, "~": 126}, ml_in = for_each(iterator(mvrange(1,129), "i"), cast(map_get(mapC, substr(eventLine, i, 1)), "float") ) +| apply_model connection_id="YOUR_S3_ONNX_CONNECTOR_ID" name="phishing_email_v8" path="s3://smle-experiments/models/phishing_email" +| eval probability = mvindex(ml_out, 0) +| where probability > 0.5 +| eval start_time=_time, end_time=_time, entities="TBD", body="TBD" +| select probability, body, entities, start_time, end_time +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement Events are fed to DSP contains at least email's sender, subject and its message body. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11486,25 +20036,30 @@ Events are fed to DSP contains at least email's sender, subject and its message | T1566 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Because of imbalance of anomaly data in training, the model will less likely report false positive. Instead, the model is more prone to false negative. Current best recall score is ~85% #### References + #### Test Dataset + _version_: 1
--- + ### Probing Access with Stolen Credentials via PowerSploit modules This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1078, T1098 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-04
@@ -11512,21 +20067,32 @@ This detection identifies use of PowerSploit modules that facilitate access prob #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-AdminAccess/)=true OR match_regex(cmd_line, /(?i)Invoke-CheckLocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Test-ServiceDaclPermission/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-AdminAccess/)=true OR match_regex(cmd_line, /(?i)Invoke-CheckLocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Test-ServiceDaclPermission/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_user_id + * dest_device_id + #### ATT&CK | ID | technique | Tactic | @@ -11535,26 +20101,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1098 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Process Creating LNK file in Suspicious Location This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1566.002 +- **ATT&CK**: [T1566.002](https://attack.mitre.org/techniques/T1566.002/) - **Last Updated**: 2021-01-28
@@ -11562,15 +20134,31 @@ This search looks for a process launching an `*.lnk` file under `C:\User*` or `* #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_id as lnk_pid | join lnk_pid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` | rename parent_process_id as lnk_pid | fields _time lnk_pid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `process_creating_lnk_file_in_suspicious_location_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user +| `drop_dm_object_name(Filesystem)` +| rename process_id as lnk_pid +| join lnk_pid, _time [ +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process +| `drop_dm_object_name(Processes)` +| rename parent_process_id as lnk_pid +| fields _time lnk_pid process_id dest process_name process_path process] +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash +| `process_creating_lnk_file_in_suspicious_location_filter` ``` #### Associated Analytic Story +* Phishing Payloads + + #### How To Implement You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11578,29 +20166,38 @@ You must be ingesting data that records filesystem and process activity from you | T1566.002 | x | x | #### Kill Chain Phases + * Installation + * Actions on Objectives + #### Known False Positives This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories. #### References + * https://attack.mitre.org/techniques/T1566/001/ + * https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log + _version_: 4
--- + ### Process Execution via WMI This search looks for processes launched via WMI. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1047 +- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) - **Last Updated**: 2020-03-16
@@ -11608,15 +20205,24 @@ This search looks for processes launched via WMI. #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `process_execution_via_wmi_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `process_execution_via_wmi_filter` ``` #### Associated Analytic Story +* Suspicious WMI Use + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11624,20 +20230,26 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1047 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, administrators may use wmi to execute commands for legitimate purposes. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + _version_: 3
--- + ### Processes Tapping Keyboard Events This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input @@ -11651,40 +20263,55 @@ This search looks for processes in an MacOS system that is tapping keyboard even #### Search ``` -| from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id| dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter` + +| from datamodel Alerts.Alerts +| search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps +| rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id +| dedup host,process_name +| table host,process_name, cmd, process_id +| `processes_tapping_keyboard_events_filter` ``` #### Associated Analytic Story +* ColdRoot MacOS RAT + + #### How To Implement In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Command and Control + #### Known False Positives There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. #### References + #### Test Dataset + _version_: 1 --- + ### Processes created by netsh This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.004 +- **Data Models**: Endpoint +- **ATT&CK**: [T1562.004](https://attack.mitre.org/techniques/T1562.004/) - **Last Updated**: 2020-11-23
@@ -11692,15 +20319,24 @@ This search looks for processes launching netsh.exe to execute various commands #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `processes_created_by_netsh_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `processes_created_by_netsh_filter` ``` #### Associated Analytic Story +* Netsh Abuse + + #### How To Implement To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11708,25 +20344,30 @@ To successfully implement this search, you must be ingesting logs with the proce | T1562.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft. #### References + #### Test Dataset + _version_: 5
--- + ### Processes launching netsh This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.004 +- **Data Models**: Endpoint +- **ATT&CK**: [T1562.004](https://attack.mitre.org/techniques/T1562.004/) - **Last Updated**: 2020-07-10
@@ -11734,15 +20375,28 @@ This search looks for processes launching netsh.exe. Netsh is a command-line scr #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`processes_launching_netsh_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest +|`drop_dm_object_name("Processes")` +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`processes_launching_netsh_filter` ``` #### Associated Analytic Story +* Netsh Abuse + +* Disabling Security Tools + +* DHS Report TA18-074A + + #### How To Implement To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11750,26 +20404,32 @@ To successfully implement this search, you must be ingesting data that records p | T1562.004 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/atomic_red_team/windows-sysmon.log + _version_: 3
--- + ### Prohibited Network Traffic Allowed This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1048](https://attack.mitre.org/techniques/T1048/) - **Last Updated**: 2020-07-21
@@ -11777,15 +20437,30 @@ This search looks for network traffic defined by port and transport layer protoc #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action +| lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport +| search is_prohibited=true +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `prohibited_network_traffic_allowed_filter` ``` #### Associated Analytic Story +* Prohibited Traffic Allowed or Protocol Mismatch + +* Ransomware + +* Command and Control + + #### How To Implement In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11793,25 +20468,31 @@ In order to properly run this search, Splunk needs to ingest data from firewalls | T1048 | x | x | #### Kill Chain Phases + * Delivery + * Command and Control + #### Known False Positives None identified #### References + #### Test Dataset + _version_: 2
--- + ### Prohibited Software On Endpoint This search looks for applications on the endpoint that you have marked as prohibited. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Endpoint - **ATT&CK**: - **Last Updated**: 2019-10-11 @@ -11820,42 +20501,63 @@ This search looks for applications on the endpoint that you have marked as prohi #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `prohibited_softwares` | `prohibited_software_on_endpoint_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `prohibited_softwares` +| `prohibited_software_on_endpoint_filter` ``` #### Associated Analytic Story +* Monitor for Unauthorized Software + +* Emotet Malware DHS Report TA18-201A + +* SamSam Ransomware + + #### How To Implement To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Installation + * Command and Control + * Actions on Objectives + #### Known False Positives None identified #### References + #### Test Dataset + _version_: 2 --- + ### Protocol or Port Mismatch This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1048.003 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) - **Last Updated**: 2020-07-21
@@ -11863,15 +20565,26 @@ This search looks for network traffic on common ports where a higher layer proto #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port +|`security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `protocol_or_port_mismatch_filter` ``` #### Associated Analytic Story +* Prohibited Traffic Allowed or Protocol Mismatch + +* Command and Control + + #### How To Implement Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -11879,24 +20592,29 @@ Running this search properly requires a technology that can inspect network traf | T1048.003 | x | x | #### Kill Chain Phases + * Command and Control + #### Known False Positives None identified #### References + #### Test Dataset + _version_: 2
--- + ### Protocols passing authentication in cleartext This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Network_Traffic - **ATT&CK**: - **Last Updated**: 2020-11-04 @@ -11905,41 +20623,56 @@ This search looks for cleartext protocols at risk of leaking credentials. Curren #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocols_passing_authentication_in_cleartext_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `protocols_passing_authentication_in_cleartext_filter` ``` #### Associated Analytic Story +* Use of Cleartext Protocols + + #### How To Implement This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Reconnaissance + * Actions on Objectives + #### Known False Positives Some networks may use kerberized FTP or telnet servers, however, this is rare. #### References + #### Test Dataset + _version_: 2 --- + ### Rare Parent-Child Process Relationship An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1203, T1059, T1053, T1072 +- **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/), [T1059](https://attack.mitre.org/techniques/T1059/), [T1053](https://attack.mitre.org/techniques/T1053/), [T1072](https://attack.mitre.org/techniques/T1072/) - **Last Updated**: 2020-08-13
@@ -11947,21 +20680,40 @@ An attacker may use LOLBAS tools spawned from vulnerable applications not typica #### Search ``` -| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) | where parent_process_name!=null | select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id | conditional_anomaly conditional="parent_process_name" target="process_name" | rename output as input | where input < 1 | adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L | where label AND quantile<0.1 AND (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe") -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) +| where parent_process_name!=null +| select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id +| conditional_anomaly conditional="parent_process_name" target="process_name" +| rename output as input +| where input < 1 +| adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L +| where label AND quantile<0.1 AND (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe") + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement Collect endpoint data such as sysmon or 4688 events. #### Required fields + * process_name + * parent_process_name + * _time + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -11972,26 +20724,31 @@ Collect endpoint data such as sysmon or 4688 events. | T1072 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. #### References + #### Test Dataset + _version_: 1
--- + ### Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1078, T1087, T1484 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1087](https://attack.mitre.org/techniques/T1087/), [T1484](https://attack.mitre.org/techniques/T1484/) - **Last Updated**: 2020-11-05
@@ -11999,21 +20756,32 @@ This detection identifies access to PowerSploit modules that discover accounts, #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Invoke-EnumerateLocalAdmin/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Invoke-EventHunter/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserLocation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserHunter/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignGroupMember/)=true OR match_regex(cmd_line, /(?i)Find-ForeignGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignUser/)=true OR match_regex(cmd_line, /(?i)Find-ForeignUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPO/)=true OR match_regex(cmd_line, /(?i)Get-NetGPO/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOComputerLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOComputerAdmin/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGPOGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOUserLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOLocation/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-DomainManagedSecurityGroup/)=true OR match_regex(cmd_line, /(?i)Find-ManagedSecurityGroups/)=true OR match_regex(cmd_line, /(?i)Get-DomainOU/)=true OR match_regex(cmd_line, /(?i)Get-NetOU/)=true OR match_regex(cmd_line, /(?i)Get-DomainUser/)=true OR match_regex(cmd_line, /(?i)Get-NetUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Get-UserEvent/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetLoggedon/)=true OR match_regex(cmd_line, /(?i)Get-RegLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegLastLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-LastLoggedOn/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Invoke-EnumerateLocalAdmin/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Invoke-EventHunter/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserLocation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserHunter/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignGroupMember/)=true OR match_regex(cmd_line, /(?i)Find-ForeignGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignUser/)=true OR match_regex(cmd_line, /(?i)Find-ForeignUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPO/)=true OR match_regex(cmd_line, /(?i)Get-NetGPO/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOComputerLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOComputerAdmin/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGPOGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOUserLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOLocation/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-DomainManagedSecurityGroup/)=true OR match_regex(cmd_line, /(?i)Find-ManagedSecurityGroups/)=true OR match_regex(cmd_line, /(?i)Get-DomainOU/)=true OR match_regex(cmd_line, /(?i)Get-NetOU/)=true OR match_regex(cmd_line, /(?i)Get-DomainUser/)=true OR match_regex(cmd_line, /(?i)Get-NetUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Get-UserEvent/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetLoggedon/)=true OR match_regex(cmd_line, /(?i)Get-RegLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegLastLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-LastLoggedOn/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12023,26 +20791,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1484 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Reconnaissance and Access to Accounts and Groups via Mimikatz modules This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1078, T1087, T1484 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1087](https://attack.mitre.org/techniques/T1087/), [T1484](https://attack.mitre.org/techniques/T1484/) - **Last Updated**: 2020-11-05
@@ -12050,21 +20824,32 @@ This detection identifies use of Mimikatz modules for discovery of accounts and #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::user/)=true OR match_regex(cmd_line, /(?i)net::group/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::user/)=true OR match_regex(cmd_line, /(?i)net::group/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12074,26 +20859,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1484 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + #### Test Dataset + _version_: 1
--- + ### Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1199, T1482, T1590, T1591, T1595 +- **ATT&CK**: [T1199](https://attack.mitre.org/techniques/T1199/), [T1482](https://attack.mitre.org/techniques/T1482/), [T1590](https://attack.mitre.org/techniques/T1590/), [T1591](https://attack.mitre.org/techniques/T1591/), [T1595](https://attack.mitre.org/techniques/T1595/) - **Last Updated**: 2020-11-06
@@ -12101,21 +20892,32 @@ This detection identifies access to PowerSploit modules for reconnaissance and a #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainSID/)=true OR match_regex(cmd_line, /(?i)Get-DomainSite/)=true OR match_regex(cmd_line, /(?i)Get-NetSite/)=true OR match_regex(cmd_line, /(?i)Get-DomainSubnet/)=true OR match_regex(cmd_line, /(?i)Get-NetSubnet/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrustMapping/)=true OR match_regex(cmd_line, /(?i)Invoke-MapDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-Forest/)=true OR match_regex(cmd_line, /(?i)Get-NetForest/)=true OR match_regex(cmd_line, /(?i)Get-ForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-NetForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-ForestGlobalCatalog/)=true OR match_regex(cmd_line, /(?i)Get-NetForestCatalog/)=true OR match_regex(cmd_line, /(?i)Get-ForestTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetForestTrust/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainSID/)=true OR match_regex(cmd_line, /(?i)Get-DomainSite/)=true OR match_regex(cmd_line, /(?i)Get-NetSite/)=true OR match_regex(cmd_line, /(?i)Get-DomainSubnet/)=true OR match_regex(cmd_line, /(?i)Get-NetSubnet/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrustMapping/)=true OR match_regex(cmd_line, /(?i)Invoke-MapDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-Forest/)=true OR match_regex(cmd_line, /(?i)Get-NetForest/)=true OR match_regex(cmd_line, /(?i)Get-ForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-NetForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-ForestGlobalCatalog/)=true OR match_regex(cmd_line, /(?i)Get-NetForestCatalog/)=true OR match_regex(cmd_line, /(?i)Get-ForestTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetForestTrust/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12127,26 +20929,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1595 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Reconnaissance and Access to Computers and Domains via PowerSploit modules This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1592, T1590, T1087 +- **ATT&CK**: [T1592](https://attack.mitre.org/techniques/T1592/), [T1590](https://attack.mitre.org/techniques/T1590/), [T1087](https://attack.mitre.org/techniques/T1087/) - **Last Updated**: 2020-11-06
@@ -12154,21 +20962,32 @@ This detection identifies access to PowerSploit modules that discover computers, #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ComputerDetail/)=true OR match_regex(cmd_line, /(?i)Get-Domain/)=true OR match_regex(cmd_line, /(?i)Get-NetDomain/)=true OR match_regex(cmd_line, /(?i)Get-DomainComputer/)=true OR match_regex(cmd_line, /(?i)Get-NetComputer/)=true OR match_regex(cmd_line, /(?i)Get-DomainController/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainController/)=true OR match_regex(cmd_line, /(?i)Get-DomainFileServer/)=true OR match_regex(cmd_line, /(?i)Get-NetFileServer/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ComputerDetail/)=true OR match_regex(cmd_line, /(?i)Get-Domain/)=true OR match_regex(cmd_line, /(?i)Get-NetDomain/)=true OR match_regex(cmd_line, /(?i)Get-DomainComputer/)=true OR match_regex(cmd_line, /(?i)Get-NetComputer/)=true OR match_regex(cmd_line, /(?i)Get-DomainController/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainController/)=true OR match_regex(cmd_line, /(?i)Get-DomainFileServer/)=true OR match_regex(cmd_line, /(?i)Get-NetFileServer/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12178,26 +20997,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1087 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Reconnaissance and Access to Computers via Mimikatz modules This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1592 +- **ATT&CK**: [T1592](https://attack.mitre.org/techniques/T1592/) - **Last Updated**: 2020-11-06
@@ -12205,21 +21030,32 @@ This detection identifies use of Mimikatz modules for discovery of computers and #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::ServerInfo/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::ServerInfo/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12227,26 +21063,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1592 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + #### Test Dataset + _version_: 1
--- + ### Reconnaissance and Access to Operating System Elements via PowerSploit modules This detection identifies access to PowerSploit modules that discover and access operating system elements, such as processes, services, registry locations, security packages and files. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1007, T1012, T1046, T1047, T1057, T1083, T1518, T1592.002 +- **ATT&CK**: [T1007](https://attack.mitre.org/techniques/T1007/), [T1012](https://attack.mitre.org/techniques/T1012/), [T1046](https://attack.mitre.org/techniques/T1046/), [T1047](https://attack.mitre.org/techniques/T1047/), [T1057](https://attack.mitre.org/techniques/T1057/), [T1083](https://attack.mitre.org/techniques/T1083/), [T1518](https://attack.mitre.org/techniques/T1518/), [T1592.002](https://attack.mitre.org/techniques/T1592.002/) - **Last Updated**: 2020-11-06
@@ -12254,21 +21096,32 @@ This detection identifies access to PowerSploit modules that discover and access #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainProcess/)=true OR match_regex(cmd_line, /(?i)Invoke-ProcessHunter/)=true OR match_regex(cmd_line, /(?i)Get-ServiceDetail/)=true OR match_regex(cmd_line, /(?i)Get-WMIProcess/)=true OR match_regex(cmd_line, /(?i)Get-NetProcess/)=true OR match_regex(cmd_line, /(?i)Get-SecurityPackage/)=true OR match_regex(cmd_line, /(?i)Find-DomainObjectPropertyOutlier/)=true OR match_regex(cmd_line, /(?i)Get-DomainObject/)=true OR match_regex(cmd_line, /(?i)Get-ADObject/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegMountedDrive/)=true OR match_regex(cmd_line, /(?i)Get-RegistryMountedDrive/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainProcess/)=true OR match_regex(cmd_line, /(?i)Invoke-ProcessHunter/)=true OR match_regex(cmd_line, /(?i)Get-ServiceDetail/)=true OR match_regex(cmd_line, /(?i)Get-WMIProcess/)=true OR match_regex(cmd_line, /(?i)Get-NetProcess/)=true OR match_regex(cmd_line, /(?i)Get-SecurityPackage/)=true OR match_regex(cmd_line, /(?i)Find-DomainObjectPropertyOutlier/)=true OR match_regex(cmd_line, /(?i)Get-DomainObject/)=true OR match_regex(cmd_line, /(?i)Get-ADObject/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegMountedDrive/)=true OR match_regex(cmd_line, /(?i)Get-RegistryMountedDrive/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12283,26 +21136,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1592.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Reconnaissance and Access to Processes and Services via Mimikatz modules This detection identifies use of Mimikatz modules for discovery and access to services and processes. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1007, T1046, T1057 +- **ATT&CK**: [T1007](https://attack.mitre.org/techniques/T1007/), [T1046](https://attack.mitre.org/techniques/T1046/), [T1057](https://attack.mitre.org/techniques/T1057/) - **Last Updated**: 2020-11-06
@@ -12310,21 +21169,32 @@ This detection identifies use of Mimikatz modules for discovery and access to se #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::list/)=true OR match_regex(cmd_line, /(?i)service::list/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::list/)=true OR match_regex(cmd_line, /(?i)service::list/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12334,26 +21204,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1057 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + #### Test Dataset + _version_: 1
--- + ### Reconnaissance and Access to Shared Resources via Mimikatz modules This detection identifies use of Mimikatz modules for discovery and access to network shares. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1021.002, T1135, T1039 +- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/), [T1135](https://attack.mitre.org/techniques/T1135/), [T1039](https://attack.mitre.org/techniques/T1039/) - **Last Updated**: 2020-11-06
@@ -12361,21 +21237,32 @@ This detection identifies use of Mimikatz modules for discovery and access to ne #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::share/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::share/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12385,26 +21272,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1039 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + #### Test Dataset + _version_: 1
--- + ### Reconnaissance and Access to Shared Resources via PowerSploit modules This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1021.002, T1135, T1039 +- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/), [T1135](https://attack.mitre.org/techniques/T1135/), [T1039](https://attack.mitre.org/techniques/T1039/) - **Last Updated**: 2020-11-06
@@ -12412,21 +21305,32 @@ This detection identifies access to PowerSploit modules that discover and access #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainShare/)=true OR match_regex(cmd_line, /(?i)Invoke-ShareFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainShareFile/)=true OR match_regex(cmd_line, /(?i)Invoke-FileFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingFile/)=true OR match_regex(cmd_line, /(?i)Get-DomainDFSShare/)=true OR match_regex(cmd_line, /(?i)Get-DFSshare/)=true OR match_regex(cmd_line, /(?i)Get-NetShare/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainShare/)=true OR match_regex(cmd_line, /(?i)Invoke-ShareFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainShareFile/)=true OR match_regex(cmd_line, /(?i)Invoke-FileFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingFile/)=true OR match_regex(cmd_line, /(?i)Get-DomainDFSShare/)=true OR match_regex(cmd_line, /(?i)Get-DFSshare/)=true OR match_regex(cmd_line, /(?i)Get-NetShare/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12436,26 +21340,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1039 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Reconnaissance of Access and Persistence Opportunities via PowerSploit modules This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1053, T1068, T1078, T1543, T1547, T1574 +- **ATT&CK**: [T1053](https://attack.mitre.org/techniques/T1053/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1543](https://attack.mitre.org/techniques/T1543/), [T1547](https://attack.mitre.org/techniques/T1547/), [T1574](https://attack.mitre.org/techniques/T1574/) - **Last Updated**: 2020-11-05
@@ -12463,21 +21373,32 @@ This detection identifies use of PowerSploit modules that discover opportunities #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-LocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainAcl/)=true OR match_regex(cmd_line, /(?i)Invoke-ACLScanner/)=true OR match_regex(cmd_line, /(?i)Find-PathDLLHijack/)=true OR match_regex(cmd_line, /(?i)Find-ProcessDLLHijack/)=true OR match_regex(cmd_line, /(?i)Get-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-DomainPolicy/)=true OR match_regex(cmd_line, /(?i)Get-ModifiablePath/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableRegistryAutoRun/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableScheduledTaskFile/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableService/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableServiceFile/)=true OR match_regex(cmd_line, /(?i)Get-PathAcl/)=true OR match_regex(cmd_line, /(?i)Get-UnattendedInstallFile/)=true OR match_regex(cmd_line, /(?i)Get-UnquotedService/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-LocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainAcl/)=true OR match_regex(cmd_line, /(?i)Invoke-ACLScanner/)=true OR match_regex(cmd_line, /(?i)Find-PathDLLHijack/)=true OR match_regex(cmd_line, /(?i)Find-ProcessDLLHijack/)=true OR match_regex(cmd_line, /(?i)Get-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-DomainPolicy/)=true OR match_regex(cmd_line, /(?i)Get-ModifiablePath/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableRegistryAutoRun/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableScheduledTaskFile/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableService/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableServiceFile/)=true OR match_regex(cmd_line, /(?i)Get-PathAcl/)=true OR match_regex(cmd_line, /(?i)Get-UnattendedInstallFile/)=true OR match_regex(cmd_line, /(?i)Get-UnquotedService/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12490,26 +21411,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1574 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Reconnaissance of Connectivity via PowerSploit modules This detection identifies access to PowerSploit modules for reconnaissance of connectivity. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1021.002, T1135, T1039 +- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/), [T1135](https://attack.mitre.org/techniques/T1135/), [T1039](https://attack.mitre.org/techniques/T1039/) - **Last Updated**: 2020-11-06
@@ -12517,21 +21444,32 @@ This detection identifies access to PowerSploit modules for reconnaissance of co #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainDNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DomainDNSZone/)=true OR match_regex(cmd_line, /(?i)Get-DNSZone/)=true OR match_regex(cmd_line, /(?i)Invoke-ReverseDnsLookup/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegCachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-CachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegProxy/)=true OR match_regex(cmd_line, /(?i)Get-Proxy/)=true OR match_regex(cmd_line, /(?i)Invoke-Portscan/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainDNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DomainDNSZone/)=true OR match_regex(cmd_line, /(?i)Get-DNSZone/)=true OR match_regex(cmd_line, /(?i)Invoke-ReverseDnsLookup/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegCachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-CachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegProxy/)=true OR match_regex(cmd_line, /(?i)Get-Proxy/)=true OR match_regex(cmd_line, /(?i)Invoke-Portscan/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12541,26 +21479,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1039 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Reconnaissance of Credential Stores and Services via Mimikatz modules This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1589.001, T1590.001, T1590.003, T1068, T1078, T1098 +- **ATT&CK**: [T1589.001](https://attack.mitre.org/techniques/T1589.001/), [T1590.001](https://attack.mitre.org/techniques/T1590.001/), [T1590.003](https://attack.mitre.org/techniques/T1590.003/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-03
@@ -12568,21 +21512,32 @@ This detection identifies reconnaissance of credential stores and use of CryptoA #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)crypto::capi/)=true OR match_regex(cmd_line, /(?i)crypto::cng/)=true OR match_regex(cmd_line, /(?i)crypto::providers/)=true OR match_regex(cmd_line, /(?i)crypto::stores/)=true OR match_regex(cmd_line, /(?i)crypto::sc/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)crypto::capi/)=true OR match_regex(cmd_line, /(?i)crypto::cng/)=true OR match_regex(cmd_line, /(?i)crypto::providers/)=true OR match_regex(cmd_line, /(?i)crypto::stores/)=true OR match_regex(cmd_line, /(?i)crypto::sc/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12595,26 +21550,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1098 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + #### Test Dataset + _version_: 1
--- + ### Reconnaissance of Defensive Tools via PowerSploit modules This detection identifies use of PowerSploit modules for assessment of presence of defensive tools. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1595.002, T1592.002 +- **ATT&CK**: [T1595.002](https://attack.mitre.org/techniques/T1595.002/), [T1592.002](https://attack.mitre.org/techniques/T1592.002/) - **Last Updated**: 2020-11-05
@@ -12622,21 +21583,32 @@ This detection identifies use of PowerSploit modules for assessment of presence #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-AVSignature/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-AVSignature/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12645,26 +21617,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1592.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1068, T1078, T1098 +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-05
@@ -12672,21 +21650,32 @@ This detection identifies use of PowerSploit modules for assessment of privilege #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-PrivescAudit/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-PrivescAudit/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12696,26 +21685,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1098 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1543, T1055, T1574 +- **ATT&CK**: [T1543](https://attack.mitre.org/techniques/T1543/), [T1055](https://attack.mitre.org/techniques/T1055/), [T1574](https://attack.mitre.org/techniques/T1574/) - **Last Updated**: 2020-11-05
@@ -12723,21 +21718,32 @@ This detection identifies use of Mimikatz modules for discovery of process or se #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::detours/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::detours/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * _time + * process + * dest_device_id + * dest_user_id + #### ATT&CK | ID | technique | Tactic | @@ -12747,27 +21753,34 @@ You must be ingesting Windows Security logs from devices of interest, including | T1574 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + * https://en.wikipedia.org/wiki/Microsoft_Detours + #### Test Dataset + _version_: 1
--- + ### Reg exe Manipulating Windows Services Registry Keys The search looks for reg.exe modifying registry keys that define Windows services and their configurations. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1574.011 +- **Data Models**: Endpoint +- **ATT&CK**: [T1574.011](https://attack.mitre.org/techniques/T1574.011/) - **Last Updated**: 2020-11-26
@@ -12775,15 +21788,26 @@ The search looks for reg.exe modifying registry keys that define Windows service #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `reg_exe_manipulating_windows_services_registry_keys_filter` ``` #### Associated Analytic Story +* Windows Service Abuse + +* Windows Persistence Techniques + + #### How To Implement To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -12791,26 +21815,32 @@ To successfully implement this search, you must be ingesting data that records r | T1574.011 | x | x | #### Kill Chain Phases + * Installation + #### Known False Positives It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log + _version_: 5
--- + ### Reg exe used to hide files directories via registry keys The search looks for command-line arguments used to hide a file or directory using the reg add command. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1564.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1564.001](https://attack.mitre.org/techniques/T1564.001/) - **Last Updated**: 2019-02-27
@@ -12818,15 +21848,29 @@ The search looks for command-line arguments used to hide a file or directory usi #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)" | `reg_exe_used_to_hide_files_directories_via_registry_keys_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| regex process = "(/d\s+2)" +| `reg_exe_used_to_hide_files_directories_via_registry_keys_filter` ``` #### Associated Analytic Story +* Windows Defense Evasion Tactics + +* Suspicious Windows Registry Activities + +* Windows Persistence Techniques + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -12834,25 +21878,30 @@ You must be ingesting data that records process activity from your hosts to popu | T1564.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None at the moment #### References + #### Test Dataset + _version_: 2
--- + ### Registry Keys Used For Persistence The search looks for modifications to registry keys that can be used to launch an application or service at system startup. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1547.001 +- **ATT&CK**: [T1547.001](https://attack.mitre.org/techniques/T1547.001/) - **Last Updated**: 2020-11-27
@@ -12860,15 +21909,36 @@ The search looks for modifications to registry keys that can be used to launch a #### Search ``` -| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_persistence_filter` + +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `registry_keys_used_for_persistence_filter` ``` #### Associated Analytic Story +* Suspicious Windows Registry Activities + +* Suspicious MSHTA Activity + +* DHS Report TA18-074A + +* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + +* Ransomware + +* Windows Persistence Techniques + +* Emotet Malware DHS Report TA18-201A + + #### How To Implement To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -12876,26 +21946,32 @@ To successfully implement this search, you must be ingesting data that records r | T1547.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/windows-sysmon.log + _version_: 5
--- + ### Registry Keys Used For Privilege Escalation This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1546.012 +- **ATT&CK**: [T1546.012](https://attack.mitre.org/techniques/T1546.012/) - **Last Updated**: 2020-11-27
@@ -12903,15 +21979,28 @@ This search looks for modifications to registry keys that can be used to elevate #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_privilege_escalation_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `registry_keys_used_for_privilege_escalation_filter` ``` #### Associated Analytic Story +* Windows Privilege Escalation + +* Suspicious Windows Registry Activities + +* Cloud Federated Credential Abuse + + #### How To Implement To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -12919,27 +22008,34 @@ To successfully implement this search, you must be ingesting data that records r | T1546.012 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. #### References + * https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### Registry Keys for Creating SHIM Databases This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1546.011 +- **ATT&CK**: [T1546.011](https://attack.mitre.org/techniques/T1546.011/) - **Last Updated**: 2020-11-26
@@ -12947,15 +22043,26 @@ This search looks for registry activity associated with application compatibilit #### Search ``` -| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_for_creating_shim_databases_filter` + +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `registry_keys_for_creating_shim_databases_filter` ``` #### Associated Analytic Story +* Suspicious Windows Registry Activities + +* Windows Persistence Techniques + + #### How To Implement To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response product, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -12963,26 +22070,32 @@ To successfully implement this search, you must populate the Change_Analysis dat | T1546.011 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + _version_: 3
--- + ### Remote Desktop Network Bruteforce This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1021.001 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021.001/) - **Last Updated**: 2020-07-21
@@ -12990,15 +22103,27 @@ This search looks for RDP application network traffic and filters any source/des #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev | `remote_desktop_network_bruteforce_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 +| where count>(avg + stdev*2) +| rename All_Traffic.src AS src All_Traffic.dest AS dest +| table firstTime lastTime src dest count avg p50 stdev +| `remote_desktop_network_bruteforce_filter` ``` #### Associated Analytic Story +* SamSam Ransomware + +* Ryuk Ransomware + + #### How To Implement You must ensure that your network traffic data is populating the Network_Traffic data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13006,26 +22131,32 @@ You must ensure that your network traffic data is populating the Network_Traffic | T1021.001 | x | x | #### Kill Chain Phases + * Reconnaissance + * Delivery + #### Known False Positives RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. #### References + #### Test Dataset + _version_: 2
--- + ### Remote Desktop Network Traffic This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1021.001 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021.001/) - **Last Updated**: 2020-07-07
@@ -13033,15 +22164,30 @@ This search looks for network traffic on TCP/3389, the default port used by remo #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| `drop_dm_object_name("All_Traffic")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `remote_desktop_network_traffic_filter` ``` #### Associated Analytic Story +* SamSam Ransomware + +* Ryuk Ransomware + +* Hidden Cobra Malware + +* Lateral Movement + + #### How To Implement To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13049,25 +22195,30 @@ To successfully implement this search you need to identify systems that commonly | T1021.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Remote Desktop may be used legitimately by users on the network. #### References + #### Test Dataset + _version_: 3
--- + ### Remote Desktop Process Running On System This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1021.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021.001/) - **Last Updated**: 2020-07-21
@@ -13075,15 +22226,26 @@ This search looks for the remote desktop process mstsc.exe running on systems up #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `remote_desktop_process_running_on_system_filter` ``` #### Associated Analytic Story +* Hidden Cobra Malware + +* Lateral Movement + + #### How To Implement To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13091,25 +22253,30 @@ To successfully implement this search, you must be ingesting data that records p | T1021.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Remote Desktop may be used legitimately by users on the network. #### References + #### Test Dataset + _version_: 5
--- + ### Remote Process Instantiation via WMI This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1047 +- **Data Models**: Endpoint +- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) - **Last Updated**: 2020-11-30
@@ -13117,15 +22284,26 @@ This search looks for wmic.exe being launched with parameters to spawn a process #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `remote_process_instantiation_via_wmi_filter` ``` #### Associated Analytic Story +* Ransomware + +* Suspicious WMI Use + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13133,20 +22311,26 @@ You must be ingesting data that records process activity from your hosts to popu | T1047 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + _version_: 5
--- + ### Remote Registry Key modifications This search monitors for remote modifications to registry keys. @@ -13160,40 +22344,58 @@ This search monitors for remote modifications to registry keys. #### Search ``` -| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `remote_registry_key_modifications_filter` + +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `remote_registry_key_modifications_filter` ``` #### Associated Analytic Story +* Windows Defense Evasion Tactics + +* Suspicious Windows Registry Activities + +* Windows Persistence Techniques + + #### How To Implement To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. #### References + #### Test Dataset + _version_: 3 --- + ### Remote WMI Command Attempt This search looks for wmic.exe being launched with parameters to operate on remote systems. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1047 +- **Data Models**: Endpoint +- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) - **Last Updated**: 2018-12-03
@@ -13201,15 +22403,24 @@ This search looks for wmic.exe being launched with parameters to operate on remo #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_wmi_command_attempt_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `remote_wmi_command_attempt_filter` ``` #### Associated Analytic Story +* Suspicious WMI Use + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13217,25 +22428,30 @@ You must be ingesting data that records process activity from your hosts to popu | T1047 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators may use this legitimately to gather info from remote systems. #### References + #### Test Dataset + _version_: 2
--- + ### RunDLL Loading DLL By Ordinal This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2020-11-30
@@ -13243,15 +22459,24 @@ This search looks for executing scripts with rundll32. Adversaries may abuse run #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll_loading_dll_by_ordinal_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `rundll_loading_dll_by_ordinal_filter` ``` #### Associated Analytic Story +* Unusual Processes + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13259,26 +22484,32 @@ You must be ingesting data that records process activity from your hosts to popu | T1218.011 | x | x | #### Kill Chain Phases + * Installation + #### Known False Positives While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### Ryuk Test Files Detected The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1486 +- **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/) - **Last Updated**: 2020-11-06
@@ -13286,15 +22517,24 @@ The search looks for files that contain the key word *Ryuk* under any folder in #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `ryuk_test_files_detected_filter` ``` #### Associated Analytic Story +* Ryuk Ransomware + + #### How To Implement You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13302,26 +22542,32 @@ You must be ingesting data that records the filesystem activity from your hosts | T1486 | x | x | #### Kill Chain Phases + * Delivery + #### Known False Positives If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log + _version_: 1
--- + ### SMB Traffic Spike This search looks for spikes in the number of Server Message Block (SMB) traffic connections. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1021.002 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/) - **Last Updated**: 2020-07-22
@@ -13329,15 +22575,33 @@ This search looks for spikes in the number of Server Message Block (SMB) traffic #### Search ``` -| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter` + +| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src +| `drop_dm_object_name("All_Traffic")` +| eventstats max(_time) as maxtime +| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) +| where isOutlier=1 +| table src count +| `smb_traffic_spike_filter` ``` #### Associated Analytic Story +* Emotet Malware DHS Report TA18-201A + +* Hidden Cobra Malware + +* Ransomware + +* DHS Report TA18-074A + + #### How To Implement This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13345,25 +22609,30 @@ This search requires you to be ingesting your network traffic logs and populatin | T1021.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives A file server may experience high-demand loads that could cause this analytic to trigger. #### References + #### Test Dataset + _version_: 3
--- + ### SMB Traffic Spike - MLTK This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1021.002 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/) - **Last Updated**: 2020-07-22
@@ -13371,10 +22640,29 @@ This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the n #### Search ``` -| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count | `smb_traffic_spike___mltk_filter` + +| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src +| eval HourOfDay=strftime(_time, "%H") +| eval DayOfWeek=strftime(_time, "%A") +| `drop_dm_object_name(All_Traffic)` +| apply smb_pdfmodel threshold=0.001 +| rename "IsOutlier(count)" as isOutlier +| search isOutlier > 0 +| sort -count +| table _time src dest port count +| `smb_traffic_spike___mltk_filter` ``` #### Associated Analytic Story +* Emotet Malware DHS Report TA18-201A + +* Hidden Cobra Malware + +* Ransomware + +* DHS Report TA18-074A + + #### How To Implement To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ @@ -13383,6 +22671,7 @@ Detailed documentation on how to create a new field within Incident Review is fo #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13390,25 +22679,30 @@ Detailed documentation on how to create a new field within Incident Review is fo | T1021.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results #### References + #### Test Dataset + _version_: 3
--- + ### SQL Injection with Long URLs This search looks for long URLs that have several SQL commands visible within them. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1190 +- **Data Models**: Web +- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/) - **Last Updated**: 2020-07-21
@@ -13416,15 +22710,24 @@ This search looks for long URLs that have several SQL commands visible within th #### Search ``` -| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter` + +| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent +| `drop_dm_object_name("Web")` +| eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 +| where num_sql_cmds > 3 +| `sql_injection_with_long_urls_filter` ``` #### Associated Analytic Story +* SQL Injection + + #### How To Implement To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13432,25 +22735,30 @@ To successfully implement this search, you need to be monitoring network communi | T1190 | x | x | #### Kill Chain Phases + * Delivery + #### Known False Positives It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. #### References + #### Test Dataset + _version_: 2
--- + ### Samsam Test File Write The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1486 +- **Data Models**: Endpoint +- **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/) - **Last Updated**: 2018-12-14
@@ -13458,15 +22766,24 @@ The search looks for a file named "test.txt" written to the windows system direc #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `samsam_test_file_write_filter` ``` #### Associated Analytic Story +* SamSam Ransomware + + #### How To Implement You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13474,26 +22791,32 @@ You must be ingesting data that records the file-system activity from your hosts | T1486 | x | x | #### Kill Chain Phases + * Delivery + #### Known False Positives No false positives have been identified. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/sam_sam_note/windows-sysmon.log + _version_: 1
--- + ### Sc exe Manipulating Windows Services This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1543.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1543.003](https://attack.mitre.org/techniques/T1543.003/) - **Last Updated**: 2020-07-21
@@ -13501,15 +22824,34 @@ This search looks for arguments to sc.exe indicating the creation or modificatio #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `sc_exe_manipulating_windows_services_filter` ``` #### Associated Analytic Story +* Windows Service Abuse + +* DHS Report TA18-074A + +* Orangeworm Attack Group + +* Windows Persistence Techniques + +* Disabling Security Tools + +* Sunburst Malware + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13517,26 +22859,32 @@ To successfully implement this search you need to be ingesting information on pr | T1543.003 | x | x | #### Kill Chain Phases + * Installation + #### Known False Positives Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### Scheduled Task Deleted Or Created via CMD This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1053.005 +- **Data Models**: Endpoint +- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/) - **Last Updated**: 2020-12-17
@@ -13544,15 +22892,26 @@ This search looks for flags passed to schtasks.exe on the command-line that indi #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_deleted_or_created_via_cmd_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `scheduled_task_deleted_or_created_via_cmd_filter` ``` #### Associated Analytic Story +* DHS Report TA18-074A + +* Sunburst Malware + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13560,26 +22919,32 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1053.005 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Tasks should not be manually created via CLI, this is rarely done by admins as well #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log + _version_: 5
--- + ### Scheduled tasks used in BadRabbit ransomware This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1053.005 +- **Data Models**: Endpoint +- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/) - **Last Updated**: 2020-07-21
@@ -13587,15 +22952,25 @@ This search looks for flags passed to schtasks.exe on the command-line that indi #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) | `scheduled_tasks_used_in_badrabbit_ransomware_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| search (process=*rhaegal* OR process=*drogon* OR *viserion_*) +| `scheduled_tasks_used_in_badrabbit_ransomware_filter` ``` #### Associated Analytic Story +* Ransomware + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13603,25 +22978,30 @@ You must be ingesting data that records process activity from your hosts to popu | T1053.005 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives No known false positives #### References + #### Test Dataset + _version_: 3
--- + ### Schtasks scheduling job on remote system This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1053.005 +- **Data Models**: Endpoint +- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/) - **Last Updated**: 2020-07-21
@@ -13629,15 +23009,26 @@ This search looks for flags passed to schtasks.exe on the command-line that indi #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `schtasks_scheduling_job_on_remote_system_filter` ``` #### Associated Analytic Story +* Lateral Movement + +* Sunburst Malware + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13645,26 +23036,32 @@ You must be ingesting data that records process activity from your hosts to popu | T1053.005 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### Schtasks used for forcing a reboot This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1053.005 +- **Data Models**: Endpoint +- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/) - **Last Updated**: 2020-12-07
@@ -13672,15 +23069,26 @@ This search looks for flags passed to schtasks.exe on the command-line that indi #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_used_for_forcing_a_reboot_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `schtasks_used_for_forcing_a_reboot_filter` ``` #### Associated Analytic Story +* Windows Persistence Techniques + +* Ransomware + + #### How To Implement To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13688,26 +23096,32 @@ To successfully implement this search you need to be ingesting logs with both th | T1053.005 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_shutdown/windows-sysmon.log + _version_: 4
--- + ### Script Execution via WMI This search looks for scripts launched via WMI. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1047 +- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) - **Last Updated**: 2020-03-16
@@ -13715,15 +23129,24 @@ This search looks for scripts launched via WMI. #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `script_execution_via_wmi_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `script_execution_via_wmi_filter` ``` #### Associated Analytic Story +* Suspicious WMI Use + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13731,26 +23154,32 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1047 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/execution_scrcons/windows-sysmon.log + _version_: 3
--- + ### Setting Credentials via DSInternals modules This detection identifies illegal setting of credentials via DSInternals modules. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1068, T1078, T1098 +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-03
@@ -13758,24 +23187,38 @@ This detection identifies illegal setting of credentials via DSInternals modules #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-ADDBSidHistory/)=true OR match_regex(cmd_line, /(?i)Add-ADReplNgcKey/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPassword/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-ADDBBootKey/)=true OR match_regex(cmd_line, /(?i)Set-SamAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-AzureADUserEx/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-ADDBSidHistory/)=true OR match_regex(cmd_line, /(?i)Add-ADReplNgcKey/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPassword/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-ADDBBootKey/)=true OR match_regex(cmd_line, /(?i)Set-SamAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-AzureADUserEx/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * process_name + * parent_process_name + * _time + * process_path + * dest_user_id + * process + #### ATT&CK | ID | technique | Tactic | @@ -13785,26 +23228,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1098 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/MichaelGrafnetter/DSInternals + #### Test Dataset + _version_: 1
--- + ### Setting Credentials via Mimikatz modules This detection identifies illegal setting of credentials via Mimikatz modules. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1068, T1078, T1098 +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-03
@@ -13812,21 +23261,32 @@ This detection identifies illegal setting of credentials via Mimikatz modules. #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::addsid/)=true OR match_regex(cmd_line, /(?i)CRYPTO::scauth/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::addsid/)=true OR match_regex(cmd_line, /(?i)CRYPTO::scauth/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -13836,26 +23296,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1098 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/gentilkiwi/mimikatz + #### Test Dataset + _version_: 1
--- + ### Setting Credentials via PowerSploit modules This detection identifies illegal setting of credentials via PowerSploit modules. - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1068, T1078, T1098 +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-03
@@ -13863,21 +23329,32 @@ This detection identifies illegal setting of credentials via PowerSploit modules #### Search ``` + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainUserPassword/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainUserPassword/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. #### Required fields + * dest_device_id + * dest_user_id + * process + * _time + #### ATT&CK | ID | technique | Tactic | @@ -13887,26 +23364,32 @@ You must be ingesting Windows Security logs from devices of interest, including | T1098 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified. #### References + * https://github.com/PowerShellMafia/PowerSploit + #### Test Dataset + _version_: 1
--- + ### Shim Database File Creation This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1546.011 +- **ATT&CK**: [T1546.011](https://attack.mitre.org/techniques/T1546.011/) - **Last Updated**: 2020-12-08
@@ -13914,15 +23397,24 @@ This search looks for shim database files being written to default directories. #### Search ``` -| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter` + +| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +|`drop_dm_object_name(Filesystem)` +| `shim_database_file_creation_filter` ``` #### Associated Analytic Story +* Windows Persistence Techniques + + #### How To Implement You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13930,26 +23422,32 @@ You must be ingesting data that records the filesystem activity from your hosts | T1546.011 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + _version_: 3
--- + ### Shim Database Installation With Suspicious Parameters This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1546.011 +- **Data Models**: Endpoint +- **ATT&CK**: [T1546.011](https://attack.mitre.org/techniques/T1546.011/) - **Last Updated**: 2020-11-23
@@ -13957,15 +23455,24 @@ This search detects the process execution and arguments required to silently cre #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `shim_database_installation_with_suspicious_parameters_filter` ``` #### Associated Analytic Story +* Windows Persistence Techniques + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -13973,26 +23480,32 @@ You must be ingesting data that records process activity from your hosts to popu | T1546.011 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### Short Lived Windows Accounts This search detects accounts that were created and deleted in a short time period. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1136.001 +- **Data Models**: Change +- **ATT&CK**: [T1136.001](https://attack.mitre.org/techniques/T1136.001/) - **Last Updated**: 2020-07-06
@@ -14000,15 +23513,27 @@ This search detects accounts that were created and deleted in a short time perio #### Search ``` -| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 | transaction user connected=false maxspan=240m | table firstTime lastTime count user dest result_id | `short_lived_windows_accounts_filter` + +| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name("All_Changes")` +| search result_id = 4720 result_id=4726 +| transaction user connected=false maxspan=240m +| table firstTime lastTime count user dest result_id +| `short_lived_windows_accounts_filter` ``` #### Associated Analytic Story +* Account Monitoring and Controls + + #### How To Implement This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/ #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14017,26 +23542,33 @@ This search requires you to have enabled your Group Management Audit Logs in you #### Kill Chain Phases + #### Known False Positives It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + _version_: 2
--- + ### Single Letter Process On Endpoint This search looks for process names that consist only of a single letter. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1204.002 +- **Data Models**: Endpoint +- **ATT&CK**: [T1204.002](https://attack.mitre.org/techniques/T1204.002/) - **Last Updated**: 2020-12-08
@@ -14044,15 +23576,27 @@ This search looks for process names that consist only of a single letter. #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name | `single_letter_process_on_endpoint_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name +| `drop_dm_object_name(Processes)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) +| search process_name_length=5 AND endExe=1 +| table count, firstTime, lastTime, dest, user, process, process_name +| `single_letter_process_on_endpoint_filter` ``` #### Associated Analytic Story +* DHS Report TA18-074A + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14060,25 +23604,31 @@ You must be ingesting data that records process activity from your hosts to popu | T1204.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/single_letter_exe/windows-sysmon.log + _version_: 3
--- + ### Spectre and Meltdown Vulnerable Systems The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Data Models**: Vulnerabilities - **ATT&CK**: - **Last Updated**: 2017-01-07 @@ -14087,15 +23637,24 @@ The search is used to detect systems that are still vulnerable to the Spectre an #### Search ``` -| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest | `drop_dm_object_name(Vulnerabilities)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spectre_and_meltdown_vulnerable_systems_filter` + +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest +| `drop_dm_object_name(Vulnerabilities)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `spectre_and_meltdown_vulnerable_systems_filter` ``` #### Associated Analytic Story +* Spectre And Meltdown Vulnerabilities + + #### How To Implement The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14103,17 +23662,21 @@ The search requires that you are ingesting your vulnerability-scanner data and t #### Kill Chain Phases + #### Known False Positives It is possible that your vulnerability scanner is not detecting that the patches have been applied. #### References + #### Test Dataset + _version_: 1 --- + ### Spike in File Writes The search looks for a sharp increase in the number of files written to a particular host @@ -14127,34 +23690,54 @@ The search looks for a sharp increase in the number of files written to a partic #### Search ``` -| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter` + +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest +| `drop_dm_object_name(Filesystem)` +| eventstats max(_time) as maxtime +| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) +| search isOutlier=1 +| `spike_in_file_writes_filter` ``` #### Associated Analytic Story +* SamSam Ransomware + +* Ryuk Ransomware + +* Ransomware + + #### How To Implement In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. #### References + #### Test Dataset + _version_: 3 --- + ### Splunk Enterprise Information Disclosure This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. @@ -14168,40 +23751,55 @@ This search allows you to look for evidence of exploitation for CVE-2018-11409, #### Search ``` -index=_internal sourcetype=splunkd_ui_access server-info | search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" | rename clientip as src_ip, splunk_server as dest | stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_enterprise_information_disclosure_filter` +index=_internal sourcetype=splunkd_ui_access server-info +| search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" +| rename clientip as src_ip, splunk_server as dest +| stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `splunk_enterprise_information_disclosure_filter` ``` #### Associated Analytic Story +* Splunk Enterprise Vulnerability CVE-2018-11409 + + #### How To Implement The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Delivery + #### Known False Positives Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information. #### References + #### Test Dataset + _version_: 1 --- + ### Sunburst Correlation DLL and Network Event The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1203 +- **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/) - **Last Updated**: 2020-12-14
@@ -14209,15 +23807,26 @@ The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost #### Search ``` -(`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) | eventstats dc(EventCode) AS dc_events | where dc_events=2 | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host | rename host as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `sunburst_correlation_dll_and_network_event_filter` +(`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) +| eventstats dc(EventCode) AS dc_events +| where dc_events=2 +| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host +| rename host as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `sunburst_correlation_dll_and_network_event_filter` ``` #### Associated Analytic Story +* Sunburst Malware + + #### How To Implement This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14225,26 +23834,32 @@ This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please | T1203 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives unknown #### References + * https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html + #### Test Dataset + _version_: 1
--- + ### Supernova Webshell This search aims to detect the Supernova webshell used in the SUNBURST attack. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1505.003 +- **Data Models**: Web +- **ATT&CK**: [T1505.003](https://attack.mitre.org/techniques/T1505.003/) - **Last Updated**: 2021-01-06
@@ -14252,15 +23867,21 @@ This search aims to detect the Supernova webshell used in the SUNBURST attack. #### Search ``` -| tstats `security_content_summariesonly` count from datamodel=Web.Web where web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s | `supernova_webshell_filter` + +| tstats `security_content_summariesonly` count from datamodel=Web.Web where web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s +| `supernova_webshell_filter` ``` #### Associated Analytic Story +* Sunburst Malware + + #### How To Implement To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14268,27 +23889,34 @@ To successfully implement this search, you need to be monitoring web traffic to | T1505.003 | x | x | #### Kill Chain Phases + * Exfiltration + #### Known False Positives There might be false positives associted with this detection since items like args as a web argument is pretty generic. #### References + * https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html + * https://www.guidepointsecurity.com/supernova-solarwinds-net-webshell-analysis/ + #### Test Dataset + _version_: 1
--- + ### Suspicious Changes to File Associations This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1546.001 +- **ATT&CK**: [T1546.001](https://attack.mitre.org/techniques/T1546.001/) - **Last Updated**: 2020-07-22
@@ -14296,15 +23924,30 @@ This search looks for changes to registry values that control Windows file assoc #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table process_id dest registry_path]| `suspicious_changes_to_file_associations_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| join [ +| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest +| `drop_dm_object_name("Registry")` +| table process_id dest registry_path] +| `suspicious_changes_to_file_associations_filter` ``` #### Associated Analytic Story +* Suspicious Windows Registry Activities + +* Windows File Extension and Association Abuse + + #### How To Implement To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14312,25 +23955,30 @@ To successfully implement this search you need to be ingesting information on re | T1546.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. #### References + #### Test Dataset + _version_: 4
--- + ### Suspicious Email - UBA Anomaly This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1566 +- **Data Models**: UEBA +- **ATT&CK**: [T1566](https://attack.mitre.org/techniques/T1566/) - **Last Updated**: 2020-07-22
@@ -14338,15 +23986,25 @@ This detection looks for emails that are suspicious because of their sender, dom #### Search ``` -|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model | `drop_dm_object_name(All_UEBA_Events)` | `drop_dm_object_name(UEBA_Anomalies)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_email___uba_anomaly_filter` + +|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model +| `drop_dm_object_name(All_UEBA_Events)` +| `drop_dm_object_name(UEBA_Anomalies)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_email___uba_anomaly_filter` ``` #### Associated Analytic Story +* Suspicious Emails + + #### How To Implement You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14354,25 +24012,30 @@ You must be ingesting data from email logs and have Splunk integrated with UBA. | T1566 | x | x | #### Kill Chain Phases + * Delivery + #### Known False Positives This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and add the URL to an allow list if you determine that it is a legitimate sender. #### References + #### Test Dataset + _version_: 3
--- + ### Suspicious Email Attachment Extensions This search looks for emails that have attachments with suspicious file extensions. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1566.001 +- **Data Models**: Email +- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/) - **Last Updated**: 2020-07-22
@@ -14380,10 +24043,21 @@ This search looks for emails that have attachments with suspicious file extensio #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` | `suspicious_email_attachment_extensions_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Email")` +| `suspicious_email_attachments` +| `suspicious_email_attachment_extensions_filter` ``` #### Associated Analytic Story +* Emotet Malware DHS Report TA18-201A + +* Suspicious Emails + + #### How To Implement You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ **Splunk Phantom Playbook Integration**\ @@ -14391,6 +24065,7 @@ If Splunk Phantom is also configured in your environment, a Playbook called "Sus #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14398,19 +24073,24 @@ If Splunk Phantom is also configured in your environment, a Playbook called "Sus | T1566.001 | x | x | #### Kill Chain Phases + * Delivery + #### Known False Positives None identified #### References + #### Test Dataset + _version_: 3
--- + ### Suspicious File Write The search looks for files created with names that have been linked to malicious activity. @@ -14424,34 +24104,49 @@ The search looks for files created with names that have been linked to malicious #### Search ``` -| tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` | `suspicious_writes` | `suspicious_file_write_filter` + +| tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Filesystem)` +| `suspicious_writes` +| `suspicious_file_write_filter` ``` #### Associated Analytic Story +* Hidden Cobra Malware + + #### How To Implement You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. #### References + #### Test Dataset + _version_: 3 --- + ### Suspicious Java Classes This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. @@ -14465,40 +24160,56 @@ This search looks for suspicious Java classes that are often used to exploit rem #### Search ``` -`stream_http` http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_java_classes_filter` +`stream_http` http_method=POST http_content_length>1 +| regex form_data="(?i)java\.lang\.(?:runtime +|processbuilder)" +| rename src_ip as src +| stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_java_classes_filter` ``` #### Associated Analytic Story +* Apache Struts Vulnerability + + #### How To Implement In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Exploitation + #### Known False Positives There are no known false positives. #### References + #### Test Dataset + _version_: 1 --- + ### Suspicious MSBuild Rename The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1127.001, T1036.003 +- **ATT&CK**: [T1127.001](https://attack.mitre.org/techniques/T1127.001/), [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2021-01-12
@@ -14506,15 +24217,24 @@ The following analytic identifies renamed instances of msbuild.exe executing. Ms #### Search ``` -`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_msbuild_rename_filter` +`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_msbuild_rename_filter` ``` #### Associated Analytic Story +* Trusted Developer Utilities Proxy Execution MSBuild + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14523,29 +24243,38 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1036.003 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive. #### References + * https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + * https://github.com/infosecn1nja/MaliciousMacroMSBuild/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + _version_: 1
--- + ### Suspicious MSBuild Spawn The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1127.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1127.001](https://attack.mitre.org/techniques/T1127.001/) - **Last Updated**: 2021-01-12
@@ -14553,15 +24282,24 @@ The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavi #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_msbuild_spawn_filter` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_msbuild_spawn_filter` ``` #### Associated Analytic Story +* Trusted Developer Utilities Proxy Execution MSBuild + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14569,28 +24307,36 @@ To successfully implement this search you need to be ingesting information on pr | T1127.001 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. #### References + * https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + _version_: 1
--- + ### Suspicious Reg exe Process This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1112 +- **ATT&CK**: [T1112](https://attack.mitre.org/techniques/T1112/) - **Last Updated**: 2020-07-22
@@ -14598,15 +24344,36 @@ This search looks for reg.exe being launched from a command prompt not started b #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup process_id| table process_id dest] | `suspicious_reg_exe_process_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search [ +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename parent_process_id as process_id +|dedup process_id +| table process_id dest] +| `suspicious_reg_exe_process_filter` ``` #### Associated Analytic Story +* Windows Defense Evasion Tactics + +* Disabling Security Tools + +* DHS Report TA18-074A + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14614,27 +24381,34 @@ You must be ingesting data that records process activity from your hosts to popu | T1112 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. #### References + * https://car.mitre.org/wiki/CAR-2013-03-001 + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log + _version_: 4
--- + ### Suspicious Regsvr32 Register Suspicious Path Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.010 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.010](https://attack.mitre.org/techniques/T1218.010/) - **Last Updated**: 2021-01-28
@@ -14642,15 +24416,24 @@ Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_regsvr32_register_suspicious_path_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_regsvr32_register_suspicious_path_filter` ``` #### Associated Analytic Story +* Suspicious Regsvr32 Activity + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14658,31 +24441,42 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1218.010 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues. #### References + * https://attack.mitre.org/techniques/T1218/010/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md + * https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ + * https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 + * https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718 + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Suspicious Rundll32 Rename The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1218.011, T1036.003 +- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/), [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2021-02-04
@@ -14690,15 +24484,24 @@ The following analytic identifies renamed instances of rundll32.exe executing. r #### Search ``` -`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_rundll32_rename_filter` +`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_rundll32_rename_filter` ``` #### Associated Analytic Story +* Suspicious Rundll32 Activity + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14707,29 +24510,38 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1036.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. #### References + * https://attack.mitre.org/techniques/T1218/011/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + * https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Suspicious Rundll32 StartW The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-04
@@ -14737,15 +24549,26 @@ The following analytic identifies rundll32.exe executing a DLL function name, St #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_startw_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_rundll32_startw_filter` ``` #### Associated Analytic Story +* Suspicious Rundll32 Activity + +* Cobalt Strike + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14753,31 +24576,42 @@ To successfully implement this search you need to be ingesting information on pr | T1218.011 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed. #### References + * https://attack.mitre.org/techniques/T1218/011/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + * https://www.cobaltstrike.com/help-windows-executable + * https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + * https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Suspicious Rundll32 dllregisterserver The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.011 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-09
@@ -14785,15 +24619,24 @@ The following analytic identifies rundll32.exe using dllregisterserver on the co #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_dllregisterserver_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_rundll32_dllregisterserver_filter` ``` #### Associated Analytic Story +* Suspicious Rundll32 Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14801,33 +24644,46 @@ To successfully implement this search you need to be ingesting information on pr | T1218.011 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names. #### References + * https://attack.mitre.org/techniques/T1218/011/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + * https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + * https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/seedworm-apt-iran-middle-east + * https://github.com/pan-unit42/tweets/blob/master/2020-12-10-IOCs-from-Ursnif-infection-with-Delf-variant.txt + * https://www.crowdstrike.com/blog/duck-hunting-with-falcon-complete-qakbot-zip-based-campaign/ + * https://msdn.microsoft.com/en-us/library/windows/desktop/ms682162(v=vs.85).aspx + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Suspicious Rundll32 no CommandLine Arguments The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1218.011 +- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-09
@@ -14835,15 +24691,27 @@ The following analytic identifies rundll32.exe with no command line arguments. I #### Search ``` -`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) | regex CommandLine="(rundll32\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_no_commandline_arguments_filter` +`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) +| regex CommandLine="(rundll32\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_rundll32_no_commandline_arguments_filter` ``` #### Associated Analytic Story +* Suspicious Rundll32 Activity + +* Cobalt Strike + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14851,30 +24719,40 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1218.011 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. #### References + * https://attack.mitre.org/techniques/T1218/011/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + * https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + * https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Suspicious microsoft workflow compiler rename The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1127, T1036.003 +- **ATT&CK**: [T1127, T1036.003](https://attack.mitre.org/techniques/T1127, T1036.003/) - **Last Updated**: 2021-01-12
@@ -14882,15 +24760,24 @@ The following analytic identifies a renamed instance of microsoft.workflow.compi #### Search ``` -`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_microsoft_workflow_compiler_rename_filter` +`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_microsoft_workflow_compiler_rename_filter` ``` #### Associated Analytic Story +* Trusted Developer Utilities Proxy Execution + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14898,28 +24785,36 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1127, T1036.003 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive. #### References + * https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log + _version_: 1
--- + ### Suspicious microsoft workflow compiler usage The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1127 +- **Data Models**: Endpoint +- **ATT&CK**: [T1127](https://attack.mitre.org/techniques/T1127/) - **Last Updated**: 2021-01-12
@@ -14927,15 +24822,24 @@ The following analytic identifies microsoft.workflow.compiler.exe usage. microso #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_usage_filter` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_microsoft_workflow_compiler_usage_filter` ``` #### Associated Analytic Story +* Trusted Developer Utilities Proxy Execution + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14943,28 +24847,36 @@ To successfully implement this search you need to be ingesting information on pr | T1127 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM. #### References + * https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log + _version_: 1
--- + ### Suspicious msbuild path The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1127.001, T1036.003 +- **Data Models**: Endpoint +- **ATT&CK**: [T1127.001](https://attack.mitre.org/techniques/T1127.001/), [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2021-01-12
@@ -14972,15 +24884,24 @@ The following analytic identifies msbuild.exe executing from a non-standard path #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `suspicious_msbuild_path_filter` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_msbuild_path_filter` ``` #### Associated Analytic Story +* Trusted Developer Utilities Proxy Execution MSBuild + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -14989,28 +24910,36 @@ To successfully implement this search you need to be ingesting information on pr | T1036.003 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on. #### References + * https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + _version_: 1
--- + ### Suspicious mshta child process The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.005 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-12
@@ -15018,15 +24947,24 @@ The following analytic identifies child processes spawning from "mshta.exe". Th #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_child_process_filter` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_mshta_child_process_filter` ``` #### Associated Analytic Story +* Suspicious MSHTA Activity + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15034,28 +24972,36 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1218.005 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. #### References + * https://github.com/redcanaryco/AtomicTestHarnesses + * https://redcanary.com/blog/introducing-atomictestharnesses/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Suspicious mshta spawn The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1218.005 +- **Data Models**: Endpoint +- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-20
@@ -15063,15 +25009,24 @@ The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_spawn_filter` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_mshta_spawn_filter` ``` #### Associated Analytic Story +* Suspicious MSHTA Activity + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15079,29 +25034,38 @@ To successfully implement this search you need to be ingesting information on pr | T1218.005 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. #### References + * https://codewhitesec.blogspot.com/2018/07/lethalhta.html + * https://github.com/redcanaryco/AtomicTestHarnesses + * https://redcanary.com/blog/introducing-atomictestharnesses/ + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Suspicious wevtutil Usage The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1070.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1070.001](https://attack.mitre.org/techniques/T1070.001/) - **Last Updated**: 2020-07-22
@@ -15109,15 +25073,26 @@ The wevtutil.exe application is the windows event log utility. This searches for #### Search ``` -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `suspicious_wevtutil_usage_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `suspicious_wevtutil_usage_filter` ``` #### Associated Analytic Story +* Windows Log Manipulation + +* Ransomware + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15125,26 +25100,32 @@ You must be ingesting data that records process activity from your hosts to popu | T1070.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-sysmon.log + _version_: 3
--- + ### Suspicious writes to System Volume Information This search detects writes to the 'System Volume Information' folder by something other than the System process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1036 +- **ATT&CK**: [T1036](https://attack.mitre.org/techniques/T1036/) - **Last Updated**: 2020-07-22
@@ -15152,15 +25133,23 @@ This search detects writes to the 'System Volume Information' folder by somethin #### Search ``` -(`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* | stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_writes_to_system_volume_information_filter` +(`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* +| stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_writes_to_system_volume_information_filter` ``` #### Associated Analytic Story +* Collection and Staging + + #### How To Implement You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15169,23 +25158,27 @@ You need to be ingesting logs with both the process name and command-line from y #### Kill Chain Phases + #### Known False Positives It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. #### References + #### Test Dataset + _version_: 2
--- + ### Suspicious writes to windows Recycle Bin This search detects writes to the recycle bin by a process other than explorer.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1036 +- **ATT&CK**: [T1036](https://attack.mitre.org/techniques/T1036/) - **Last Updated**: 2020-07-22
@@ -15193,15 +25186,26 @@ This search detects writes to the recycle bin by a process other than explorer.e #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`| search [| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")` | table process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest +| `drop_dm_object_name("Filesystem")` +| search [ +| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest +| `drop_dm_object_name("Processes")` +| table process_id dest] +| `suspicious_writes_to_windows_recycle_bin_filter` ``` #### Associated Analytic Story +* Collection and Staging + + #### How To Implement To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15210,24 +25214,29 @@ To successfully implement this search you need to be ingesting information on fi #### Kill Chain Phases + #### Known False Positives Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/write_to_recycle_bin/windows-sysmon.log + _version_: 4
--- + ### System Information Discovery Detection Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1082 +- **Data Models**: Endpoint +- **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/) - **Last Updated**: 2020-10-12
@@ -15235,15 +25244,27 @@ Detect system information discovery techniques used by attackers to understand c #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest by dest | where dc_processes_by_dest > 2 | stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| eventstats dc(process) as dc_processes_by_dest by dest +| where dc_processes_by_dest > 2 +| stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `system_information_discovery_detection_filter` ``` #### Associated Analytic Story +* Discovery Techniques + + #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15251,27 +25272,34 @@ To successfully implement this search you need to be ingesting information on pr | T1082 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators debugging servers #### References + * https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### System Process Running from Unexpected Location An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml - **Product**: UEBA for Security Cloud - **Data Models**: -- **ATT&CK**: T1036 +- **ATT&CK**: [T1036](https://attack.mitre.org/techniques/T1036/) - **Last Updated**: 2020-08-25
@@ -15279,27 +25307,57 @@ An attacker tries might try to use different version of a system command without #### Search ``` - $ssa_input = | from read_ssa_enriched_events() | eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), user=ucast(map_get(input_event, "dest_user_id"), "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=lower(ucast(map_get(input_event, "process_path"), "string", null)); -$cond_1 = | from $ssa_input | where process_name="arp.exe" OR process_name="adaptertroubleshooter.exe" OR process_name="applicationframehost.exe" OR process_name="atbroker.exe" OR process_name="authhost.exe" OR process_name="autoworkplace.exe" OR process_name="axinstui.exe" OR process_name="backgroundtransferhost.exe" OR process_name="bdehdcfg.exe" OR process_name="bdeuisrv.exe" OR process_name="bdeunlockwizard.exe" OR process_name="bitlockerdeviceencryption.exe" OR process_name="bitlockerwizard.exe" OR process_name="bitlockerwizardelev.exe" OR process_name="bytecodegenerator.exe" OR process_name="camerasettingsuihost.exe" OR process_name="castsrv.exe" OR process_name="certenrollctrl.exe" OR process_name="checknetisolation.exe" OR process_name="clipup.exe" OR process_name="cloudexperiencehostbroker.exe" OR process_name="cloudnotifications.exe" OR process_name="cloudstoragewizard.exe" OR process_name="compmgmtlauncher.exe" OR process_name="compattelrunner.exe" OR process_name="computerdefaults.exe" OR process_name="credentialuibroker.exe" OR process_name="dfdwiz.exe" OR process_name="dwwin.exe" OR process_name="dataexchangehost.exe" OR process_name="defrag.exe" OR process_name="devicedisplayobjectprovider.exe" OR process_name="deviceeject.exe" OR process_name="deviceenroller.exe" OR process_name="devicepairingwizard.exe" OR process_name="deviceproperties.exe" OR process_name="disksnapshot.exe" OR process_name="dism.exe" OR process_name="displayswitch.exe" OR process_name="dmnotificationbroker.exe" OR process_name="dmomacpmo.exe" OR process_name="dpiscaling.exe" OR process_name="dsmusertask.exe" OR process_name="dxpserver.exe" OR process_name="edpcleanup.exe" OR process_name="eosnotify.exe" OR process_name="eap3host.exe" OR process_name="easpoliciesbrokerhost.exe" OR process_name="easeofaccessdialog.exe" OR process_name="ehstorauthn.exe" OR process_name="fxscover.exe" OR process_name="fxssvc.exe" OR process_name="fxsunatd.exe" OR process_name="filehistory.exe" OR process_name="fondue.exe" OR process_name="gamepanel.exe" OR process_name="genvalobj.exe" OR process_name="gettingstarted.exe" OR process_name="hostname.exe" OR process_name="icsentitlementhost.exe" OR process_name="infdefaultinstall.exe" OR process_name="installagent.exe" OR process_name="languagecomponentsinstallercomhandler.exe" OR process_name="launchtm.exe" OR process_name="launchwinapp.exe" OR process_name="legacynetuxhost.exe" OR process_name="licensemanagershellext.exe" OR process_name="licensingui.exe" OR process_name="locationnotificationwindows.exe" OR process_name="locationnotifications.exe" OR process_name="locator.exe" OR process_name="lockapphost.exe" OR process_name="lockscreencontentserver.exe" OR process_name="logonui.exe" OR process_name="lsaiso.exe" OR process_name="mdeserver.exe" OR process_name="mdmagent.exe" OR process_name="mdmappinstaller.exe" OR process_name="mrinfo.exe" OR process_name="mrt.exe" OR process_name="mschedexe.exe" OR process_name="magnify.exe" OR process_name="mbaeparsertask.exe" OR process_name="mdres.exe" OR process_name="mdsched.exe" OR process_name="migautoplay.exe" OR process_name="mpsigstub.exe" OR process_name="msspellcheckinghost.exe" OR process_name="muiunattend.exe" OR process_name="multidigimon.exe" OR process_name="musnotification.exe" OR process_name="musnotificationux.exe" OR process_name="napstat.exe" OR process_name="netstat.exe" OR process_name="narrator.exe" OR process_name="netcfgnotifyobjecthost.exe" OR process_name="netevtfwdr.exe" OR process_name="netproj.exe" OR process_name="netplwiz.exe" OR process_name="networkuxbroker.exe"; -$cond_2 = | from $ssa_input | where process_name="openwith.exe" OR process_name="optionalfeatures.exe" OR process_name="pathping.exe" OR process_name="ping.exe" OR process_name="passwordonwakesettingflyout.exe" OR process_name="pickerhost.exe" OR process_name="pkgmgr.exe" OR process_name="pnpunattend.exe" OR process_name="pnputil.exe" OR process_name="presentationhost.exe" OR process_name="presentationsettings.exe" OR process_name="printbrmui.exe" OR process_name="printdialoghost.exe" OR process_name="printdialoghost3d.exe" OR process_name="printisolationhost.exe" OR process_name="proximityuxhost.exe" OR process_name="rdspnf.exe" OR process_name="rmactivate.exe" OR process_name="rmactivate_isv.exe" OR process_name="rmactivate_ssp.exe" OR process_name="rmactivate_ssp_isv.exe" OR process_name="route.exe" OR process_name="rdpsa.exe" OR process_name="rdpsaproxy.exe" OR process_name="rdpsauachelper.exe" OR process_name="reagentc.exe" OR process_name="recoverydrive.exe" OR process_name="register-cimprovider.exe" OR process_name="registeriepkeys.exe" OR process_name="relpost.exe" OR process_name="remoteposworker.exe" OR process_name="rmclient.exe" OR process_name="robocopy.exe" OR process_name="rpcping.exe" OR process_name="runlegacycplelevated.exe" OR process_name="runtimebroker.exe" OR process_name="sihclient.exe" OR process_name="searchfilterhost.exe" OR process_name="searchindexer.exe" OR process_name="searchprotocolhost.exe" OR process_name="secedit.exe" OR process_name="sensordataservice.exe" OR process_name="setieinstalleddate.exe" OR process_name="settingsynchost.exe" OR process_name="slidetoshutdown.exe" OR process_name="smartscreensettings.exe" OR process_name="sndvol.exe" OR process_name="snippingtool.exe" OR process_name="soundrecorder.exe" OR process_name="spaceagent.exe" OR process_name="sppextcomobj.exe" OR process_name="srtasks.exe" OR process_name="stikynot.exe" OR process_name="synchost.exe" OR process_name="sysreseterr.exe" OR process_name="systempropertiesadvanced.exe" OR process_name="systempropertiescomputername.exe" OR process_name="systempropertiesdataexecutionprevention.exe" OR process_name="systempropertieshardware.exe" OR process_name="systempropertiesperformance.exe" OR process_name="systempropertiesprotection.exe" OR process_name="systempropertiesremote.exe" OR process_name="systemsettingsadminflows.exe" OR process_name="systemsettingsbroker.exe" OR process_name="systemsettingsremovedevice.exe" OR process_name="tcpsvcs.exe" OR process_name="tracert.exe" OR process_name="tstheme.exe" OR process_name="tswbprxy.exe" OR process_name="tapiunattend.exe" OR process_name="taskmgr.exe" OR process_name="thumbnailextractionhost.exe" OR process_name="tokenbrokercookies.exe" OR process_name="tpminit.exe" OR process_name="tswpfwrp.exe" OR process_name="ui0detect.exe" OR process_name="upgraderesultsui.exe" OR process_name="useraccountbroker.exe" OR process_name="useraccountcontrolsettings.exe" OR process_name="usoclient.exe" OR process_name="utilman.exe" OR process_name="vssvc.exe" OR process_name="vaultcmd.exe" OR process_name="vaultsysui.exe" OR process_name="wfs.exe" OR process_name="wmpdmc.exe" OR process_name="wpdshextautoplay.exe" OR process_name="wscollect.exe" OR process_name="wsmanhttpconfig.exe" OR process_name="wsreset.exe" OR process_name="wudfhost.exe" OR process_name="wwahost.exe" OR process_name="wallpaperhost.exe" OR process_name="webcache.exe" OR process_name="werfault.exe" OR process_name="werfaultsecure.exe" OR process_name="winsat.exe" OR process_name="windows.media.backgroundplayback.exe" OR process_name="windowsactiondialog.exe" OR process_name="windowsanytimeupgrade.exe" OR process_name="windowsanytimeupgraderesults.exe"; -$cond_3 = | from $ssa_input | where process_name="windowsanytimeupgradeui.exe" OR process_name="windowsupdateelevatedinstaller.exe" OR process_name="workfolders.exe" OR process_name="wpcmon.exe" OR process_name="acu.exe" OR process_name="aitagent.exe" OR process_name="aitstatic.exe" OR process_name="alg.exe" OR process_name="appidcertstorecheck.exe" OR process_name="appidpolicyconverter.exe" OR process_name="at.exe" OR process_name="attrib.exe" OR process_name="audiodg.exe" OR process_name="auditpol.exe" OR process_name="autochk.exe" OR process_name="autoconv.exe" OR process_name="autofmt.exe" OR process_name="baaupdate.exe" OR process_name="backgroundtaskhost.exe" OR process_name="bcastdvr.exe" OR process_name="bcdboot.exe" OR process_name="bcdedit.exe" OR process_name="bdechangepin.exe" OR process_name="bdeunlock.exe" OR process_name="bitsadmin.exe" OR process_name="bootcfg.exe" OR process_name="bootim.exe" OR process_name="bootsect.exe" OR process_name="bridgeunattend.exe" OR process_name="browser_broker.exe" OR process_name="bthudtask.exe" OR process_name="cacls.exe" OR process_name="calc.exe" OR process_name="cdpreference.exe" OR process_name="certreq.exe" OR process_name="certutil.exe" OR process_name="change.exe" OR process_name="changepk.exe" OR process_name="charmap.exe" OR process_name="chglogon.exe" OR process_name="chgport.exe" OR process_name="chgusr.exe" OR process_name="chkdsk.exe" OR process_name="chkntfs.exe" OR process_name="choice.exe" OR process_name="cipher.exe" OR process_name="cleanmgr.exe" OR process_name="cliconfg.exe" OR process_name="clip.exe" OR process_name="cmd.exe" OR process_name="cmdkey.exe" OR process_name="cmdl32.exe" OR process_name="cmmon32.exe" OR process_name="cmstp.exe" OR process_name="cofire.exe" OR process_name="colorcpl.exe" OR process_name="comp.exe" OR process_name="compact.exe" OR process_name="conhost.exe" OR process_name="consent.exe" OR process_name="control.exe" OR process_name="convert.exe" OR process_name="credwiz.exe" OR process_name="cscript.exe" OR process_name="csrss.exe" OR process_name="ctfmon.exe" OR process_name="cttune.exe" OR process_name="cttunesvr.exe" OR process_name="dashost.exe" OR process_name="dccw.exe" OR process_name="dcomcnfg.exe" OR process_name="ddodiag.exe" OR process_name="dfrgui.exe" OR process_name="dialer.exe" OR process_name="diantz.exe" OR process_name="dinotify.exe" OR process_name="diskpart.exe" OR process_name="diskperf.exe" OR process_name="diskraid.exe" OR process_name="dispdiag.exe" OR process_name="djoin.exe" OR process_name="dllhost.exe" OR process_name="dllhst3g.exe" OR process_name="dmcertinst.exe" OR process_name="dmcfghost.exe" OR process_name="dmclient.exe" OR process_name="dnscacheugc.exe" OR process_name="doskey.exe" OR process_name="dpapimig.exe" OR process_name="dpnsvr.exe" OR process_name="driverquery.exe" OR process_name="drvcfg.exe" OR process_name="drvinst.exe" OR process_name="dsregcmd.exe" OR process_name="dstokenclean.exe" OR process_name="dvdplay.exe" OR process_name="dvdupgrd.exe" OR process_name="dwm.exe" OR process_name="dxdiag.exe" OR process_name="easinvoker.exe" OR process_name="efsui.exe"; -$cond_4 = | from $ssa_input | where process_name="embeddedapplauncher.exe" OR process_name="esentutl.exe" OR process_name="eudcedit.exe" OR process_name="eventcreate.exe" OR process_name="eventvwr.exe" OR process_name="expand.exe" OR process_name="extrac32.exe" OR process_name="fc.exe" OR process_name="fhmanagew.exe" OR process_name="find.exe" OR process_name="findstr.exe" OR process_name="finger.exe" OR process_name="fixmapi.exe" OR process_name="fltmc.exe" OR process_name="fodhelper.exe" OR process_name="fontdrvhost.exe" OR process_name="fontview.exe" OR process_name="forfiles.exe" OR process_name="fsavailux.exe" OR process_name="fsquirt.exe" OR process_name="fsutil.exe" OR process_name="ftp.exe" OR process_name="fvenotify.exe" OR process_name="fveprompt.exe" OR process_name="getmac.exe" OR process_name="gpresult.exe" OR process_name="gpscript.exe" OR process_name="gpupdate.exe" OR process_name="grpconv.exe" OR process_name="hdwwiz.exe" OR process_name="help.exe" OR process_name="hwrcomp.exe" OR process_name="hwrreg.exe" OR process_name="icacls.exe" OR process_name="icardagt.exe" OR process_name="icsunattend.exe" OR process_name="ie4uinit.exe" OR process_name="ieunatt.exe" OR process_name="ieetwcollector.exe" OR process_name="iexpress.exe" OR process_name="immersivetpmvscmgrsvr.exe" OR process_name="ipconfig.exe" OR process_name="irftp.exe" OR process_name="iscsicli.exe" OR process_name="iscsicpl.exe" OR process_name="isoburn.exe" OR process_name="klist.exe" OR process_name="ksetup.exe" OR process_name="ktmutil.exe" OR process_name="label.exe" OR process_name="licensingdiag.exe" OR process_name="lodctr.exe" OR process_name="logagent.exe" OR process_name="logman.exe" OR process_name="logoff.exe" OR process_name="lpkinstall.exe" OR process_name="lpksetup.exe" OR process_name="lpremove.exe" OR process_name="lsass.exe" OR process_name="lsm.exe" OR process_name="makecab.exe" OR process_name="manage-bde.exe" OR process_name="mblctr.exe" OR process_name="mcbuilder.exe" OR process_name="mctadmin.exe" OR process_name="mfpmp.exe" OR process_name="mmc.exe" OR process_name="mobsync.exe" OR process_name="mountvol.exe" OR process_name="mpnotify.exe" OR process_name="msconfig.exe" OR process_name="msdt.exe" OR process_name="msdtc.exe" OR process_name="msfeedssync.exe" OR process_name="msg.exe" OR process_name="mshta.exe" OR process_name="msiexec.exe" OR process_name="msinfo32.exe" OR process_name="mspaint.exe" OR process_name="msra.exe" OR process_name="mstsc.exe" OR process_name="mtstocom.exe" OR process_name="nbtstat.exe" OR process_name="ndadmin.exe" OR process_name="net.exe" OR process_name="net1.exe" OR process_name="netbtugc.exe" OR process_name="netcfg.exe" OR process_name="netiougc.exe" OR process_name="netsh.exe" OR process_name="newdev.exe" OR process_name="nltest.exe" OR process_name="notepad.exe" OR process_name="nslookup.exe" OR process_name="ntoskrnl.exe" OR process_name="ntprint.exe" OR process_name="ocsetup.exe" OR process_name="odbcad32.exe" OR process_name="odbcconf.exe" OR process_name="omadmclient.exe" OR process_name="omadmprc.exe"; -$cond_5 = | from $ssa_input | where process_name="openfiles.exe" OR process_name="osk.exe" OR process_name="p2phost.exe" OR process_name="pcalua.exe" OR process_name="pcaui.exe" OR process_name="pcawrk.exe" OR process_name="pcwrun.exe" OR process_name="perfmon.exe" OR process_name="phoneactivate.exe" OR process_name="plasrv.exe" OR process_name="poqexec.exe" OR process_name="powercfg.exe" OR process_name="prevhost.exe" OR process_name="print.exe" OR process_name="printfilterpipelinesvc.exe" OR process_name="printui.exe" OR process_name="proquota.exe" OR process_name="provtool.exe" OR process_name="psr.exe" OR process_name="pwlauncher.exe" OR process_name="qappsrv.exe" OR process_name="qprocess.exe" OR process_name="query.exe" OR process_name="quser.exe" OR process_name="qwinsta.exe" OR process_name="rasautou.exe" OR process_name="rasdial.exe" OR process_name="raserver.exe" OR process_name="rasphone.exe" OR process_name="rdpclip.exe" OR process_name="rdpinput.exe" OR process_name="rdrleakdiag.exe" OR process_name="recdisc.exe" OR process_name="recover.exe" OR process_name="reg.exe" OR process_name="regedt32.exe" OR process_name="regini.exe" OR process_name="regsvr32.exe" OR process_name="rekeywiz.exe" OR process_name="relog.exe" OR process_name="repair-bde.exe" OR process_name="replace.exe" OR process_name="reset.exe" OR process_name="resmon.exe" OR process_name="rmttpmvscmgrsvr.exe" OR process_name="rrinstaller.exe" OR process_name="rstrui.exe" OR process_name="runas.exe" OR process_name="rundll32.exe" OR process_name="runonce.exe" OR process_name="rwinsta.exe" OR process_name="sbunattend.exe" OR process_name="sc.exe" OR process_name="schtasks.exe" OR process_name="sdbinst.exe" OR process_name="sdchange.exe" OR process_name="sdclt.exe" OR process_name="sdiagnhost.exe" OR process_name="secinit.exe" OR process_name="services.exe" OR process_name="sessionmsg.exe" OR process_name="sethc.exe" OR process_name="setspn.exe" OR process_name="setupcl.exe" OR process_name="setupugc.exe" OR process_name="setx.exe" OR process_name="sfc.exe" OR process_name="shadow.exe" OR process_name="shrpubw.exe" OR process_name="shutdown.exe" OR process_name="sigverif.exe" OR process_name="sihost.exe" OR process_name="slui.exe" OR process_name="smss.exe" OR process_name="snmptrap.exe" OR process_name="sort.exe" OR process_name="spinstall.exe" OR process_name="spoolsv.exe" OR process_name="sppsvc.exe" OR process_name="spreview.exe" OR process_name="srdelayed.exe" OR process_name="subst.exe" OR process_name="svchost.exe" OR process_name="sxstrace.exe" OR process_name="syskey.exe" OR process_name="systeminfo.exe" OR process_name="systemreset.exe" OR process_name="systray.exe" OR process_name="tabcal.exe" OR process_name="takeown.exe" OR process_name="taskeng.exe" OR process_name="taskhost.exe" OR process_name="taskhostw.exe" OR process_name="taskkill.exe" OR process_name="tasklist.exe" OR process_name="taskmgr.exe" OR process_name="tcmsetup.exe" OR process_name="timeout.exe" OR process_name="tpmvscmgr.exe" OR process_name="tpmvscmgrsvr.exe"; -$cond_6 = | from $ssa_input | where process_name="tracerpt.exe" OR process_name="tscon.exe" OR process_name="tsdiscon.exe" OR process_name="tskill.exe" OR process_name="typeperf.exe" OR process_name="tzsync.exe" OR process_name="tzutil.exe" OR process_name="ucsvc.exe" OR process_name="unlodctr.exe" OR process_name="unregmp2.exe" OR process_name="upnpcont.exe" OR process_name="userinit.exe" OR process_name="vds.exe" OR process_name="vdsldr.exe" OR process_name="verclsid.exe" OR process_name="verifier.exe" OR process_name="verifiergui.exe" OR process_name="vmicsvc.exe" OR process_name="vssadmin.exe" OR process_name="w32tm.exe" OR process_name="waitfor.exe" OR process_name="wbadmin.exe" OR process_name="wbengine.exe" OR process_name="wecutil.exe" OR process_name="wermgr.exe" OR process_name="wevtutil.exe" OR process_name="wextract.exe" OR process_name="where.exe" OR process_name="whoami.exe" OR process_name="wiaacmgr.exe" OR process_name="wiawow64.exe" OR process_name="wifitask.exe" OR process_name="wimserv.exe" OR process_name="wininit.exe" OR process_name="winload.exe" OR process_name="winlogon.exe" OR process_name="winresume.exe" OR process_name="winrs.exe" OR process_name="winrshost.exe" OR process_name="winver.exe" OR process_name="wisptis.exe" OR process_name="wkspbroker.exe" OR process_name="wksprt.exe" OR process_name="wlanext.exe" OR process_name="wlrmdr.exe" OR process_name="wowreg32.exe" OR process_name="wpnpinst.exe" OR process_name="wpr.exe" OR process_name="write.exe" OR process_name="wscript.exe" OR process_name="wsmprovhost.exe" OR process_name="wsqmcons.exe" OR process_name="wuapihost.exe" OR process_name="wuapp.exe" OR process_name="wuauclt.exe" OR process_name="wusa.exe" OR process_name="xcopy.exe" OR process_name="xpsrchvw.exe" OR process_name="xwizard.exe"; -| from $cond_1 | union $cond_2 | union $cond_3 | union $cond_4 | union $cond_5 | union $cond_6 | where process_path!="c:\\windows\\system32" AND process_path!="c:\\windows\\syswow64" | eval start_time = timestamp, end_time = timestamp, entities = mvappend(device, user), body = "TBD" | into write_ssa_detected_events(); + $ssa_input = +| from read_ssa_enriched_events() +| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), user=ucast(map_get(input_event, "dest_user_id"), "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=lower(ucast(map_get(input_event, "process_path"), "string", null)); +$cond_1 = +| from $ssa_input +| where process_name="arp.exe" OR process_name="adaptertroubleshooter.exe" OR process_name="applicationframehost.exe" OR process_name="atbroker.exe" OR process_name="authhost.exe" OR process_name="autoworkplace.exe" OR process_name="axinstui.exe" OR process_name="backgroundtransferhost.exe" OR process_name="bdehdcfg.exe" OR process_name="bdeuisrv.exe" OR process_name="bdeunlockwizard.exe" OR process_name="bitlockerdeviceencryption.exe" OR process_name="bitlockerwizard.exe" OR process_name="bitlockerwizardelev.exe" OR process_name="bytecodegenerator.exe" OR process_name="camerasettingsuihost.exe" OR process_name="castsrv.exe" OR process_name="certenrollctrl.exe" OR process_name="checknetisolation.exe" OR process_name="clipup.exe" OR process_name="cloudexperiencehostbroker.exe" OR process_name="cloudnotifications.exe" OR process_name="cloudstoragewizard.exe" OR process_name="compmgmtlauncher.exe" OR process_name="compattelrunner.exe" OR process_name="computerdefaults.exe" OR process_name="credentialuibroker.exe" OR process_name="dfdwiz.exe" OR process_name="dwwin.exe" OR process_name="dataexchangehost.exe" OR process_name="defrag.exe" OR process_name="devicedisplayobjectprovider.exe" OR process_name="deviceeject.exe" OR process_name="deviceenroller.exe" OR process_name="devicepairingwizard.exe" OR process_name="deviceproperties.exe" OR process_name="disksnapshot.exe" OR process_name="dism.exe" OR process_name="displayswitch.exe" OR process_name="dmnotificationbroker.exe" OR process_name="dmomacpmo.exe" OR process_name="dpiscaling.exe" OR process_name="dsmusertask.exe" OR process_name="dxpserver.exe" OR process_name="edpcleanup.exe" OR process_name="eosnotify.exe" OR process_name="eap3host.exe" OR process_name="easpoliciesbrokerhost.exe" OR process_name="easeofaccessdialog.exe" OR process_name="ehstorauthn.exe" OR process_name="fxscover.exe" OR process_name="fxssvc.exe" OR process_name="fxsunatd.exe" OR process_name="filehistory.exe" OR process_name="fondue.exe" OR process_name="gamepanel.exe" OR process_name="genvalobj.exe" OR process_name="gettingstarted.exe" OR process_name="hostname.exe" OR process_name="icsentitlementhost.exe" OR process_name="infdefaultinstall.exe" OR process_name="installagent.exe" OR process_name="languagecomponentsinstallercomhandler.exe" OR process_name="launchtm.exe" OR process_name="launchwinapp.exe" OR process_name="legacynetuxhost.exe" OR process_name="licensemanagershellext.exe" OR process_name="licensingui.exe" OR process_name="locationnotificationwindows.exe" OR process_name="locationnotifications.exe" OR process_name="locator.exe" OR process_name="lockapphost.exe" OR process_name="lockscreencontentserver.exe" OR process_name="logonui.exe" OR process_name="lsaiso.exe" OR process_name="mdeserver.exe" OR process_name="mdmagent.exe" OR process_name="mdmappinstaller.exe" OR process_name="mrinfo.exe" OR process_name="mrt.exe" OR process_name="mschedexe.exe" OR process_name="magnify.exe" OR process_name="mbaeparsertask.exe" OR process_name="mdres.exe" OR process_name="mdsched.exe" OR process_name="migautoplay.exe" OR process_name="mpsigstub.exe" OR process_name="msspellcheckinghost.exe" OR process_name="muiunattend.exe" OR process_name="multidigimon.exe" OR process_name="musnotification.exe" OR process_name="musnotificationux.exe" OR process_name="napstat.exe" OR process_name="netstat.exe" OR process_name="narrator.exe" OR process_name="netcfgnotifyobjecthost.exe" OR process_name="netevtfwdr.exe" OR process_name="netproj.exe" OR process_name="netplwiz.exe" OR process_name="networkuxbroker.exe"; +$cond_2 = +| from $ssa_input +| where process_name="openwith.exe" OR process_name="optionalfeatures.exe" OR process_name="pathping.exe" OR process_name="ping.exe" OR process_name="passwordonwakesettingflyout.exe" OR process_name="pickerhost.exe" OR process_name="pkgmgr.exe" OR process_name="pnpunattend.exe" OR process_name="pnputil.exe" OR process_name="presentationhost.exe" OR process_name="presentationsettings.exe" OR process_name="printbrmui.exe" OR process_name="printdialoghost.exe" OR process_name="printdialoghost3d.exe" OR process_name="printisolationhost.exe" OR process_name="proximityuxhost.exe" OR process_name="rdspnf.exe" OR process_name="rmactivate.exe" OR process_name="rmactivate_isv.exe" OR process_name="rmactivate_ssp.exe" OR process_name="rmactivate_ssp_isv.exe" OR process_name="route.exe" OR process_name="rdpsa.exe" OR process_name="rdpsaproxy.exe" OR process_name="rdpsauachelper.exe" OR process_name="reagentc.exe" OR process_name="recoverydrive.exe" OR process_name="register-cimprovider.exe" OR process_name="registeriepkeys.exe" OR process_name="relpost.exe" OR process_name="remoteposworker.exe" OR process_name="rmclient.exe" OR process_name="robocopy.exe" OR process_name="rpcping.exe" OR process_name="runlegacycplelevated.exe" OR process_name="runtimebroker.exe" OR process_name="sihclient.exe" OR process_name="searchfilterhost.exe" OR process_name="searchindexer.exe" OR process_name="searchprotocolhost.exe" OR process_name="secedit.exe" OR process_name="sensordataservice.exe" OR process_name="setieinstalleddate.exe" OR process_name="settingsynchost.exe" OR process_name="slidetoshutdown.exe" OR process_name="smartscreensettings.exe" OR process_name="sndvol.exe" OR process_name="snippingtool.exe" OR process_name="soundrecorder.exe" OR process_name="spaceagent.exe" OR process_name="sppextcomobj.exe" OR process_name="srtasks.exe" OR process_name="stikynot.exe" OR process_name="synchost.exe" OR process_name="sysreseterr.exe" OR process_name="systempropertiesadvanced.exe" OR process_name="systempropertiescomputername.exe" OR process_name="systempropertiesdataexecutionprevention.exe" OR process_name="systempropertieshardware.exe" OR process_name="systempropertiesperformance.exe" OR process_name="systempropertiesprotection.exe" OR process_name="systempropertiesremote.exe" OR process_name="systemsettingsadminflows.exe" OR process_name="systemsettingsbroker.exe" OR process_name="systemsettingsremovedevice.exe" OR process_name="tcpsvcs.exe" OR process_name="tracert.exe" OR process_name="tstheme.exe" OR process_name="tswbprxy.exe" OR process_name="tapiunattend.exe" OR process_name="taskmgr.exe" OR process_name="thumbnailextractionhost.exe" OR process_name="tokenbrokercookies.exe" OR process_name="tpminit.exe" OR process_name="tswpfwrp.exe" OR process_name="ui0detect.exe" OR process_name="upgraderesultsui.exe" OR process_name="useraccountbroker.exe" OR process_name="useraccountcontrolsettings.exe" OR process_name="usoclient.exe" OR process_name="utilman.exe" OR process_name="vssvc.exe" OR process_name="vaultcmd.exe" OR process_name="vaultsysui.exe" OR process_name="wfs.exe" OR process_name="wmpdmc.exe" OR process_name="wpdshextautoplay.exe" OR process_name="wscollect.exe" OR process_name="wsmanhttpconfig.exe" OR process_name="wsreset.exe" OR process_name="wudfhost.exe" OR process_name="wwahost.exe" OR process_name="wallpaperhost.exe" OR process_name="webcache.exe" OR process_name="werfault.exe" OR process_name="werfaultsecure.exe" OR process_name="winsat.exe" OR process_name="windows.media.backgroundplayback.exe" OR process_name="windowsactiondialog.exe" OR process_name="windowsanytimeupgrade.exe" OR process_name="windowsanytimeupgraderesults.exe"; +$cond_3 = +| from $ssa_input +| where process_name="windowsanytimeupgradeui.exe" OR process_name="windowsupdateelevatedinstaller.exe" OR process_name="workfolders.exe" OR process_name="wpcmon.exe" OR process_name="acu.exe" OR process_name="aitagent.exe" OR process_name="aitstatic.exe" OR process_name="alg.exe" OR process_name="appidcertstorecheck.exe" OR process_name="appidpolicyconverter.exe" OR process_name="at.exe" OR process_name="attrib.exe" OR process_name="audiodg.exe" OR process_name="auditpol.exe" OR process_name="autochk.exe" OR process_name="autoconv.exe" OR process_name="autofmt.exe" OR process_name="baaupdate.exe" OR process_name="backgroundtaskhost.exe" OR process_name="bcastdvr.exe" OR process_name="bcdboot.exe" OR process_name="bcdedit.exe" OR process_name="bdechangepin.exe" OR process_name="bdeunlock.exe" OR process_name="bitsadmin.exe" OR process_name="bootcfg.exe" OR process_name="bootim.exe" OR process_name="bootsect.exe" OR process_name="bridgeunattend.exe" OR process_name="browser_broker.exe" OR process_name="bthudtask.exe" OR process_name="cacls.exe" OR process_name="calc.exe" OR process_name="cdpreference.exe" OR process_name="certreq.exe" OR process_name="certutil.exe" OR process_name="change.exe" OR process_name="changepk.exe" OR process_name="charmap.exe" OR process_name="chglogon.exe" OR process_name="chgport.exe" OR process_name="chgusr.exe" OR process_name="chkdsk.exe" OR process_name="chkntfs.exe" OR process_name="choice.exe" OR process_name="cipher.exe" OR process_name="cleanmgr.exe" OR process_name="cliconfg.exe" OR process_name="clip.exe" OR process_name="cmd.exe" OR process_name="cmdkey.exe" OR process_name="cmdl32.exe" OR process_name="cmmon32.exe" OR process_name="cmstp.exe" OR process_name="cofire.exe" OR process_name="colorcpl.exe" OR process_name="comp.exe" OR process_name="compact.exe" OR process_name="conhost.exe" OR process_name="consent.exe" OR process_name="control.exe" OR process_name="convert.exe" OR process_name="credwiz.exe" OR process_name="cscript.exe" OR process_name="csrss.exe" OR process_name="ctfmon.exe" OR process_name="cttune.exe" OR process_name="cttunesvr.exe" OR process_name="dashost.exe" OR process_name="dccw.exe" OR process_name="dcomcnfg.exe" OR process_name="ddodiag.exe" OR process_name="dfrgui.exe" OR process_name="dialer.exe" OR process_name="diantz.exe" OR process_name="dinotify.exe" OR process_name="diskpart.exe" OR process_name="diskperf.exe" OR process_name="diskraid.exe" OR process_name="dispdiag.exe" OR process_name="djoin.exe" OR process_name="dllhost.exe" OR process_name="dllhst3g.exe" OR process_name="dmcertinst.exe" OR process_name="dmcfghost.exe" OR process_name="dmclient.exe" OR process_name="dnscacheugc.exe" OR process_name="doskey.exe" OR process_name="dpapimig.exe" OR process_name="dpnsvr.exe" OR process_name="driverquery.exe" OR process_name="drvcfg.exe" OR process_name="drvinst.exe" OR process_name="dsregcmd.exe" OR process_name="dstokenclean.exe" OR process_name="dvdplay.exe" OR process_name="dvdupgrd.exe" OR process_name="dwm.exe" OR process_name="dxdiag.exe" OR process_name="easinvoker.exe" OR process_name="efsui.exe"; +$cond_4 = +| from $ssa_input +| where process_name="embeddedapplauncher.exe" OR process_name="esentutl.exe" OR process_name="eudcedit.exe" OR process_name="eventcreate.exe" OR process_name="eventvwr.exe" OR process_name="expand.exe" OR process_name="extrac32.exe" OR process_name="fc.exe" OR process_name="fhmanagew.exe" OR process_name="find.exe" OR process_name="findstr.exe" OR process_name="finger.exe" OR process_name="fixmapi.exe" OR process_name="fltmc.exe" OR process_name="fodhelper.exe" OR process_name="fontdrvhost.exe" OR process_name="fontview.exe" OR process_name="forfiles.exe" OR process_name="fsavailux.exe" OR process_name="fsquirt.exe" OR process_name="fsutil.exe" OR process_name="ftp.exe" OR process_name="fvenotify.exe" OR process_name="fveprompt.exe" OR process_name="getmac.exe" OR process_name="gpresult.exe" OR process_name="gpscript.exe" OR process_name="gpupdate.exe" OR process_name="grpconv.exe" OR process_name="hdwwiz.exe" OR process_name="help.exe" OR process_name="hwrcomp.exe" OR process_name="hwrreg.exe" OR process_name="icacls.exe" OR process_name="icardagt.exe" OR process_name="icsunattend.exe" OR process_name="ie4uinit.exe" OR process_name="ieunatt.exe" OR process_name="ieetwcollector.exe" OR process_name="iexpress.exe" OR process_name="immersivetpmvscmgrsvr.exe" OR process_name="ipconfig.exe" OR process_name="irftp.exe" OR process_name="iscsicli.exe" OR process_name="iscsicpl.exe" OR process_name="isoburn.exe" OR process_name="klist.exe" OR process_name="ksetup.exe" OR process_name="ktmutil.exe" OR process_name="label.exe" OR process_name="licensingdiag.exe" OR process_name="lodctr.exe" OR process_name="logagent.exe" OR process_name="logman.exe" OR process_name="logoff.exe" OR process_name="lpkinstall.exe" OR process_name="lpksetup.exe" OR process_name="lpremove.exe" OR process_name="lsass.exe" OR process_name="lsm.exe" OR process_name="makecab.exe" OR process_name="manage-bde.exe" OR process_name="mblctr.exe" OR process_name="mcbuilder.exe" OR process_name="mctadmin.exe" OR process_name="mfpmp.exe" OR process_name="mmc.exe" OR process_name="mobsync.exe" OR process_name="mountvol.exe" OR process_name="mpnotify.exe" OR process_name="msconfig.exe" OR process_name="msdt.exe" OR process_name="msdtc.exe" OR process_name="msfeedssync.exe" OR process_name="msg.exe" OR process_name="mshta.exe" OR process_name="msiexec.exe" OR process_name="msinfo32.exe" OR process_name="mspaint.exe" OR process_name="msra.exe" OR process_name="mstsc.exe" OR process_name="mtstocom.exe" OR process_name="nbtstat.exe" OR process_name="ndadmin.exe" OR process_name="net.exe" OR process_name="net1.exe" OR process_name="netbtugc.exe" OR process_name="netcfg.exe" OR process_name="netiougc.exe" OR process_name="netsh.exe" OR process_name="newdev.exe" OR process_name="nltest.exe" OR process_name="notepad.exe" OR process_name="nslookup.exe" OR process_name="ntoskrnl.exe" OR process_name="ntprint.exe" OR process_name="ocsetup.exe" OR process_name="odbcad32.exe" OR process_name="odbcconf.exe" OR process_name="omadmclient.exe" OR process_name="omadmprc.exe"; +$cond_5 = +| from $ssa_input +| where process_name="openfiles.exe" OR process_name="osk.exe" OR process_name="p2phost.exe" OR process_name="pcalua.exe" OR process_name="pcaui.exe" OR process_name="pcawrk.exe" OR process_name="pcwrun.exe" OR process_name="perfmon.exe" OR process_name="phoneactivate.exe" OR process_name="plasrv.exe" OR process_name="poqexec.exe" OR process_name="powercfg.exe" OR process_name="prevhost.exe" OR process_name="print.exe" OR process_name="printfilterpipelinesvc.exe" OR process_name="printui.exe" OR process_name="proquota.exe" OR process_name="provtool.exe" OR process_name="psr.exe" OR process_name="pwlauncher.exe" OR process_name="qappsrv.exe" OR process_name="qprocess.exe" OR process_name="query.exe" OR process_name="quser.exe" OR process_name="qwinsta.exe" OR process_name="rasautou.exe" OR process_name="rasdial.exe" OR process_name="raserver.exe" OR process_name="rasphone.exe" OR process_name="rdpclip.exe" OR process_name="rdpinput.exe" OR process_name="rdrleakdiag.exe" OR process_name="recdisc.exe" OR process_name="recover.exe" OR process_name="reg.exe" OR process_name="regedt32.exe" OR process_name="regini.exe" OR process_name="regsvr32.exe" OR process_name="rekeywiz.exe" OR process_name="relog.exe" OR process_name="repair-bde.exe" OR process_name="replace.exe" OR process_name="reset.exe" OR process_name="resmon.exe" OR process_name="rmttpmvscmgrsvr.exe" OR process_name="rrinstaller.exe" OR process_name="rstrui.exe" OR process_name="runas.exe" OR process_name="rundll32.exe" OR process_name="runonce.exe" OR process_name="rwinsta.exe" OR process_name="sbunattend.exe" OR process_name="sc.exe" OR process_name="schtasks.exe" OR process_name="sdbinst.exe" OR process_name="sdchange.exe" OR process_name="sdclt.exe" OR process_name="sdiagnhost.exe" OR process_name="secinit.exe" OR process_name="services.exe" OR process_name="sessionmsg.exe" OR process_name="sethc.exe" OR process_name="setspn.exe" OR process_name="setupcl.exe" OR process_name="setupugc.exe" OR process_name="setx.exe" OR process_name="sfc.exe" OR process_name="shadow.exe" OR process_name="shrpubw.exe" OR process_name="shutdown.exe" OR process_name="sigverif.exe" OR process_name="sihost.exe" OR process_name="slui.exe" OR process_name="smss.exe" OR process_name="snmptrap.exe" OR process_name="sort.exe" OR process_name="spinstall.exe" OR process_name="spoolsv.exe" OR process_name="sppsvc.exe" OR process_name="spreview.exe" OR process_name="srdelayed.exe" OR process_name="subst.exe" OR process_name="svchost.exe" OR process_name="sxstrace.exe" OR process_name="syskey.exe" OR process_name="systeminfo.exe" OR process_name="systemreset.exe" OR process_name="systray.exe" OR process_name="tabcal.exe" OR process_name="takeown.exe" OR process_name="taskeng.exe" OR process_name="taskhost.exe" OR process_name="taskhostw.exe" OR process_name="taskkill.exe" OR process_name="tasklist.exe" OR process_name="taskmgr.exe" OR process_name="tcmsetup.exe" OR process_name="timeout.exe" OR process_name="tpmvscmgr.exe" OR process_name="tpmvscmgrsvr.exe"; +$cond_6 = +| from $ssa_input +| where process_name="tracerpt.exe" OR process_name="tscon.exe" OR process_name="tsdiscon.exe" OR process_name="tskill.exe" OR process_name="typeperf.exe" OR process_name="tzsync.exe" OR process_name="tzutil.exe" OR process_name="ucsvc.exe" OR process_name="unlodctr.exe" OR process_name="unregmp2.exe" OR process_name="upnpcont.exe" OR process_name="userinit.exe" OR process_name="vds.exe" OR process_name="vdsldr.exe" OR process_name="verclsid.exe" OR process_name="verifier.exe" OR process_name="verifiergui.exe" OR process_name="vmicsvc.exe" OR process_name="vssadmin.exe" OR process_name="w32tm.exe" OR process_name="waitfor.exe" OR process_name="wbadmin.exe" OR process_name="wbengine.exe" OR process_name="wecutil.exe" OR process_name="wermgr.exe" OR process_name="wevtutil.exe" OR process_name="wextract.exe" OR process_name="where.exe" OR process_name="whoami.exe" OR process_name="wiaacmgr.exe" OR process_name="wiawow64.exe" OR process_name="wifitask.exe" OR process_name="wimserv.exe" OR process_name="wininit.exe" OR process_name="winload.exe" OR process_name="winlogon.exe" OR process_name="winresume.exe" OR process_name="winrs.exe" OR process_name="winrshost.exe" OR process_name="winver.exe" OR process_name="wisptis.exe" OR process_name="wkspbroker.exe" OR process_name="wksprt.exe" OR process_name="wlanext.exe" OR process_name="wlrmdr.exe" OR process_name="wowreg32.exe" OR process_name="wpnpinst.exe" OR process_name="wpr.exe" OR process_name="write.exe" OR process_name="wscript.exe" OR process_name="wsmprovhost.exe" OR process_name="wsqmcons.exe" OR process_name="wuapihost.exe" OR process_name="wuapp.exe" OR process_name="wuauclt.exe" OR process_name="wusa.exe" OR process_name="xcopy.exe" OR process_name="xpsrchvw.exe" OR process_name="xwizard.exe"; + +| from $cond_1 +| union $cond_2 +| union $cond_3 +| union $cond_4 +| union $cond_5 +| union $cond_6 +| where process_path!="c:\\windows\\system32" AND process_path!="c:\\windows\\syswow64" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(device, user), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement Collect endpoint data such as sysmon or 4688 events. #### Required fields + * dest_device_id + * process_name + * _time + * dest_user_id + * process_path + #### ATT&CK | ID | technique | Tactic | @@ -15307,25 +25365,30 @@ Collect endpoint data such as sysmon or 4688 events. | T1036 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None #### References + #### Test Dataset + _version_: 1
--- + ### System Processes Run From Unexpected Locations This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1036.003 +- **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2020-12-08
@@ -15333,15 +25396,29 @@ This search looks for system processes that normally run out of C:\Windows\Syste #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name Processes.process_hash| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file` | `system_processes_run_from_unexpected_locations_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name Processes.process_hash +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `is_windows_system_file` +| `system_processes_run_from_unexpected_locations_filter` ``` #### Associated Analytic Story +* Suspicious Command-Line Executions + +* Unusual Processes + +* Ransomware + + #### How To Implement To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15349,26 +25426,32 @@ To successfully implement this search you need to ingest details about process e | T1036.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + _version_: 5
--- + ### TOR Traffic This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1071.001 +- **Data Models**: Network_Traffic +- **ATT&CK**: [T1071.001](https://attack.mitre.org/techniques/T1071.001/) - **Last Updated**: 2020-07-22
@@ -15376,15 +25459,30 @@ This search looks for network traffic identified as The Onion Router (TOR), a be #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `tor_traffic_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `tor_traffic_filter` ``` #### Associated Analytic Story +* Prohibited Traffic Allowed or Protocol Mismatch + +* Ransomware + +* Command and Control + +* Sunburst Malware + + #### How To Implement In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15392,25 +25490,30 @@ In order to properly run this search, Splunk needs to ingest data from firewalls | T1071.001 | x | x | #### Kill Chain Phases + * Command and Control + #### Known False Positives None at this time #### References + #### Test Dataset + _version_: 2
--- + ### USN Journal Deletion The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1070 +- **Data Models**: Endpoint +- **ATT&CK**: [T1070](https://attack.mitre.org/techniques/T1070/) - **Last Updated**: 2018-12-03
@@ -15418,15 +25521,27 @@ The fsutil.exe application is a legitimate Windows utility used to perform tasks #### Search ``` -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search process="*deletejournal*" AND process="*usn*" +| `usn_journal_deletion_filter` ``` #### Associated Analytic Story +* Windows Log Manipulation + +* Ransomware + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15434,26 +25549,32 @@ You must be ingesting data that records process activity from your hosts to popu | T1070 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/atomic_red_team/windows-sysmon.log + _version_: 2
--- + ### Uncommon Processes On Endpoint This search looks for applications on the endpoint that you have marked as uncommon. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1204.002 +- **Data Models**: Endpoint +- **ATT&CK**: [T1204.002](https://attack.mitre.org/techniques/T1204.002/) - **Last Updated**: 2020-07-22
@@ -15461,15 +25582,29 @@ This search looks for applications on the endpoint that you have marked as uncom #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_on_endpoint_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `uncommon_processes` +|`uncommon_processes_on_endpoint_filter` ``` #### Associated Analytic Story +* Windows Privilege Escalation + +* Unusual Processes + +* Cloud Federated Credential Abuse + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15477,25 +25612,30 @@ You must be ingesting data that records process activity from your hosts to popu | T1204.002 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives None identified #### References + #### Test Dataset + _version_: 4
--- + ### Unload Sysmon Filter Driver Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562.001/) - **Last Updated**: 2020-07-22
@@ -15503,15 +25643,25 @@ Attackers often disable security tools to avoid detection. This search looks for #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` |`unload_sysmon_filter_driver_filter`| table firstTime lastTime dest user count process_name process_id parent_process_name process + +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`unload_sysmon_filter_driver_filter` +| table firstTime lastTime dest user count process_name process_id parent_process_name process ``` #### Associated Analytic Story +* Disabling Security Tools + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15519,26 +25669,32 @@ You must be ingesting data that records process activity from your hosts to popu | T1562.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log + _version_: 3
--- + ### Unsigned Image Loaded by LSASS This search detects loading of unsigned images by LSASS. Deprecated because too noisy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1003.001 +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2019-12-06
@@ -15546,15 +25702,24 @@ This search detects loading of unsigned images by LSASS. Deprecated because too #### Search ``` -`sysmon` EventID=7 Image=*lsass.exe Signed=false | stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `unsigned_image_loaded_by_lsass_filter` +`sysmon` EventID=7 Image=*lsass.exe Signed=false +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `unsigned_image_loaded_by_lsass_filter` ``` #### Associated Analytic Story +* Credential Dumping + + #### How To Implement This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15562,20 +25727,26 @@ This search needs Sysmon Logs with a sysmon configuration, which includes EventC | T1003.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. #### References + * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + #### Test Dataset + _version_: 1
--- + ### Unsuccessful Netbackup backups This search gives you the hosts where a backup was attempted and then failed. @@ -15589,15 +25760,25 @@ This search gives you the hosts where a backup was attempted and then failed. #### Search ``` -`netbackup` | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature | `unsuccessful_netbackup_backups_filter` +`netbackup` +| stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE +| search MESSAGE="An error occurred, failed to backup." +| `security_content_ctime(latestTime)` +| rename COMPUTERNAME as dest, MESSAGE as signature +| table latestTime, dest, signature +| `unsuccessful_netbackup_backups_filter` ``` #### Associated Analytic Story +* Monitor Backup Solution + + #### How To Implement To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15605,17 +25786,21 @@ To successfully implement this search you need to obtain data from your backup s #### Kill Chain Phases + #### Known False Positives None identified #### References + #### Test Dataset + _version_: 1 --- + ### Unusually Long Command Line Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez. @@ -15629,39 +25814,64 @@ Command lines that are extremely long may be indicative of malicious activity on #### Search ``` - | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) | where cmd_line!=null and dest_user_id!=null | eval cmd_line_norm=replace(cast(cmd_line, "string"), /\s(--?\w+)|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) | select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input | adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 | where label AND quantile>0.99 | first_time_event input_columns=["dest_device_id", "cmd_line"] | where first_time_dest_device_id_cmd_line | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) +| where cmd_line!=null and dest_user_id!=null +| eval cmd_line_norm=replace(cast(cmd_line, "string"), /\s(--?\w+) +|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) +| select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input +| adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 +| where label AND quantile>0.99 +| first_time_event input_columns=["dest_device_id", "cmd_line"] +| where first_time_dest_device_id_cmd_line +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); ``` #### Associated Analytic Story + #### How To Implement You must be ingesting sysmon endpoint data that monitors command lines. #### Required fields + * process_name + * _time + * dest_device_id + * dest_user_id + * process + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives This detection may flag suspiciously long command lines when there is not sufficient evidence (samples) for a given process that this detection is tracking; or when there is high variability in the length of the command line for the tracked process. Also, some legitimate applications may use long command lines. Such is the case of Ansible, that encodes Powershell scripts using long base64. Attackers may use this technique to obfuscate their payloads. #### References + #### Test Dataset + _version_: 1 --- + ### Unusually Long Command Line Command lines that are extremely long may be indicative of malicious activity on your hosts. @@ -15675,35 +25885,61 @@ Command lines that are extremely long may be indicative of malicious activity on #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process | `unusually_long_command_line_filter` |eval threshold = 3 | where maxlen > ((threshold*stdevperhost) + avgperhost) + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval processlen=len(process) +| eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest +| stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process +| `unusually_long_command_line_filter` +|eval threshold = 3 +| where maxlen > ((threshold*stdevperhost) + avgperhost) ``` #### Associated Analytic Story +* Suspicious Command-Line Executions + +* Unusual Processes + +* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + +* Ransomware + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the process field in the Endpoint data model. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Some legitimate applications start with long command lines. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + _version_: 5 --- + ### Unusually Long Command Line - MLTK Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. @@ -15717,34 +25953,60 @@ Command lines that are extremely long may be indicative of malicious activity on #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime lastTime user dest process_name process processlen count | `unusually_long_command_line___mltk_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval processlen=len(process) +| search user!=unknown +| apply cmdline_pdfmodel threshold=0.01 +| rename "IsOutlier(processlen)" as isOutlier +| search isOutlier > 0 +| table firstTime lastTime user dest process_name process processlen count +| `unusually_long_command_line___mltk_filter` ``` #### Associated Analytic Story +* Suspicious Command-Line Executions + +* Unusual Processes + +* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + +* Ransomware + + #### How To Implement You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. #### References + #### Test Dataset + _version_: 1 --- + ### Unusually Long Content-Type Length This search looks for unusually long strings in the Content-Type http header that the client sends the server. @@ -15758,40 +26020,53 @@ This search looks for unusually long strings in the Content-Type http header tha #### Search ``` -`stream_http` | eval cs_content_type_length = len(cs_content_type) | where cs_content_type_length > 100 | table endtime src_ip dest_ip cs_content_type_length cs_content_type url | `unusually_long_content_type_length_filter` +`stream_http` +| eval cs_content_type_length = len(cs_content_type) +| where cs_content_type_length > 100 +| table endtime src_ip dest_ip cs_content_type_length cs_content_type url +| `unusually_long_content_type_length_filter` ``` #### Associated Analytic Story +* Apache Struts Vulnerability + + #### How To Implement This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Delivery + #### Known False Positives Very few legitimate Content-Type fields will have a length greater than 100 characters. #### References + #### Test Dataset + _version_: 1 --- + ### WBAdmin Delete System Backups This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1490 +- **Data Models**: Endpoint +- **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/) - **Last Updated**: 2021-01-22
@@ -15799,15 +26074,26 @@ This search looks for flags passed to wbadmin.exe (Windows Backup Administrator #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `wbadmin_delete_system_backups_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `wbadmin_delete_system_backups_filter` ``` #### Associated Analytic Story +* Ryuk Ransomware + +* Ransomware + + #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15815,30 +26101,40 @@ You must be ingesting endpoint data that tracks process activity, including pare | T1490 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Administrators may modify the boot configuration. #### References + * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md + * https://thedfirreport.com/2020/10/08/ryuks-return/ + * https://attack.mitre.org/techniques/T1490/ + * https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### WMI Permanent Event Subscription This search looks for the creation of WMI permanent event subscriptions. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1047 +- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) - **Last Updated**: 2018-10-23
@@ -15846,15 +26142,27 @@ This search looks for the creation of WMI permanent event subscriptions. #### Search ``` -`wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | rename ComputerName as dest | `wmi_permanent_event_subscription_filter` +`wmi` EventCode=5861 Binding +| rex field=Message "Consumer =\s+(?[^; +|^$]+)" +| search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" +| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename ComputerName as dest +| `wmi_permanent_event_subscription_filter` ``` #### Associated Analytic Story +* Suspicious WMI Use + + #### How To Implement To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15862,25 +26170,30 @@ To successfully implement this search, you must be ingesting the Windows WMI act | T1047 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, administrators may use event subscriptions for legitimate purposes. #### References + #### Test Dataset + _version_: 1
--- + ### WMI Permanent Event Subscription - Sysmon This search looks for the creation of WMI permanent event subscriptions. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1546.003 +- **ATT&CK**: [T1546.003](https://attack.mitre.org/techniques/T1546.003/) - **Last Updated**: 2020-12-08
@@ -15888,15 +26201,22 @@ This search looks for the creation of WMI permanent event subscriptions. #### Search ``` -`sysmon` EventCode=21 | rename host as dest | table _time, dest, user, Operation, EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter` +`sysmon` EventCode=21 +| rename host as dest +| table _time, dest, user, Operation, EventType, Query, Consumer, Filter +| `wmi_permanent_event_subscription___sysmon_filter` ``` #### Associated Analytic Story +* Suspicious WMI Use + + #### How To Implement To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15904,26 +26224,32 @@ To successfully implement this search, you must be collecting Sysmon data using | T1546.003 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Although unlikely, administrators may use event subscriptions for legitimate purposes. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log + _version_: 2
--- + ### WMI Temporary Event Subscription This search looks for the creation of WMI temporary event subscriptions. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1047 +- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) - **Last Updated**: 2018-10-23
@@ -15931,15 +26257,26 @@ This search looks for the creation of WMI temporary event subscriptions. #### Search ``` -`wmi` EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?[^;|^$]+)" | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter` +`wmi` EventCode=5860 Temporary +| rex field=Message "NotificationQuery =\s+(?[^; +|^$]+)" +| search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" +| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `wmi_temporary_event_subscription_filter` ``` #### Associated Analytic Story +* Suspicious WMI Use + + #### How To Implement To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15947,25 +26284,30 @@ To successfully implement this search, you must be ingesting the Windows WMI act | T1047 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. #### References + #### Test Dataset + _version_: 1
--- + ### Web Fraud - Account Harvesting This search is used to identify the creation of multiple user accounts using the same email domain name. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1136 +- **ATT&CK**: [T1136](https://attack.mitre.org/techniques/T1136/) - **Last Updated**: 2018-10-08
@@ -15973,15 +26315,27 @@ This search is used to identify the creation of multiple user accounts using the #### Search ``` -`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" | rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^&|^$]+)" | search Username=* | rex field=Username "@(?.*)" | stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain | where UniqueUsernames> 25 | `web_fraud___account_harvesting_filter` +`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" +| rex field=cookie "form_key=(?\w+)" +| rex field=form_data "login\[username\]=(?[^& +|^$]+)" +| search Username=* +| rex field=Username "@(?.*)" +| stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain +| where UniqueUsernames> 25 +| `web_fraud___account_harvesting_filter` ``` #### Associated Analytic Story +* Web Fraud Detection + + #### How To Implement We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -15989,27 +26343,34 @@ We start with a dataset that provides visibility into the email address used for | T1136 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. #### References + * https://splunkbase.splunk.com/app/2734/ + * https://splunkbase.splunk.com/app/1809/ + #### Test Dataset + _version_: 1
--- + ### Web Fraud - Anomalous User Clickspeed This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2018-10-08
@@ -16017,15 +26378,25 @@ This search is used to examine web sessions to identify those where the clicks a #### Search ``` -`stream_http` http_content_type=text* | rex field=cookie "form_key=(?\w+)" | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) | `web_fraud___anomalous_user_clickspeed_filter` +`stream_http` http_content_type=text* +| rex field=cookie "form_key=(?\w+)" +| streamstats window=2 current=1 range(_time) as TimeDelta by session_id +| where TimeDelta>0 +|stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id +| where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) +| `web_fraud___anomalous_user_clickspeed_filter` ``` #### Associated Analytic Story +* Web Fraud Detection + + #### How To Implement Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16033,23 +26404,32 @@ Start with a dataset that allows you to see clickstream data for each user click | T1078 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior. #### References + * https://en.wikipedia.org/wiki/Session_ID + * https://en.wikipedia.org/wiki/Session_(computer_science) + * https://en.wikipedia.org/wiki/HTTP_cookie + * https://splunkbase.splunk.com/app/1809/ + #### Test Dataset + _version_: 1
--- + ### Web Fraud - Password Sharing Across Accounts This search is used to identify user accounts that share a common password. @@ -16063,15 +26443,26 @@ This search is used to identify user accounts that share a common password. #### Search ``` -`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data "login\[username\]=(?[^&|^$]+)" | rex field=form_data "login\[password\]=(?[^&|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 | `web_fraud___password_sharing_across_accounts_filter` +`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* +| rex field=form_data "login\[username\]=(?[^& +|^$]+)" +| rex field=form_data "login\[password\]=(?[^& +|^$]+)" +| stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password +|where UniqueUsernames>5 +| `web_fraud___password_sharing_across_accounts_filter` ``` #### Associated Analytic Story +* Web Fraud Detection + + #### How To Implement We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16079,27 +26470,35 @@ We need to start with a dataset that allows us to see the values of usernames an #### Kill Chain Phases + #### Known False Positives As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. #### References + * https://en.wikipedia.org/wiki/Session_ID + * https://en.wikipedia.org/wiki/Session_(computer_science) + * https://en.wikipedia.org/wiki/HTTP_cookie + * https://splunkbase.splunk.com/app/1809/ + #### Test Dataset + _version_: 1 --- + ### Web Servers Executing Suspicious Processes This search looks for suspicious processes on all systems labeled as web servers. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1082 +- **Data Models**: Endpoint +- **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/) - **Last Updated**: 2019-04-01
@@ -16107,15 +26506,24 @@ This search looks for suspicious processes on all systems labeled as web servers #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `web_servers_executing_suspicious_processes_filter` ``` #### Associated Analytic Story +* Apache Struts Vulnerability + + #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16123,25 +26531,30 @@ You must be ingesting data that records process activity from your hosts to popu | T1082 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. #### References + #### Test Dataset + _version_: 1
--- + ### Windows AdFind Exe This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1018 +- **Data Models**: Endpoint +- **ATT&CK**: [T1018](https://attack.mitre.org/techniques/T1018/) - **Last Updated**: 2020-12-16
@@ -16149,15 +26562,24 @@ This search looks for the execution of `adfind.exe` with command-line arguments #### Search ``` - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_adfind_exe_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `windows_adfind_exe_filter` ``` #### Associated Analytic Story +* Sunburst Malware + + #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16165,28 +26587,36 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1018 | x | x | #### Kill Chain Phases + * Exploitation + #### Known False Positives administrators rarely use adfind, usually not used for legitimate reasons #### References + * https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/ + * https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log + _version_: 1
--- + ### Windows DisableAntiSpyware Registry The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: T1562.001 +- **Data Models**: Endpoint +- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562.001/) - **Last Updated**: 2020-11-06
@@ -16194,15 +26624,24 @@ The search looks for the Registry Key DisableAntiSpyware set to disable. This is #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name +| `drop_dm_object_name(Registry)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `windows_disableantispyware_registry_filter` ``` #### Associated Analytic Story +* Ryuk Ransomware + + #### How To Implement You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16210,25 +26649,30 @@ You must be ingesting data that records the process-system activity from your ho | T1562.001 | x | x | #### Kill Chain Phases + * Delivery + #### Known False Positives It is unusual to turn this feature on a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. #### References + #### Test Dataset + _version_: 1
--- + ### Windows Event Log Cleared This search looks for Windows events that indicate one of the Windows event logs has been purged. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1070.001 +- **ATT&CK**: [T1070.001](https://attack.mitre.org/techniques/T1070.001/) - **Last Updated**: 2020-07-06
@@ -16236,15 +26680,25 @@ This search looks for Windows events that indicate one of the Windows event logs #### Search ``` -(`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_log_cleared_filter` +(`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) +| stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `windows_event_log_cleared_filter` ``` #### Associated Analytic Story +* Windows Log Manipulation + +* Ransomware + + #### How To Implement To successfully implement this search, you need to be ingesting Windows event logs from your hosts. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16252,27 +26706,34 @@ To successfully implement this search, you need to be ingesting Windows event lo | T1070.001 | x | x | #### Kill Chain Phases + * Actions on Objectives + #### Known False Positives It is possible that these logs may be legitimately cleared by Administrators. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-security.log + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-system.log + _version_: 4
--- + ### Windows Security Account Manager Stopped The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1489 +- **ATT&CK**: [T1489](https://attack.mitre.org/techniques/T1489/) - **Last Updated**: 2020-11-06
@@ -16280,15 +26741,24 @@ The search looks for a Windows Security Account Manager (SAM) was stopped via co #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_security_account_manager_stopped_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" +| `drop_dm_object_name(Processes)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `windows_security_account_manager_stopped_filter` ``` #### Associated Analytic Story +* Ryuk Ransomware + + #### How To Implement You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16296,26 +26766,32 @@ You must be ingesting data that records the process-system activity from your ho | T1489 | x | x | #### Kill Chain Phases + * Delivery + #### Known False Positives SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified. #### References + #### Test Dataset + * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log + _version_: 1
--- + ### Windows connhost exe started forcefully The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1059.003 +- **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059.003/) - **Last Updated**: 2020-11-06
@@ -16323,15 +26799,24 @@ The search looks for the Console Window Host process (connhost.exe) executed usi #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_connhost_exe_started_forcefully_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `windows_connhost_exe_started_forcefully_filter` ``` #### Associated Analytic Story +* Ryuk Ransomware + + #### How To Implement You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16339,19 +26824,24 @@ You must be ingesting data that records the process-system activity from your ho | T1059.003 | x | x | #### Kill Chain Phases + * Delivery + #### Known False Positives This process should not be ran forcefully, we have not see any false positives for this detection #### References + #### Test Dataset + _version_: 1
--- + ### Windows hosts file modification The search looks for modifications to the hosts file on all Windows endpoints across your environment. @@ -16365,40 +26855,55 @@ The search looks for modifications to the hosts file on all Windows endpoints ac #### Search ``` -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* | `drop_dm_object_name(Filesystem)` | `windows_hosts_file_modification_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* +| `drop_dm_object_name(Filesystem)` +| `windows_hosts_file_modification_filter` ``` #### Associated Analytic Story +* Host Redirection + + #### How To Implement To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. #### Required fields + #### ATT&CK | ID | technique | Tactic | | ----------- | ----------- |:-------------:| #### Kill Chain Phases + * Command and Control + #### Known False Positives There may be legitimate reasons for system administrators to add entries to this file. #### References + #### Test Dataset + _version_: 1 --- + ### aws detect attach to role policy This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-07-27
@@ -16406,15 +26911,22 @@ This search provides detection of an user attaching itself to a different role t #### Search ``` -`aws_cloudwatchlogs_eks` attach policy| spath requestParameters.policyArn | table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate | `aws_detect_attach_to_role_policy_filter` +`aws_cloudwatchlogs_eks` attach policy +| spath requestParameters.policyArn +| table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate +| `aws_detect_attach_to_role_policy_filter` ``` #### Associated Analytic Story +* AWS Cross Account Activity + + #### How To Implement You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16422,25 +26934,30 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit | T1078 | x | x | #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies. #### References + #### Test Dataset + _version_: 1
--- + ### aws detect permanent key creation This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-07-27
@@ -16448,15 +26965,23 @@ This search provides detection of accounts creating permanent keys. Permanent ke #### Search ``` -`aws_cloudwatchlogs_eks` CreateAccessKey | spath eventName | search eventName=CreateAccessKey "userIdentity.type"=IAMUser | table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId |`aws_detect_permanent_key_creation_filter` +`aws_cloudwatchlogs_eks` CreateAccessKey +| spath eventName +| search eventName=CreateAccessKey "userIdentity.type"=IAMUser +| table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId +|`aws_detect_permanent_key_creation_filter` ``` #### Associated Analytic Story +* AWS Cross Account Activity + + #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16464,25 +26989,30 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit | T1078 | x | x | #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context. #### References + #### Test Dataset + _version_: 1
--- + ### aws detect role creation This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-07-27
@@ -16490,15 +27020,21 @@ This search provides detection of role creation by IAM users. Role creation is a #### Search ``` -`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* | table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate | `aws_detect_role_creation_filter` +`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* +| table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate +| `aws_detect_role_creation_filter` ``` #### Associated Analytic Story +* AWS Cross Account Activity + + #### How To Implement You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16506,25 +27042,30 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit | T1078 | x | x | #### Kill Chain Phases + * Lateral Movement + #### Known False Positives CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases. #### References + #### Test Dataset + _version_: 1
--- + ### aws detect sts assume role abuse This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-07-27
@@ -16532,15 +27073,21 @@ This search provides detection of suspicious use of sts:AssumeRole. These tokens #### Search ``` -`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role | table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate | `aws_detect_sts_assume_role_abuse_filter` +`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role +| table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate +| `aws_detect_sts_assume_role_abuse_filter` ``` #### Associated Analytic Story +* AWS Cross Account Activity + + #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16548,25 +27095,30 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit | T1078 | x | x | #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse. #### References + #### Test Dataset + _version_: 1
--- + ### aws detect sts get session token abuse This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1550 +- **ATT&CK**: [T1550](https://attack.mitre.org/techniques/T1550/) - **Last Updated**: 2020-07-27
@@ -16574,15 +27126,23 @@ This search provides detection of suspicious use of sts:GetSessionToken. These t #### Search ``` -`aws_cloudwatchlogs_eks` ASIA userIdentity.type=IAMUser| spath eventName | search eventName=GetSessionToken | table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region | `aws_detect_sts_get_session_token_abuse_filter` +`aws_cloudwatchlogs_eks` ASIA userIdentity.type=IAMUser +| spath eventName +| search eventName=GetSessionToken +| table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region +| `aws_detect_sts_get_session_token_abuse_filter` ``` #### Associated Analytic Story +* AWS Cross Account Activity + + #### How To Implement You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16590,25 +27150,30 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit | T1550 | x | x | #### Kill Chain Phases + * Lateral Movement + #### Known False Positives Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used. #### References + #### Test Dataset + _version_: 1
--- + ### gcp detect oauth token abuse This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Data Models**: -- **ATT&CK**: T1078 +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-09-01
@@ -16616,15 +27181,21 @@ This search provides detection of possible GCP Oauth token abuse. GCP Oauth toke #### Search ``` -`google_gcp_pubsub_message` type.googleapis.com/google.cloud.audit.AuditLog |table protoPayload.@type protoPayload.status.details{}.@type protoPayload.status.details{}.violations{}.callerIp protoPayload.status.details{}.violations{}.type protoPayload.status.message | `gcp_detect_oauth_token_abuse_filter` +`google_gcp_pubsub_message` type.googleapis.com/google.cloud.audit.AuditLog +|table protoPayload.@type protoPayload.status.details{}.@type protoPayload.status.details{}.violations{}.callerIp protoPayload.status.details{}.violations{}.type protoPayload.status.message +| `gcp_detect_oauth_token_abuse_filter` ``` #### Associated Analytic Story +* GCP Cross Account Activity + + #### How To Implement You must install splunk GCP add-on. This search works with gcp:pubsub:message logs #### Required fields + #### ATT&CK | ID | technique | Tactic | @@ -16632,17 +27203,23 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo | T1078 | x | x | #### Kill Chain Phases + * Lateral Movement + #### Known False Positives GCP Oauth token abuse detection will only work if there are access policies in place along with audit logs. #### References + * https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-1 + * https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-2 + #### Test Dataset + _version_: 1
From 75c24a9f343064896aa4e7cee2c1c187c53e819f Mon Sep 17 00:00:00 2001 From: divious1 Date: Mon, 1 Mar 2021 13:49:16 -0500 Subject: [PATCH 07/62] added markup --- bin/doc_gen.py | 54 +- .../doc_detections_markdown.j2 | 29 +- bin/jinja2_templates/doc_detections_wiki.j2 | 78 + .../splunk_docs_categories.j2 | 65 - bin/jinja2_templates/stories_categories.j2 | 68 - docs/detections.md | 12941 ++---------- docs/detections.wiki | 17553 ++++++++++++++++ 7 files changed, 19767 insertions(+), 11021 deletions(-) create mode 100644 bin/jinja2_templates/doc_detections_wiki.j2 delete mode 100644 bin/jinja2_templates/splunk_docs_categories.j2 delete mode 100644 bin/jinja2_templates/stories_categories.j2 create mode 100644 docs/detections.wiki diff --git a/bin/doc_gen.py b/bin/doc_gen.py index b3a98fc183..4c1b2983a6 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -1,17 +1,44 @@ import glob import yaml import argparse -from os import path, walk import sys import re +from os import path, walk +import json from jinja2 import Environment, FileSystemLoader +from attackcti import attack_client +from pyattck import Attck +def mitre_attack_object(technique, attack): + mitre_attack = dict() + mitre_attack['technique_id'] = technique.id + mitre_attack['technique'] = technique.name + # process tactics + tactics = [] + for tactic in technique.tactics: + tactics.append(tactic.name) + mitre_attack['tactic'] = tactics + + return mitre_attack + +def get_mitre_enrichment_new(attack, mitre_attack_id): + for technique in attack.enterprise.techniques: + apt_groups = [] + if '.' in mitre_attack_id: + for subtechnique in technique.subtechniques: + if mitre_attack_id == subtechnique.id: + mitre_attack = mitre_attack_object(subtechnique, attack) + return mitre_attack + + elif mitre_attack_id == technique.id: + mitre_attack = mitre_attack_object(technique, attack) + return mitre_attack def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): - types = ["endpoint", "application", "cloud", "deprecated", "experimental", "network", "web"] + types = ["endpoint", "application", "cloud", "network", "web"] manifest_files = [] for t in types: for root, dirs, files in walk(REPO_PATH + '/detections/' + t): @@ -19,6 +46,9 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): if file.endswith(".yml"): manifest_files.append((path.join(root, file))) + if VERBOSE: + print("getting mitre enrichment data from cti") + attack = Attck() detections = [] for manifest_file in manifest_files: detection_yaml = dict() @@ -34,6 +64,14 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): error = True continue detection_yaml = object + + # enrich the mitre object + mitre_attacks = [] + if 'mitre_attack_id' in detection_yaml['tags']: + for mitre_technique_id in detection_yaml['tags']['mitre_attack_id']: + mitre_attack = get_mitre_enrichment_new(attack, mitre_technique_id) + mitre_attacks.append(mitre_attack) + detection_yaml['mitre_attacks'] = mitre_attacks detection_yaml['kind'] = manifest_file.split('/')[-2] detections.append(detection_yaml) @@ -41,14 +79,22 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), trim_blocks=False) + + # write markdown template = j2_env.get_template('doc_detections_markdown.j2') output_path = path.join(OUTPUT_DIR + '/detections.md') output = template.render(detections=sorted_detections) with open(output_path, 'w', encoding="utf-8") as f: f.write(output) - print("doc_gen.py wrote {0} detection documentation to: {1}".format(len(detections),output_path)) + print("doc_gen.py wrote {0} detections documentation in markdown to: {1}".format(len(detections),output_path)) - return False + # write wikimarkup + template = j2_env.get_template('doc_detections_wiki.j2') + output_path = path.join(OUTPUT_DIR + '/detections.wiki') + output = template.render(detections=sorted_detections) + with open(output_path, 'w', encoding="utf-8") as f: + f.write(output) + print("doc_gen.py wrote {0} detections documentation in mediawiki to: {1}".format(len(detections),output_path)) if __name__ == "__main__": diff --git a/bin/jinja2_templates/doc_detections_markdown.j2 b/bin/jinja2_templates/doc_detections_markdown.j2 index 92f0500d80..eb843c3b62 100644 --- a/bin/jinja2_templates/doc_detections_markdown.j2 +++ b/bin/jinja2_templates/doc_detections_markdown.j2 @@ -1,4 +1,3 @@ -#jinja2: trim_blocks:True # Splunk Security Content Detections ![security_content](static/logo.png) ===== @@ -6,7 +5,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by ## Cloud
- View + details {% for detection in detections %} {% if detection.kind == 'cloud' %} @@ -17,7 +16,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by ## Endpoint
- View + details {% for detection in detections %} {% if detection.kind == 'endpoint' %} @@ -28,7 +27,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by ## Network
- View + details {% for detection in detections %} {% if detection.kind == 'network' %} @@ -39,7 +38,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by ## Application
- View + details {% for detection in detections %} {% if detection.kind == 'application' %} @@ -50,7 +49,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by ## Web
- View + details {% for detection in detections %} {% if detection.kind == 'web' %} @@ -66,12 +65,12 @@ All the detections shipped to different Splunk products. Below is a breakdown by {{ detection.description }} - **Product**: {{ detection.tags.product|join(', ') }} -- **Data Models**: {{ detection.datamodel|join(', ') }} +- **Datamodel**: {{ detection.datamodel|join(', ') }} - **ATT&CK**: {% for mitre_attack_id in detection.tags.mitre_attack_id %}[{{ mitre_attack_id }}](https://attack.mitre.org/techniques/{{ mitre_attack_id }}/){% if not loop.last %}, {% endif %}{% endfor %} - **Last Updated**: {{ detection.date }}
- View + details #### Search ``` @@ -85,20 +84,20 @@ All the detections shipped to different Splunk products. Below is a breakdown by #### How To Implement {{ detection.how_to_implement}} -#### Required fields +#### Required field {% for field in detection.tags.required_fields %} * {{ field }} {% endfor %} #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -{%- for id in detection.tags.mitre_attack_id %} -| {{ id }} | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +{%- for attack in detection.mitre_attacks %} +| {{ attack.technique_id }} | {{ attack.technique }} | {{ attack.tactic|join(', ') }} | {%- endfor %} -#### Kill Chain Phases +#### Kill Chain Phase {% for phase in detection.tags.kill_chain_phases %} * {{ phase }} {% endfor %} @@ -106,7 +105,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by #### Known False Positives {{ detection.known_false_positives}} -#### References +#### Reference {% for reference in detection.references %} * {{ reference }} {% endfor %} diff --git a/bin/jinja2_templates/doc_detections_wiki.j2 b/bin/jinja2_templates/doc_detections_wiki.j2 new file mode 100644 index 0000000000..72776aab69 --- /dev/null +++ b/bin/jinja2_templates/doc_detections_wiki.j2 @@ -0,0 +1,78 @@ +=Splunk Security Content Detections = + +---- +All the detections shipped to different Splunk products. Below is a breakdown by kind. +==Cloud== +{% for detection in detections %} +{% if detection.kind == 'cloud' %} +* [[#{{ detection.name }}|{{ detection.name }}]] +{% endif %} +{% endfor %} +{% for detection in detections %} +==={% raw %}{{{% endraw %}visible anchor|{{ detection.name }}|{{ detection.name|lower|replace(" ", "-") }}{% raw %}}}{% endraw %}=== +{{ detection.description }} + +* '''Product''': {{ detection.tags.product|join(', ') }} +* '''Datamodel''': {{ detection.datamodel|join(', ') }} +* '''ATT&CK''': {% for mitre_attack_id in detection.tags.mitre_attack_id %}[https://attack.mitre.org/techniques/{{ mitre_attack_id }}/ {{ mitre_attack_id }}]{% if not loop.last %}, {% endif %}{% endfor %} +* '''Last Updated''': {{ detection.date }} + +
+
+ +====Search==== +{{ detection.search|replace("|", "\n|") }} + +====Associated Analytic Story==== +{% for story in detection.tags.analytic_story %} +* {{ story }} +{% endfor %} + +====How To Implement==== +{{ detection.how_to_implement}} + +====Required field==== +{% for field in detection.tags.required_fields %} +* {{ field }} +{% endfor %} + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +{%-for attack in detection.mitre_attacks %} +|- +| {{ attack.technique_id }} +| {{ attack.technique }} +| {{ attack.tactic|join(', ') }} +{%- endfor %} +|} + +====Kill Chain Phase==== +{% for phase in detection.tags.kill_chain_phases %} +* {{ phase }} +{% endfor %} + +====Known False Positives==== +{{ detection.known_false_positives}} + +====Reference==== +{% for reference in detection.references %} +* {{ reference }} +{% endfor %} + +====Test Dataset==== +{% for dataset in detection.tags.dataset %} +* {{ dataset }} +{% endfor %} + +''version'': {{detection.version}} +
+
+ +---- +{% endfor %} + + +[[Category:V:ESSOC:3.15.0]] diff --git a/bin/jinja2_templates/splunk_docs_categories.j2 b/bin/jinja2_templates/splunk_docs_categories.j2 deleted file mode 100644 index b0582430a8..0000000000 --- a/bin/jinja2_templates/splunk_docs_categories.j2 +++ /dev/null @@ -1,65 +0,0 @@ -= Use Case Categories= -The collapse... - - -{% for category in categories %} -=={{ category.name }}== - -{% for story in category.stories %} -==={{ story.name }}=== - -{{ story.description }} - -
-
- -====Narrative==== -{{ story.narrative }} - -====Detections==== -{% for detection in story.detections %} -* {{ detection }} -{% endfor %} - -====Data Models==== -{% for data_model in story.data_models %} -* {{ data_model }} -{% endfor %} - -====Tags==== - -=====ATT&CK===== -{% for mitre_attack_id in story.mitre_attack_ids %} -* {{ mitre_attack_id }} -{% endfor %} - -=====Kill Chain Phases===== -{% for kill_chain_phase in story.kill_chain_phases %} -* {{ kill_chain_phase }} -{% endfor %} - -=====CIS===== -{% for cis in story.ciss %} -* {{ cis }} -{% endfor %} - -=====NIST===== -{% for nist in story.nists %} -* {{ nist }} -{% endfor %} - -====References==== -{% for reference in story.references %} -* {{ reference }} -{% endfor %} - -date = {{ story.date }} - -version = {{ story.version }} - -
-
- -{% endfor %} - -{% endfor %} diff --git a/bin/jinja2_templates/stories_categories.j2 b/bin/jinja2_templates/stories_categories.j2 deleted file mode 100644 index 7862cd9ed5..0000000000 --- a/bin/jinja2_templates/stories_categories.j2 +++ /dev/null @@ -1,68 +0,0 @@ - -# Categories -Analytics stories organized by categories -{% for category in categories %} - -* [{{ category.name }}](#{{ category.name | replace(' ','-') }}) -{% endfor %} - - -{% for category in categories %} -## {{ category.name }} -{% for story in category.stories %} - -* [{{ story.name }}](#{{ story.name | replace(' ','-') }}) -{% endfor %} - -{% for story in category.stories %} -### {{ story.name }} -* id = {{ story.id }} -* date = {{ story.date }} -* version = {{ story.version }} - -#### Description -{{ story.description }} - -#### Narrative -{{ story.narrative }} - -#### Detections -{% for detection in story.detections %} -* {{ detection }} -{% endfor %} - -#### Data Models -{% for data_model in story.data_models %} -* {{ data_model }} -{% endfor %} - -#### Mappings - -##### ATT&CK -{% for mitre_attack_id in story.mitre_attack_ids %} -* {{ mitre_attack_id }} -{% endfor %} - -##### Kill Chain Phases -{% for kill_chain_phase in story.kill_chain_phases %} -* {{ kill_chain_phase }} -{% endfor %} - -###### CIS -{% for cis in story.ciss %} -* {{ cis }} -{% endfor %} - -##### NIST -{% for nist in story.nists %} -* {{ nist }} -{% endfor %} - -##### References -{% for reference in story.references %} -* {{ reference }} -{% endfor %} - -{% endfor %} - -{% endfor %} diff --git a/docs/detections.md b/docs/detections.md index e12218a47c..c9b7a290b5 100644 --- a/docs/detections.md +++ b/docs/detections.md @@ -1,4 +1,3 @@ -#jinja2: trim_blocks:True # Splunk Security Content Detections ![security_content](static/logo.png) ===== @@ -6,15 +5,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by ## Cloud
- View - - - - - - - - + details @@ -30,10 +21,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [AWS EKS Kubernetes cluster sensitive object access](#aws-eks-kubernetes-cluster-sensitive-object-access) - - - - [AWS Network Access Control List Created with All Open Ports](#aws-network-access-control-list-created-with-all-open-ports) @@ -50,14 +37,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - [Abnormally High Number Of Cloud Infrastructure API Calls](#abnormally-high-number-of-cloud-infrastructure-api-calls) @@ -76,18 +55,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [Amazon EKS Kubernetes Pod scan detection](#amazon-eks-kubernetes-pod-scan-detection) - - - -- [Amazon EKS Kubernetes cluster scan detection](#amazon-eks-kubernetes-cluster-scan-detection) - - - - - - - @@ -134,8 +101,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Cloud Provisioning Activity From Previously Unseen City](#cloud-provisioning-activity-from-previously-unseen-city) @@ -187,16 +152,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - @@ -231,16 +186,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - @@ -259,14 +204,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - @@ -325,8 +262,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - @@ -338,8 +273,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Detect Spike in AWS Security Hub Alerts for EC2 Instance](#detect-spike-in-aws-security-hub-alerts-for-ec2-instance) @@ -348,14 +281,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Detect Spike in S3 Bucket deletion](#detect-spike-in-s3-bucket-deletion) - - - [Detect Spike in blocked Outbound Traffic from your AWS](#detect-spike-in-blocked-outbound-traffic-from-your-aws) @@ -400,68 +329,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- [GCP Detect accounts with high risk roles by project](#gcp-detect-accounts-with-high-risk-roles-by-project) - - - -- [GCP Detect gcploit framework](#gcp-detect-gcploit-framework) - - - -- [GCP Detect high risk permissions by resource and account](#gcp-detect-high-risk-permissions-by-resource-and-account) - - - - - -- [GCP Kubernetes cluster pod scan detection](#gcp-kubernetes-cluster-pod-scan-detection) - - - -- [GCP Kubernetes cluster scan detection](#gcp-kubernetes-cluster-scan-detection) - - - - - - [High Number of Login Failures from a single source](#high-number-of-login-failures-from-a-single-source) @@ -483,98 +350,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - -- [Kubernetes AWS detect RBAC authorization by account](#kubernetes-aws-detect-rbac-authorization-by-account) - - - -- [Kubernetes AWS detect most active service accounts by pod](#kubernetes-aws-detect-most-active-service-accounts-by-pod) - - - -- [Kubernetes AWS detect sensitive role access](#kubernetes-aws-detect-sensitive-role-access) - - - -- [Kubernetes AWS detect service accounts forbidden failure access](#kubernetes-aws-detect-service-accounts-forbidden-failure-access) - - - -- [Kubernetes AWS detect suspicious kubectl calls](#kubernetes-aws-detect-suspicious-kubectl-calls) - - - -- [Kubernetes Azure detect RBAC authorization by account](#kubernetes-azure-detect-rbac-authorization-by-account) - - - -- [Kubernetes Azure detect most active service accounts by pod namespace](#kubernetes-azure-detect-most-active-service-accounts-by-pod-namespace) - - - -- [Kubernetes Azure detect sensitive object access](#kubernetes-azure-detect-sensitive-object-access) - - - -- [Kubernetes Azure detect sensitive role access](#kubernetes-azure-detect-sensitive-role-access) - - - -- [Kubernetes Azure detect service accounts forbidden failure access](#kubernetes-azure-detect-service-accounts-forbidden-failure-access) - - - -- [Kubernetes Azure detect suspicious kubectl calls](#kubernetes-azure-detect-suspicious-kubectl-calls) - - - -- [Kubernetes Azure pod scan fingerprint](#kubernetes-azure-pod-scan-fingerprint) - - - -- [Kubernetes Azure scan fingerprint](#kubernetes-azure-scan-fingerprint) - - - -- [Kubernetes GCP detect RBAC authorizations by account](#kubernetes-gcp-detect-rbac-authorizations-by-account) - - - -- [Kubernetes GCP detect most active service accounts by pod](#kubernetes-gcp-detect-most-active-service-accounts-by-pod) - - - -- [Kubernetes GCP detect sensitive object access](#kubernetes-gcp-detect-sensitive-object-access) - - - -- [Kubernetes GCP detect sensitive role access](#kubernetes-gcp-detect-sensitive-role-access) - - - -- [Kubernetes GCP detect service accounts forbidden failure access](#kubernetes-gcp-detect-service-accounts-forbidden-failure-access) - - - -- [Kubernetes GCP detect suspicious kubectl calls](#kubernetes-gcp-detect-suspicious-kubectl-calls) - - - - - - - - - - @@ -602,8 +377,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [O365 Add App Role Assignment Grant User](#o365-add-app-role-assignment-grant-user) @@ -811,121 +584,11 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- [aws detect attach to role policy](#aws-detect-attach-to-role-policy) - - - -- [aws detect permanent key creation](#aws-detect-permanent-key-creation) - - - -- [aws detect role creation](#aws-detect-role-creation) - - - -- [aws detect sts assume role abuse](#aws-detect-sts-assume-role-abuse) - - - -- [aws detect sts get session token abuse](#aws-detect-sts-get-session-token-abuse) - - - -- [gcp detect oauth token abuse](#gcp-detect-oauth-token-abuse) - -
## Endpoint
- View - - - - - - - - - - - - - - - - - - + details @@ -955,10 +618,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - [Applying Stolen Credentials via Mimikatz modules](#applying-stolen-credentials-via-mimikatz-modules) @@ -1003,14 +662,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [Child Processes of Spoolsv exe](#child-processes-of-spoolsv-exe) - - - - - - - @@ -1115,10 +766,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - [Deleting Shadow Copies](#deleting-shadow-copies) @@ -1131,28 +778,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - [Detect Activity Related to Pass the Hash Attacks](#detect-activity-related-to-pass-the-hash-attacks) -- [Detect Baron Samedit CVE-2021-3156](#detect-baron-samedit-cve-2021-3156) - - - -- [Detect Baron Samedit CVE-2021-3156 Segfault](#detect-baron-samedit-cve-2021-3156-segfault) - - - -- [Detect Baron Samedit CVE-2021-3156 via OSQuery](#detect-baron-samedit-cve-2021-3156-via-osquery) - - - - [Detect Computer Changed with Anonymous Account](#detect-computer-changed-with-anonymous-account) @@ -1161,8 +790,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Detect Dump LSASS Memory using comsvcs](#detect-dump-lsass-memory-using-comsvcs) @@ -1177,8 +804,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Detect HTML Help Renamed](#detect-html-help-renamed) @@ -1203,16 +828,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Detect MSHTA Url in Command Line](#detect-mshta-url-in-command-line) - - - - - [Detect New Local Admin account](#detect-new-local-admin-account) @@ -1225,12 +844,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [Detect Oulook exe writing a zip file](#detect-oulook-exe-writing-a--zip-file) - - - - - - [Detect Pass the Hash](#detect-pass-the-hash) @@ -1312,14 +925,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - @@ -1341,10 +946,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - [Detect mshta inline hta execution](#detect-mshta-inline-hta-execution) @@ -1353,22 +954,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - [Detect processes used for System Network Configuration Discovery](#detect-processes-used-for-system-network-configuration-discovery) - - - - -- [Detection of tools built by NirSoft](#detection-of-tools-built-by-nirsoft) - - - - [Disabling Remote User Account Control](#disabling-remote-user-account-control) @@ -1384,24 +973,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - [Dump LSASS via procdump Rename](#dump-lsass-via-procdump-rename) - - - - - - - - - - - - - - - - - - @@ -1409,8 +980,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [File with Samsam Extension](#file-with-samsam-extension) @@ -1419,38 +988,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [First Time Seen Running Windows Service](#first-time-seen-running-windows-service) - - - - [First time seen command line argument](#first-time-seen-command-line-argument) - - - - - - - - - - - - - - - [Hiding Files And Directories With Attrib exe](#hiding-files-and-directories-with-attrib-exe) - - - - - [Illegal Access To User Content via PowerSploit modules](#illegal-access-to-user-content-via-powersploit-modules) @@ -1495,50 +1042,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- [MacOS - Re-opened Applications](#macos---re-opened-applications) - - - - [Malicious PowerShell Process - Connect To Internet With Hidden Window](#malicious-powershell-process---connect-to-internet-with-hidden-window) @@ -1551,22 +1054,14 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Malicious PowerShell Process With Obfuscation Techniques](#malicious-powershell-process-with-obfuscation-techniques) - - - - - [Monitor Registry Keys for Print Monitors](#monitor-registry-keys-for-print-monitors) - - - [More than usual number of LOLBAS applications in short time period](#more-than-usual-number-of-lolbas-applications-in-short-time-period) @@ -1579,8 +1074,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Ntdsutil export ntds](#ntdsutil-export-ntds) @@ -1608,10 +1101,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - @@ -1621,8 +1110,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Probing Access with Stolen Credentials via PowerSploit modules](#probing-access-with-stolen-credentials-via-powersploit-modules) @@ -1635,24 +1122,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [Processes Tapping Keyboard Events](#processes-tapping-keyboard-events) - - - - - - [Processes launching netsh](#processes-launching-netsh) - - - - - - - - - [Rare Parent-Child Process Relationship](#rare-parent-child-process-relationship) @@ -1721,8 +1194,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Registry Keys Used For Persistence](#registry-keys-used-for-persistence) @@ -1739,18 +1210,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [Remote Desktop Process Running On System](#remote-desktop-process-running-on-system) - - - - [Remote Process Instantiation via WMI](#remote-process-instantiation-via-wmi) - - - - - [RunDLL Loading DLL By Ordinal](#rundll-loading-dll-by-ordinal) @@ -1763,8 +1226,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Samsam Test File Write](#samsam-test-file-write) @@ -1777,8 +1238,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Schtasks scheduling job on remote system](#schtasks-scheduling-job-on-remote-system) @@ -1819,30 +1278,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - -- [Spike in File Writes](#spike-in-file-writes) - - - - - -- [Sunburst Correlation DLL and Network Event](#sunburst-correlation-dll-and-network-event) - - - - - - - - - - - - - - - - [Suspicious MSBuild Rename](#suspicious-msbuild-rename) @@ -1899,8 +1334,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Suspicious writes to windows Recycle Bin](#suspicious-writes-to-windows-recycle-bin) @@ -1923,16 +1356,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Unload Sysmon Filter Driver](#unload-sysmon-filter-driver) - - - - - [Unusually Long Command Line](#unusually-long-command-line) @@ -1951,18 +1378,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [WMI Permanent Event Subscription](#wmi-permanent-event-subscription) - - - - [WMI Permanent Event Subscription - Sysmon](#wmi-permanent-event-subscription---sysmon) -- [WMI Temporary Event Subscription](#wmi-temporary-event-subscription) - - - @@ -1975,8 +1394,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Windows Event Log Cleared](#windows-event-log-cleared) @@ -1984,55 +1401,11 @@ All the detections shipped to different Splunk products. Below is a breakdown by - [Windows Security Account Manager Stopped](#windows-security-account-manager-stopped) - - - - - - - - - - - - - - - -
## Network
- View - - - - - - - - - - - - - - - - - - - - - - - - - - - - + details @@ -2152,30 +1525,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [DNS record changed](#dns-record-changed) - - - - - - - -- [Detect ARP Poisoning](#detect-arp-poisoning) - - - - - - - - - - - - - - - @@ -2223,16 +1572,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - @@ -2300,18 +1639,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - [Detect Traffic Mirroring](#detect-traffic-mirroring) - - - [Detect Unauthorized Assets by MAC address](#detect-unauthorized-assets-by-mac-address) @@ -2330,8 +1661,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - [Detect hosts connecting to dynamic domain providers](#detect-hosts-connecting-to-dynamic-domain-providers) @@ -2378,7 +1707,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [Excessive DNS Failures](#excessive-dns-failures) @@ -2414,7 +1742,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [Hosts receiving high volume of network traffic from email server](#hosts-receiving-high-volume-of-network-traffic-from-email-server) @@ -2461,140 +1788,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - - -- [Large Volume of DNS ANY Queries](#large-volume-of-dns-any-queries) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- [Prohibited Network Traffic Allowed](#prohibited-network-traffic-allowed) - - - - - -- [Protocol or Port Mismatch](#protocol-or-port-mismatch) - - - -- [Protocols passing authentication in cleartext](#protocols-passing-authentication-in-cleartext) - - - - - - - - - - - - - - - - - - - - @@ -2634,12 +1827,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - [SMB Traffic Spike](#smb-traffic-spike) @@ -2689,32 +1876,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - - - - - - - - - @@ -2750,12 +1911,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - [Unusually Long Content-Type Length](#unusually-long-content-type-length) @@ -2777,33 +1932,11 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - - - - -
## Application
- View + details @@ -3013,7 +2146,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [Detect New Login Attempts to Routers](#detect-new-login-attempts-to-routers) @@ -3048,106 +2180,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- [Email Attachments With Lots Of Spaces](#email-attachments-with-lots-of-spaces) - - - - [Email files written outside of the Outlook directory](#email-files-written-outside-of-the-outlook-directory) -- [Email servers sending high volume traffic to hosts](#email-servers-sending-high-volume-traffic-to-hosts) @@ -3194,87 +2230,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- [Monitor Email For Brand Abuse](#monitor-email-for-brand-abuse) - - - - - - - - - - [Multiple Okta Users With Invalid Credentials From The Same IP](#multiple-okta-users-with-invalid-credentials-from-the-same-ip) @@ -3283,10 +2238,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [No Windows Updates in a time frame](#no-windows-updates-in-a-time-frame) - - - @@ -3329,7 +2280,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [Phishing Email Detection by Machine Learning Method - SSA](#phishing-email-detection-by-machine-learning-method---ssa) @@ -3424,77 +2374,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - - - - -- [Spectre and Meltdown Vulnerable Systems](#spectre-and-meltdown-vulnerable-systems) - - - - - - - - - - - - - -- [Suspicious Email - UBA Anomaly](#suspicious-email---uba-anomaly) - - - -- [Suspicious Email Attachment Extensions](#suspicious-email-attachment-extensions) - - - - - -- [Suspicious Java Classes](#suspicious-java-classes) - - - - - - - - - - - - - - - - - - - - - - - - - @@ -3551,24 +2430,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - @@ -3576,7 +2437,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by ## Web
- View + details @@ -3758,7 +2619,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [Detect F5 TMUI RCE CVE-2020-5902](#detect-f5-tmui-rce-cve-2020-5902) @@ -3872,13 +2732,11 @@ All the detections shipped to different Splunk products. Below is a breakdown by -- [Detect attackers scanning for vulnerable JBoss servers](#detect-attackers-scanning-for-vulnerable-jboss-servers) -- [Detect malicious requests to exploit JBoss servers](#detect-malicious-requests-to-exploit-jboss-servers) @@ -4019,249 +2877,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - - - - -- [Monitor Web Traffic For Brand Abuse](#monitor-web-traffic-for-brand-abuse) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- [SQL Injection with Long URLs](#sql-injection-with-long-urls) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- [Supernova Webshell](#supernova-webshell) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - @@ -4310,24 +2925,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by - - - - - - - - - - - - - - - - - - @@ -4336,277 +2933,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by -### AWS Cloud Provisioning From Previously Unseen City -This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) -- **Last Updated**: 2018-03-16 - -
- View - -#### Search -``` -`cloudtrail` (eventName=Run* OR eventName=Create*) -| iplocation sourceIPAddress -| search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) -| iplocation sourceIPAddress -| search City=* -| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country -| inputlookup append=t previously_seen_provisioning_activity_src.csv -| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country -| outputlookup previously_seen_provisioning_activity_src.csv -| stats min(firstTime) as firstTime max(lastTime) as lastTime by City -| eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) -| where newCity=1 -| table City] -| spath output=user userIdentity.arn -| rename sourceIPAddress as src_ip -| table _time, user, src_ip, City, eventName, errorCode -| `aws_cloud_provisioning_from_previously_unseen_city_filter` -``` -#### Associated Analytic Story - -* AWS Suspicious Provisioning Activities - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases - - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### AWS Cloud Provisioning From Previously Unseen Country -This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) -- **Last Updated**: 2018-03-16 - -
- View - -#### Search -``` -`cloudtrail` (eventName=Run* OR eventName=Create*) -| iplocation sourceIPAddress -| search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) -| iplocation sourceIPAddress -| search Country=* -| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country -| inputlookup append=t previously_seen_provisioning_activity_src.csv -| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country -| outputlookup previously_seen_provisioning_activity_src.csv -| stats min(firstTime) as firstTime max(lastTime) as lastTime by Country -| eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) -| where newCountry=1 -| table Country] -| spath output=user userIdentity.arn -| rename sourceIPAddress as src_ip -| table _time, user, src_ip, Country, eventName, errorCode -| `aws_cloud_provisioning_from_previously_unseen_country_filter` -``` -#### Associated Analytic Story - -* AWS Suspicious Provisioning Activities - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases - - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### AWS Cloud Provisioning From Previously Unseen IP Address -This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-03-16 - -
- View - -#### Search -``` -`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) -| iplocation sourceIPAddress -| search Country=* -| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country -| inputlookup append=t previously_seen_provisioning_activity_src.csv -| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country -| outputlookup previously_seen_provisioning_activity_src.csv -| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress -| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) -| where newIP=1 -| table sourceIPAddress] -| spath output=user userIdentity.arn -| rename sourceIPAddress as src_ip -| table _time, user, src_ip, eventName, errorCode -| `aws_cloud_provisioning_from_previously_unseen_ip_address_filter` -``` -#### Associated Analytic Story - -* AWS Suspicious Provisioning Activities - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### AWS Cloud Provisioning From Previously Unseen Region -This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) -- **Last Updated**: 2018-03-16 - -
- View - -#### Search -``` -`cloudtrail` (eventName=Run* OR eventName=Create*) -| iplocation sourceIPAddress -| search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) -| iplocation sourceIPAddress -| search Region=* -| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country -| inputlookup append=t previously_seen_provisioning_activity_src.csv -| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country -| outputlookup previously_seen_provisioning_activity_src.csv -| stats min(firstTime) as firstTime max(lastTime) as lastTime by Region -| eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) -| where newRegion=1 -| table Region] -| spath output=user userIdentity.arn -| rename sourceIPAddress as src_ip -| table _time, user, src_ip, Region, eventName, errorCode -| `aws_cloud_provisioning_from_previously_unseen_region_filter` -``` -#### Associated Analytic Story - -* AWS Suspicious Provisioning Activities - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases - - -#### Known False Positives -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### AWS Cross Account Activity From Previously Unseen Account This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. This search is deprecated and have been translated to use the latest Authentication Datamodel. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Authentication +- **Datamodel**: Authentication - **ATT&CK**: - **Last Updated**: 2020-05-28
- View + details #### Search ``` @@ -4631,15 +2967,15 @@ This search looks for AssumeRole events where an IAM role in a different account #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen AWS Cross Account Activity - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen AWS Cross Account Activity - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `aws_cross_account_activity_from_previously_unseen_account_filter` macro. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -4647,7 +2983,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y #### Known False Positives Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. -#### References +#### Reference #### Test Dataset @@ -4664,12 +3000,12 @@ _version_: 1 This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/) - **Last Updated**: 2021-01-11
- View + details #### Search ``` @@ -4694,22 +3030,22 @@ This search provides detection of KMS keys which action kms:Encrypt is accessibl #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1486 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1486 | Data Encrypted for Impact | Impact | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives unknown -#### References +#### Reference * https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ @@ -4732,12 +3068,12 @@ _version_: 1 This search provides detection of users with KMS keys performing encryption specifically against S3 buckets. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/) - **Last Updated**: 2021-01-11
- View + details #### Search ``` @@ -4756,22 +3092,22 @@ This search provides detection of users with KMS keys performing encryption spec #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1486 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1486 | Data Encrypted for Impact | Impact | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives bucket with S3 encryption -#### References +#### Reference * https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ @@ -4785,59 +3121,6 @@ bucket with S3 encryption * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json -_version_: 1 -
- ---- - -### AWS EKS Kubernetes cluster sensitive object access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 -|table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason -|dedup user.username user.groups{} -|`aws_eks_kubernetes_cluster_sensitive_object_access_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Object Access Activity - - -#### How To Implement -You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. This search works with cloudwatch logs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. - -#### References - - -#### Test Dataset - - _version_: 1
@@ -4847,12 +3130,12 @@ _version_: 1 The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562.007/) - **Last Updated**: 2021-01-11
- View + details #### Search ``` @@ -4874,16 +3157,16 @@ The search looks for CloudTrail events to detect if any network ACLs were create #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.007 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -4891,7 +3174,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Known False Positives It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. -#### References +#### Reference #### Test Dataset @@ -4908,12 +3191,12 @@ _version_: 2 Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562.007/) - **Last Updated**: 2021-01-12
- View + details #### Search ``` @@ -4932,16 +3215,16 @@ Enforcing network-access controls is one of the defensive mechanisms used by clo #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.007 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -4949,7 +3232,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Known False Positives It's possible that a user has legitimately deleted a network ACL. -#### References +#### Reference #### Test Dataset @@ -4966,12 +3249,12 @@ _version_: 2 This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2021-01-26
- View + details #### Search ``` @@ -4989,22 +3272,22 @@ This search provides specific SAML access from specific Service Provider, user a #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks. -#### References +#### Reference * https://us-cert.cisa.gov/ncas/alerts/aa21-008a @@ -5029,12 +3312,12 @@ _version_: 1 This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2021-01-26
- View + details #### Search ``` @@ -5052,22 +3335,22 @@ This search provides detection of updates to SAML provider in AWS. Updates to SA #### How To Implement You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored. -#### References +#### Reference * https://us-cert.cisa.gov/ncas/alerts/aa21-008a @@ -5088,252 +3371,16 @@ _version_: 1 --- -### Abnormally High AWS Instances Launched by User -This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=RunInstances errorCode=success -| bucket span=10m _time -| stats count AS instances_launched by _time userName -| eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev -| eval threshold_value = 4 -| eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) -| search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") -| eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) -| table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev -| `abnormally_high_aws_instances_launched_by_user_filter` -``` -#### Associated Analytic Story - -* AWS Cryptomining - -* Suspicious AWS EC2 Activities - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Abnormally High AWS Instances Launched by User - MLTK -This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` -| bucket span=10m _time -| stats count as instances_launched by _time src_user -| apply ec2_excessive_runinstances_v1 -| rename "IsOutlier(instances_launched)" as isOutlier -| where isOutlier=1 -``` -#### Associated Analytic Story - -* AWS Cryptomining - -* Suspicious AWS EC2 Activities - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Abnormally High AWS Instances Terminated by User -This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=TerminateInstances errorCode=success -| bucket span=10m _time -| stats count AS instances_terminated by _time userName -| eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev -| eval threshold_value = 4 -| eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) -| search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") -| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) -|table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev -| `abnormally_high_aws_instances_terminated_by_user_filter` -``` -#### Associated Analytic Story - -* Suspicious AWS EC2 Activities - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Abnormally High AWS Instances Terminated by User - MLTK -This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` -| bucket span=10m _time -| stats count as instances_terminated by _time src_user -| apply ec2_excessive_terminateinstances_v1 -| rename "IsOutlier(instances_terminated)" as isOutlier -| where isOutlier=1 -``` -#### Associated Analytic Story - -* Suspicious AWS EC2 Activities - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Abnormally High Number Of Cloud Infrastructure API Calls This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-09-07
- View + details #### Search ``` @@ -5363,16 +3410,16 @@ This search will detect a spike in the number of API calls made to your cloud in #### How To Implement You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to create the probability density function. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -5380,7 +3427,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base #### Known False Positives -#### References +#### Reference #### Test Dataset @@ -5397,12 +3444,12 @@ _version_: 1 This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-08-21
- View + details #### Search ``` @@ -5431,16 +3478,16 @@ This search finds for the number successfully destroyed cloud instances for ever #### How To Implement You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability density function. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -5448,7 +3495,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base #### Known False Positives Many service accounts configured within a cloud infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -#### References +#### Reference #### Test Dataset @@ -5463,12 +3510,12 @@ _version_: 1 This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-08-21
- View + details #### Search ``` @@ -5499,16 +3546,16 @@ This search finds for the number successfully created cloud instances for every #### How To Implement You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Launched` to create the probability density function. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -5516,7 +3563,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base #### Known False Positives Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -#### References +#### Reference #### Test Dataset @@ -5531,12 +3578,12 @@ _version_: 2 This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-09-07
- View + details #### Search ``` @@ -5566,16 +3613,16 @@ This search will detect a spike in the number of API calls made to your cloud in #### How To Implement You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to create the probability density function model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -5583,7 +3630,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base #### Known False Positives -#### References +#### Reference #### Test Dataset @@ -5600,12 +3647,12 @@ _version_: 1 Detect memory dumping of the LSASS process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2019-12-06
- View + details #### Search ``` @@ -5624,16 +3671,16 @@ Detect memory dumping of the LSASS process. #### How To Implement This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.001 | LSASS Memory | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -5641,7 +3688,7 @@ This search requires Sysmon Logs and a Sysmon configuration, which includes Even #### Known False Positives Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. -#### References +#### Reference * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf @@ -5656,128 +3703,16 @@ _version_: 2 --- -### Amazon EKS Kubernetes Pod scan detection -This search provides detection information on unauthenticated requests against Kubernetes' Pods API - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) -- **Last Updated**: 2020-04-15 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" -| rename source as cluster_name sourceIPs{} as src_ip -| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `amazon_eks_kubernetes_pod_scan_detection_filter` -``` -#### Associated Analytic Story - -* Kubernetes Scanning Activity - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1526 | x | x | - -#### Kill Chain Phases - -* Reconnaissance - - -#### Known False Positives -Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Amazon EKS Kubernetes cluster scan detection -This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) -- **Last Updated**: 2020-04-15 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" -| rename sourceIPs{} as src_ip -| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -|`amazon_eks_kubernetes_cluster_scan_detection_filter` -``` -#### Associated Analytic Story - -* Kubernetes Scanning Activity - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1526 | x | x | - -#### Kill Chain Phases - -* Reconnaissance - - -#### Known False Positives -Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Applying Stolen Credentials via Mimikatz modules This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/), [T1134](https://attack.mitre.org/techniques/T1134/), [T1543](https://attack.mitre.org/techniques/T1543/), [T1547](https://attack.mitre.org/techniques/T1547/), [T1548](https://attack.mitre.org/techniques/T1548/), [T1554](https://attack.mitre.org/techniques/T1554/), [T1556](https://attack.mitre.org/techniques/T1556/), [T1558](https://attack.mitre.org/techniques/T1558/) - **Last Updated**: 2020-11-03
- View + details #### Search ``` @@ -5796,7 +3731,7 @@ This detection indicates use of Mimikatz modules that facilitate Pass-the-Token #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -5809,21 +3744,21 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1055 | x | x | -| T1068 | x | x | -| T1078 | x | x | -| T1098 | x | x | -| T1134 | x | x | -| T1543 | x | x | -| T1547 | x | x | -| T1548 | x | x | -| T1554 | x | x | -| T1556 | x | x | -| T1558 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1055 | Process Injection | Defense Evasion, Privilege Escalation | +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1098 | Account Manipulation | Persistence | +| T1134 | Access Token Manipulation | Defense Evasion, Privilege Escalation | +| T1543 | Create or Modify System Process | Persistence, Privilege Escalation | +| T1547 | Boot or Logon Autostart Execution | Persistence, Privilege Escalation | +| T1548 | Abuse Elevation Control Mechanism | Defense Evasion, Privilege Escalation | +| T1554 | Compromise Client Software Binary | Persistence | +| T1556 | Modify Authentication Process | Credential Access, Defense Evasion | +| T1558 | Steal or Forge Kerberos Tickets | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -5831,7 +3766,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -5850,12 +3785,12 @@ _version_: 1 Stolen credentials are applied by methods such as user impersonation, credential injection, spoofing of authentication processes or getting hold of critical accounts. This detection indicates such activities carried out by PowerSploit exploit kit APIs. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/), [T1134](https://attack.mitre.org/techniques/T1134/), [T1543](https://attack.mitre.org/techniques/T1543/), [T1547](https://attack.mitre.org/techniques/T1547/), [T1548](https://attack.mitre.org/techniques/T1548/), [T1554](https://attack.mitre.org/techniques/T1554/), [T1556](https://attack.mitre.org/techniques/T1556/), [T1558](https://attack.mitre.org/techniques/T1558/) - **Last Updated**: 2020-11-03
- View + details #### Search ``` @@ -5874,7 +3809,7 @@ Stolen credentials are applied by methods such as user impersonation, credential #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -5887,21 +3822,21 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1055 | x | x | -| T1068 | x | x | -| T1078 | x | x | -| T1098 | x | x | -| T1134 | x | x | -| T1543 | x | x | -| T1547 | x | x | -| T1548 | x | x | -| T1554 | x | x | -| T1556 | x | x | -| T1558 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1055 | Process Injection | Defense Evasion, Privilege Escalation | +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1098 | Account Manipulation | Persistence | +| T1134 | Access Token Manipulation | Defense Evasion, Privilege Escalation | +| T1543 | Create or Modify System Process | Persistence, Privilege Escalation | +| T1547 | Boot or Logon Autostart Execution | Persistence, Privilege Escalation | +| T1548 | Abuse Elevation Control Mechanism | Defense Evasion, Privilege Escalation | +| T1554 | Compromise Client Software Binary | Persistence | +| T1556 | Modify Authentication Process | Credential Access, Defense Evasion | +| T1558 | Steal or Forge Kerberos Tickets | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -5909,7 +3844,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -5926,12 +3861,12 @@ _version_: 1 This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/), [T1087](https://attack.mitre.org/techniques/T1087/), [T1201](https://attack.mitre.org/techniques/T1201/), [T1552](https://attack.mitre.org/techniques/T1552/), [T1555](https://attack.mitre.org/techniques/T1555/) - **Last Updated**: 2020-11-03
- View + details #### Search ``` @@ -5950,7 +3885,7 @@ This detection identifies use of DSInternals modules that verify password streng #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -5963,16 +3898,16 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | -| T1098 | x | x | -| T1087 | x | x | -| T1201 | x | x | -| T1552 | x | x | -| T1555 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1098 | Account Manipulation | Persistence | +| T1087 | Account Discovery | Discovery | +| T1201 | Password Policy Discovery | Discovery | +| T1552 | Unsecured Credentials | Credential Access | +| T1555 | Credentials from Password Stores | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -5980,7 +3915,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/MichaelGrafnetter/DSInternals @@ -5997,12 +3932,12 @@ _version_: 1 Attempt to add a certificate to the certificate store - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1553.004](https://attack.mitre.org/techniques/T1553.004/) - **Last Updated**: 2020-11-03
- View + details #### Search ``` @@ -6021,16 +3956,16 @@ Attempt to add a certificate to the certificate store #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1553.004 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1553.004 | Install Root Certificate | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Installation @@ -6040,7 +3975,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. -#### References +#### Reference #### Test Dataset @@ -6057,12 +3992,12 @@ _version_: 6 Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/) - **Last Updated**: 2020-11-06
- View + details #### Search ``` @@ -6083,16 +4018,16 @@ Monitor for changes of the ExecutionPolicy in the registry to the values "unrest #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059.001 | PowerShell | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Installation @@ -6102,7 +4037,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. -#### References +#### Reference #### Test Dataset @@ -6119,12 +4054,12 @@ _version_: 6 This search looks for attempts to stop security-related services on the endpoint. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562.001/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -6145,16 +4080,16 @@ This search looks for attempts to stop security-related services on the endpoint #### How To Implement You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1562.001 | Disable or Modify Tools | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Installation @@ -6164,7 +4099,7 @@ You must be ingesting data that records the file-system activity from your hosts #### Known False Positives None identified. Attempts to disable security-related services should be identified and understood. -#### References +#### Reference #### Test Dataset @@ -6180,13 +4115,71 @@ _version_: 3 ### Attempted Credential Dump From Registry via Reg exe Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/) +- **Last Updated**: 2019-12-02 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` +``` +#### Associated Analytic Story + +* Credential Dumping + + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.002 | Security Account Manager | Credential Access | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +None identified. + +#### Reference + + +#### Test Dataset + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + + +_version_: 4 +
+ +--- + +### Attempted Credential Dump From Registry via Reg exe +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-6-04
- View + details #### Search ``` @@ -6207,7 +4200,7 @@ Monitor for execution of reg.exe with parameters specifying an export of keys th #### How To Implement You must be ingesting windows endpoint data that tracks process activity, including parent-child relationships from your endpoints. -#### Required fields +#### Required field * process_name @@ -6222,11 +4215,11 @@ You must be ingesting windows endpoint data that tracks process activity, includ #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003 | OS Credential Dumping | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -6234,7 +4227,7 @@ You must be ingesting windows endpoint data that tracks process activity, includ #### Known False Positives None identified. -#### References +#### Reference * https://github.com/splunk/security_content/blob/55a17c65f9f56c2220000b62701765422b46125d/detections/attempted_credential_dump_from_registry_via_reg_exe.yml @@ -6247,74 +4240,16 @@ _version_: 1 --- -### Attempted Credential Dump From Registry via Reg exe -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/) -- **Last Updated**: 2019-12-02 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `attempted_credential_dump_from_registry_via_reg_exe_filter` -``` -#### Associated Analytic Story - -* Credential Dumping - - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.002 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -None identified. - -#### References - - -#### Test Dataset - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - - -_version_: 4 -
- ---- - ### BCDEdit Failure Recovery Modification This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/) - **Last Updated**: 2020-12-21
- View + details #### Search ``` @@ -6335,16 +4270,16 @@ This search looks for flags passed to bcdedit.exe modifications to the built-in #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1490 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1490 | Inhibit System Recovery | Impact | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -6352,7 +4287,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives Administrators may modify the boot configuration. -#### References +#### Reference * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair @@ -6371,12 +4306,12 @@ _version_: 1 The search looks for a batch file (.bat) written to the Windows system directory tree. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1204.002](https://attack.mitre.org/techniques/T1204.002/) - **Last Updated**: 2018-12-14
- View + details #### Search ``` @@ -6397,16 +4332,16 @@ The search looks for a batch file (.bat) written to the Windows system directory #### How To Implement You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1204.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1204.002 | Malicious File | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Delivery @@ -6414,7 +4349,7 @@ You must be ingesting data that records the file-system activity from your hosts #### Known False Positives It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. -#### References +#### Reference #### Test Dataset @@ -6431,12 +4366,12 @@ _version_: 1 This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: - **Last Updated**: 2021-01-26
- View + details #### Search ``` @@ -6457,15 +4392,15 @@ This search looks for arguments to certutil.exe indicating the manipulation or e #### How To Implement -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase * Installation @@ -6473,7 +4408,7 @@ This search looks for arguments to certutil.exe indicating the manipulation or e #### Known False Positives Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services. -#### References +#### Reference #### Test Dataset @@ -6486,135 +4421,16 @@ _version_: 1 --- -### Child Processes of Spoolsv exe -This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) -- **Last Updated**: 2020-03-16 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `child_processes_of_spoolsv_exe_filter` -``` -#### Associated Analytic Story - -* Windows Privilege Escalation - - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | - -#### Kill Chain Phases - -* Exploitation - - -#### Known False Positives -Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - -### Clients Connecting to Multiple DNS Servers -This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution -- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src -| `drop_dm_object_name("Network_Resolution")` -|where dest_count > 5 -| `clients_connecting_to_multiple_dns_servers_filter` -``` -#### Associated Analytic Story - -* DNS Hijacking - -* Command and Control - -* Suspicious DNS Traffic - -* Host Redirection - - -#### How To Implement -This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ -This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048.003 | x | x | - -#### Kill Chain Phases - -* Command and Control - - -#### Known False Positives -It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - ### Cloud API Calls From Previously Unseen User Roles This search looks for new commands from each user role. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-09-04
- View + details #### Search ``` @@ -6639,22 +4455,22 @@ This search looks for new commands from each user role. #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter` -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives . -#### References +#### Reference #### Test Dataset @@ -6671,12 +4487,12 @@ _version_: 1 This search looks for cloud compute instances created by users who have not created them before. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-08-21
- View + details #### Search ``` @@ -6700,22 +4516,22 @@ This search looks for cloud compute instances created by users who have not crea #### How To Implement You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. -#### References +#### Reference #### Test Dataset @@ -6732,12 +4548,12 @@ _version_: 1 This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2020-09-02
- View + details #### Search ``` @@ -6761,16 +4577,16 @@ This search looks at cloud-infrastructure events where an instance is created in #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -6778,7 +4594,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y #### Known False Positives It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. -#### References +#### Reference #### Test Dataset @@ -6795,12 +4611,12 @@ _version_: 1 This search looks for cloud compute instances being created with previously unseen image IDs. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: - **Last Updated**: 2018-10-12
- View + details #### Search ``` @@ -6826,21 +4642,21 @@ This search looks for cloud compute instances being created with previously unse #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. -#### References +#### Reference #### Test Dataset @@ -6857,12 +4673,12 @@ _version_: 1 Find EC2 instances being created with previously unseen instance types. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: - **Last Updated**: 2020-09-12
- View + details #### Search ``` @@ -6888,21 +4704,21 @@ Find EC2 instances being created with previously unseen instance types. #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. -#### References +#### Reference #### Test Dataset @@ -6919,12 +4735,12 @@ _version_: 1 This search looks for cloud instances being modified by users who have not previously modified them. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-07-29
- View + details #### Search ``` @@ -6948,22 +4764,22 @@ This search looks for cloud instances being modified by users who have not previ #### How To Implement This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. -#### References +#### Reference #### Test Dataset @@ -6971,61 +4787,6 @@ It's possible that a new user will start to modify EC2 instances when they haven * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json -_version_: 1 -
- ---- - -### Cloud Network Access Control List Deleted -Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-09-08 - -
- View - -#### Search -``` -`cloudtrail` eventName=DeleteNetworkAcl -|rename userIdentity.arn as arn -| stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `cloud_network_access_control_list_deleted_filter` -``` -#### Associated Analytic Story - -* Cloud Network ACL Activity - - -#### How To Implement -You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the `cloud_network_access_control_list_deleted_filter` macro. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -It's possible that a user has legitimately deleted a network ACL. - -#### References - - -#### Test Dataset - - _version_: 1
@@ -7035,12 +4796,12 @@ _version_: 1 This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-10-09
- View + details #### Search ``` @@ -7066,23 +4827,23 @@ This search looks for cloud provisioning activities from previously unseen citie #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -#### References +#### Reference #### Test Dataset @@ -7099,12 +4860,12 @@ _version_: 1 This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-10-09
- View + details #### Search ``` @@ -7130,23 +4891,23 @@ This search looks for cloud provisioning activities from previously unseen count #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -#### References +#### Reference #### Test Dataset @@ -7163,12 +4924,12 @@ _version_: 1 This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-08-16
- View + details #### Search ``` @@ -7192,23 +4953,23 @@ This search looks for cloud provisioning activities from previously unseen IP ad #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -#### References +#### Reference #### Test Dataset @@ -7225,12 +4986,12 @@ _version_: 1 This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-08-16
- View + details #### Search ``` @@ -7256,23 +5017,23 @@ This search looks for cloud provisioning activities from previously unseen regio #### How To Implement You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -#### References +#### Reference #### Test Dataset @@ -7289,12 +5050,12 @@ _version_: 1 The search looks for file modifications with extensions commonly used by Ransomware - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1485](https://attack.mitre.org/techniques/T1485/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -7323,16 +5084,16 @@ This search produces fields (`query`,`query_length`,`count`) that are not yet su 1. **Label:** File Extension, **Field:** file_extension\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1485 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1485 | Data Destruction | Impact | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -7340,7 +5101,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Known False Positives It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. -#### References +#### Reference #### Test Dataset @@ -7357,12 +5118,12 @@ _version_: 4 The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1485](https://attack.mitre.org/techniques/T1485/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -7386,16 +5147,16 @@ The search looks for files created with names matching those typically used in r #### How To Implement You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1485 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1485 | Data Destruction | Impact | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -7403,7 +5164,7 @@ You must be ingesting data that records file-system activity from your hosts to #### Known False Positives It's possible that a legitimate file could be created with the same name used by ransomware note files. -#### References +#### Reference #### Test Dataset @@ -7420,12 +5181,12 @@ _version_: 4 Detect remote thread creation into LSASS consistent with credential dumping. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2019-12-06
- View + details #### Search ``` @@ -7444,16 +5205,16 @@ Detect remote thread creation into LSASS consistent with credential dumping. #### How To Implement This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.001 | LSASS Memory | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -7461,7 +5222,7 @@ This search needs Sysmon Logs with a Sysmon configuration, which includes EventC #### Known False Positives Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. -#### References +#### Reference * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf @@ -7480,12 +5241,12 @@ _version_: 1 This search looks for the creation of local administrator accounts using net.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1136.001](https://attack.mitre.org/techniques/T1136.001/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -7504,16 +5265,16 @@ This search looks for the creation of local administrator accounts using net.exe #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1136.001 | Local Account | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -7521,7 +5282,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives Administrators often leverage net.exe to create admin accounts. -#### References +#### Reference #### Test Dataset @@ -7542,12 +5303,12 @@ _version_: 4 This search looks for the creation or deletion of hidden shares using net.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1070.005](https://attack.mitre.org/techniques/T1070.005/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -7567,16 +5328,16 @@ This search looks for the creation or deletion of hidden shares using net.exe. #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1070.005 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1070.005 | Network Share Connection Removal | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -7584,7 +5345,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. -#### References +#### Reference * https://attack.mitre.org/techniques/T1070/005 @@ -7603,12 +5364,12 @@ _version_: 5 Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2019-12-10
- View + details #### Search ``` @@ -7627,16 +5388,16 @@ Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.003 | NTDS | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -7644,7 +5405,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives Legitimate administrator usage of Vssadmin or Wmic will create false positives. -#### References +#### Reference * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf @@ -7663,12 +5424,12 @@ _version_: 1 This search detects the use of wmic and Powershell to create a shadow copy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2019-12-10
- View + details #### Search ``` @@ -7687,16 +5448,16 @@ This search detects the use of wmic and Powershell to create a shadow copy. #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.003 | NTDS | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -7704,7 +5465,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Legtimate administrator usage of wmic to create a shadow copy. -#### References +#### Reference * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf @@ -7723,12 +5484,12 @@ _version_: 1 Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2020-02-03
- View + details #### Search ``` @@ -7747,16 +5508,16 @@ Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess #### How To Implement This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.001 | LSASS Memory | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -7764,7 +5525,7 @@ This search requires Sysmon Logs and a Sysmon configuration, which includes Even #### Known False Positives Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. -#### References +#### Reference * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager @@ -7787,12 +5548,12 @@ _version_: 1 This search detects credential dumping using copy command from a shadow copy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2019-12-10
- View + details #### Search ``` @@ -7811,16 +5572,16 @@ This search detects credential dumping using copy command from a shadow copy. #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.003 | NTDS | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -7828,7 +5589,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives unknown -#### References +#### Reference * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf @@ -7847,12 +5608,12 @@ _version_: 1 This search detects the creation of a symlink to a shadow copy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2019-12-10
- View + details #### Search ``` @@ -7871,16 +5632,16 @@ This search detects the creation of a symlink to a shadow copy. #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.003 | NTDS | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -7888,7 +5649,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives unknown -#### References +#### Reference * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf @@ -7907,12 +5668,12 @@ _version_: 1 Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-18
- View + details #### Search ``` @@ -7931,7 +5692,7 @@ Credential extraction is often an illegal recovery of credential material from s #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -7950,11 +5711,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003 | OS Credential Dumping | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -7962,7 +5723,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference #### Test Dataset @@ -7977,12 +5738,12 @@ _version_: 1 Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-18
- View + details #### Search ``` @@ -8001,7 +5762,7 @@ Credential extraction is often an illegal recovery of credential material from s #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -8018,11 +5779,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003 | OS Credential Dumping | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -8030,7 +5791,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference #### Test Dataset @@ -8045,12 +5806,12 @@ _version_: 1 Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/), [T1555](https://attack.mitre.org/techniques/T1555/) - **Last Updated**: 2020-10-18
- View + details #### Search ``` @@ -8069,7 +5830,7 @@ Credential extraction is often an illegal recovery of credential material from s #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -8082,12 +5843,12 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | -| T1555 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003 | OS Credential Dumping | Credential Access | +| T1555 | Credentials from Password Stores | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -8095,7 +5856,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference #### Test Dataset @@ -8110,12 +5871,12 @@ _version_: 1 Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-21
- View + details #### Search ``` @@ -8134,7 +5895,7 @@ Credential extraction is often an illegal recovery of credential material from s #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -8153,11 +5914,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003 | OS Credential Dumping | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -8165,7 +5926,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/MichaelGrafnetter/DSInternals @@ -8182,12 +5943,12 @@ _version_: 1 Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-21
- View + details #### Search ``` @@ -8206,7 +5967,7 @@ Credential extraction is often an illegal recovery of credential material from s #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -8225,11 +5986,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003 | OS Credential Dumping | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -8237,7 +5998,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/MichaelGrafnetter/DSInternals @@ -8254,12 +6015,12 @@ _version_: 1 Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-21
- View + details #### Search ``` @@ -8278,7 +6039,7 @@ Credential extraction is often an illegal recovery of credential material from s #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -8291,11 +6052,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003 | OS Credential Dumping | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -8303,7 +6064,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -8320,12 +6081,12 @@ _version_: 1 Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-21
- View + details #### Search ``` @@ -8344,7 +6105,7 @@ Credential extraction is often an illegal recovery of credential material from s #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -8357,11 +6118,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003 | OS Credential Dumping | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -8369,7 +6130,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -8386,12 +6147,12 @@ _version_: 1 Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-18
- View + details #### Search ``` @@ -8410,7 +6171,7 @@ Credential extraction is often an illegal recovery of credential material from s #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * process_name @@ -8427,11 +6188,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003 | OS Credential Dumping | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -8439,7 +6200,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. -#### References +#### Reference * https://medium.com/@clermont1050/covid-19-cyber-infection-c615ead7c29 @@ -8456,12 +6217,12 @@ _version_: 1 Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-18
- View + details #### Search ``` @@ -8480,7 +6241,7 @@ Credential extraction is often an illegal recovery of credential material from s #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * process_name @@ -8495,11 +6256,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003 | OS Credential Dumping | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -8507,7 +6268,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. -#### References +#### Reference #### Test Dataset @@ -8522,12 +6283,12 @@ _version_: 1 Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) - **Last Updated**: 2020-10-18
- View + details #### Search ``` @@ -8547,7 +6308,7 @@ Credential extraction is often an illegal recovery of credential material from s #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -8560,11 +6321,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003 | OS Credential Dumping | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -8572,7 +6333,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference #### Test Dataset @@ -8587,12 +6348,12 @@ _version_: 1 This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution +- **Datamodel**: Network_Resolution - **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071.004/) - **Last Updated**: 2020-01-22
- View + details #### Search ``` @@ -8628,16 +6389,16 @@ This search produces fields (`query`,`query_length`,`count`) that are not yet su 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1071.004 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1071.004 | DNS | Command and Control | -#### Kill Chain Phases +#### Kill Chain Phase * Command and Control @@ -8645,7 +6406,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Known False Positives If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. -#### References +#### Reference #### Test Dataset @@ -8660,12 +6421,12 @@ _version_: 2 This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution +- **Datamodel**: Network_Resolution - **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) - **Last Updated**: 2021-01-18
- View + details #### Search ``` @@ -8691,16 +6452,16 @@ This search allows you to identify DNS requests and compute the standard deviati #### How To Implement To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | -#### Kill Chain Phases +#### Kill Chain Phase * Command and Control @@ -8708,7 +6469,7 @@ To successfully implement this search, you will need to ensure that DNS data is #### Known False Positives It's possible there can be long domain names that are legitimate. -#### References +#### Reference #### Test Dataset @@ -8716,139 +6477,6 @@ It's possible there can be long domain names that are legitimate. * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log -_version_: 3 -
- ---- - -### DNS Query Requests Resolved by Unauthorized DNS Servers -This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution -- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest -| `drop_dm_object_name("DNS")` -| `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` -``` -#### Associated Analytic Story - -* DNS Hijacking - -* Command and Control - -* Suspicious DNS Traffic - -* Host Redirection - - -#### How To Implement -To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1071.004 | x | x | - -#### Kill Chain Phases - -* Command and Control - - -#### Known False Positives -Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - -### DNS record changed -The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution -- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| inputlookup discovered_dns_records -| rename answer as discovered_answer -| join domain[ -|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query -| rename DNS.query as query -| where query!="unknown" -| rex field=query "(?\w+\.\w+?)(?:$ -|/)"] -| makemv delim=" " answer -| makemv delim=" " type -| sort -count -| table count,src,domain,type,query,current_answer,discovered_answer -| makemv current_answer -| mvexpand current_answer -| makemv discovered_answer -| eval n=mvfind(discovered_answer, current_answer) -| where isnull(n) -| `dns_record_changed_filter` -``` -#### Associated Analytic Story - -* DNS Hijacking - - -#### How To Implement -To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ - **Splunk>Phantom Playbook Integration**\ -If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ -(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ - - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1071.004 | x | x | - -#### Kill Chain Phases - -* Command and Control - - -#### Known False Positives -Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. - -#### References - - -#### Test Dataset - - _version_: 3
@@ -8858,12 +6486,12 @@ _version_: 3 The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -8886,16 +6514,16 @@ The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1490 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1490 | Inhibit System Recovery | Impact | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -8903,7 +6531,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. -#### References +#### Reference #### Test Dataset @@ -8916,209 +6544,16 @@ _version_: 4 --- -### Detect API activity from users without MFA -This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-05-17 - -
- View - -#### Search -``` -`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false -| search NOT [ -| inputlookup aws_service_accounts -| fields identity -| rename identity as user] -| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_api_activity_from_users_without_mfa_filter` -``` -#### Associated Analytic Story - -* AWS User Monitoring - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ -This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ -1. \ -1. **Label:** AWS User ARN, **Field:** userIdentity.arn\ -1. \ -1. **Label:** AWS User Type, **Field:** userIdentity.type\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - - -#### Known False Positives -Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Detect ARP Poisoning -By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557.002](https://attack.mitre.org/techniques/T1557.002/) -- **Last Updated**: 2020-08-11 - -
- View - -#### Search -``` -`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" -| eval src_interface=src_int_prefix_long+src_int_suffix -| stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `detect_arp_poisoning_filter` -``` -#### Associated Analytic Story - -* Router and Infrastructure Security - - -#### How To Implement -This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1200 | x | x | -| T1498 | x | x | -| T1557.002 | x | x | - -#### Kill Chain Phases - -* Reconnaissance - -* Delivery - -* Actions on Objectives - - -#### Known False Positives -This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely). - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Detect AWS API Activities From Unapproved Accounts -This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` errorCode=success -| rename userName as identity -| search NOT [ -| inputlookup identity_lookup_expanded -| fields identity] -| search NOT [ -| inputlookup aws_service_accounts -| fields identity] -| rename identity as user -| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_aws_api_activities_from_unapproved_accounts_filter` -``` -#### Associated Analytic Story - -* AWS User Monitoring - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ -This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ -1. \ -1. **Label:** First Time, **Field:** firstTime\ -1. \ -1. **Label:** Last Time, **Field:** lastTime\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Detect AWS Console Login by New User This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Authentication +- **Datamodel**: Authentication - **ATT&CK**: - **Last Updated**: 2020-05-28
- View + details #### Search ``` @@ -9141,15 +6576,15 @@ This search looks for CloudTrail events wherein a console login event by a user #### How To Implement You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -9157,7 +6592,7 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later #### Known False Positives When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -#### References +#### Reference #### Test Dataset @@ -9174,12 +6609,12 @@ _version_: 1 This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Authentication +- **Datamodel**: Authentication - **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2020-10-07
- View + details #### Search ``` @@ -9210,16 +6645,16 @@ This search looks for CloudTrail events wherein a console login event by a user #### How To Implement You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -9227,7 +6662,7 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later #### Known False Positives When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -#### References +#### Reference #### Test Dataset @@ -9244,12 +6679,12 @@ _version_: 1 This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Authentication +- **Datamodel**: Authentication - **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2020-10-07
- View + details #### Search ``` @@ -9280,16 +6715,16 @@ This search looks for CloudTrail events wherein a console login event by a user #### How To Implement You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -9297,7 +6732,7 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later #### Known False Positives When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -#### References +#### Reference #### Test Dataset @@ -9314,12 +6749,12 @@ _version_: 1 This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Authentication +- **Datamodel**: Authentication - **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2020-10-07
- View + details #### Search ``` @@ -9350,16 +6785,16 @@ This search looks for CloudTrail events wherein a console login event by a user #### How To Implement You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -9367,7 +6802,7 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later #### Known False Positives When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -#### References +#### Reference #### Test Dataset @@ -9384,12 +6819,12 @@ _version_: 1 This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1550.002](https://attack.mitre.org/techniques/T1550.002/) - **Last Updated**: 2020-10-15
- View + details #### Search ``` @@ -9408,16 +6843,16 @@ This search looks for specific authentication events from the Windows Security E #### How To Implement To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1550.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1550.002 | Pass the Hash | Defense Evasion, Lateral Movement | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -9425,7 +6860,7 @@ To successfully implement this search, you must ingest your Windows Security Eve #### Known False Positives Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. -#### References +#### Reference #### Test Dataset @@ -9438,183 +6873,16 @@ _version_: 5 --- -### Detect Baron Samedit CVE-2021-3156 -This search detects the heap-based buffer overflow of sudoedit - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) -- **Last Updated**: 2021-01-27 - -
- View - -#### Search -``` -`linux_hosts` -| search "sudoedit -s \\" -| `detect_baron_samedit_cve_2021_3156_filter` -``` -#### Associated Analytic Story - -* Baron Samedit CVE-2021-3156 - - -#### How To Implement -Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | - -#### Kill Chain Phases - -* Exploitation - - -#### Known False Positives -unknown - -#### References - -* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Detect Baron Samedit CVE-2021-3156 Segfault -This search detects the heap-based buffer overflow of sudoedit - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) -- **Last Updated**: 2021-01-29 - -
- View - -#### Search -``` -`linux_hosts` -| search sudoedit segfault -| stats count min(_time) as firstTime max(_time) as lastTime by host -| search count > 5 -| `detect_baron_samedit_cve_2021_3156_segfault_filter` -``` -#### Associated Analytic Story - -* Baron Samedit CVE-2021-3156 - - -#### How To Implement -Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged. This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | - -#### Kill Chain Phases - -* Exploitation - - -#### Known False Positives -If sudoedit is throwing segfaults for other reasons this will pick those up too. - -#### References - -* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Detect Baron Samedit CVE-2021-3156 via OSQuery -This search detects the heap-based buffer overflow of sudoedit - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) -- **Last Updated**: 2021-01-28 - -
- View - -#### Search -``` -`osquery_process` -| search "columns.cmdline"="sudoedit -s \\*" -| `detect_baron_samedit_cve_2021_3156_via_osquery_filter` -``` -#### Associated Analytic Story - -* Baron Samedit CVE-2021-3156 - - -#### How To Implement -OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | - -#### Kill Chain Phases - -* Exploitation - - -#### Known False Positives -unknown - -#### References - -* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Detect Computer Changed with Anonymous Account This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1210](https://attack.mitre.org/techniques/T1210/) - **Last Updated**: 2020-09-18
- View + details #### Search ``` @@ -9630,16 +6898,16 @@ This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An ac #### How To Implement This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event Logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1210 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1210 | Exploitation of Remote Services | Lateral Movement | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -9647,7 +6915,7 @@ This search requires audit computer account management to be enabled on the syst #### Known False Positives None thus far found -#### References +#### Reference * https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/ @@ -9664,12 +6932,12 @@ _version_: 1 This search looks for reading lsass memory consistent with credential dumping. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2019-12-03
- View + details #### Search ``` @@ -9690,16 +6958,16 @@ This search looks for reading lsass memory consistent with credential dumping. #### How To Implement This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.001 | LSASS Memory | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -9707,7 +6975,7 @@ This search needs Sysmon Logs and a sysmon configuration, which includes EventCo #### Known False Positives The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. -#### References +#### Reference #### Test Dataset @@ -9720,89 +6988,16 @@ _version_: 3 --- -### Detect DNS requests to Phishing Sites leveraging EvilGinx2 -This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution -- **ATT&CK**: [T1566.003](https://attack.mitre.org/techniques/T1566.003/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host -| `drop_dm_object_name(DNS)` -| rex field=query ".*?(?[^./:]+\.(\S{2,3} -|\S{2,3}.\S{2,3}))$" -| stats count values(query) as query by domain dest src answer -| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` -| search NOT [ inputlookup legit_domains.csv -| fields domain] -| join domain type=outer [ -| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site -| rename "Web.*" as * -| rex field=site ".*?(?[^./:]+\.(\S{2,3} -|\S{2,3}.\S{2,3}))$" -| table dest domain url] -| table count src dest query answer domain url -| `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter` -``` -#### Associated Analytic Story - -* Common Phishing Frameworks - - -#### How To Implement -You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ - **Splunk>Phantom Playbook Integration**\ -If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ -(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\ - - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1566.003 | x | x | - -#### Kill Chain Phases - -* Delivery - -* Command and Control - - -#### Known False Positives -If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Detect Dump LSASS Memory using comsvcs This search detects the memory of lsass.exe being dumped for offline credential theft attack. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2020-09-15
- View + details #### Search ``` @@ -9821,7 +7016,7 @@ This search detects the memory of lsass.exe being dumped for offline credential #### How To Implement You must be ingesting endpoint data that tracks process activity, including Windows command line logging. You can see how we test this with [Event Code 4688](https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4688a) on the [attack_range](https://github.com/splunk/attack_range/blob/develop/ansible/roles/windows_common/tasks/windows-enable-4688-cmd-line-audit.yml). -#### Required fields +#### Required field * process_name @@ -9836,11 +7031,11 @@ You must be ingesting endpoint data that tracks process activity, including Wind #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.003 | NTDS | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -9848,7 +7043,7 @@ You must be ingesting endpoint data that tracks process activity, including Wind #### Known False Positives None identified. -#### References +#### Reference * https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf @@ -9865,12 +7060,12 @@ _version_: 1 This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078.002](https://attack.mitre.org/techniques/T1078.002/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -9895,22 +7090,22 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "Exc (Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\ -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.002 | Domain Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. -#### References +#### Reference #### Test Dataset @@ -9929,12 +7124,12 @@ _version_: 5 This search detects user accounts that have been locked out a relatively high number of times in a short period. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1078.003](https://attack.mitre.org/techniques/T1078.003/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -9955,22 +7150,22 @@ This search detects user accounts that have been locked out a relatively high nu #### How To Implement ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.003 | Local Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. -#### References +#### Reference #### Test Dataset @@ -9985,76 +7180,16 @@ _version_: 3 --- -### Detect F5 TMUI RCE CVE-2020-5902 -This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/) -- **Last Updated**: 2020-08-02 - -
- View - -#### Search -``` -`f5_bigip_rogue` -| regex _raw="(hsqldb; -|.*\\.\\.;.*)" -| search `detect_f5_tmui_rce_cve_2020_5902_filter` -``` -#### Associated Analytic Story - -* F5 TMUI RCE CVE-2020-5902 - - -#### How To Implement -To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog. As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it. I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;). - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1190 | x | x | - -#### Kill Chain Phases - -* Exploitation - - -#### Known False Positives -unknown - -#### References - -* https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/ - -* https://support.f5.com/csp/article/K52145254 - -* https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/ - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Detect GCP Storage access from a new IP This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2020-08-10
- View + details #### Search ``` @@ -10086,16 +7221,16 @@ This search looks at GCP Storage bucket-access logs and detects new or previousl #### How To Implement This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour. It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1530 | Data from Cloud Storage Object | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -10103,7 +7238,7 @@ This search relies on the Splunk Add-on for Google Cloud Platform, setting up a #### Known False Positives GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours. -#### References +#### Reference #### Test Dataset @@ -10118,12 +7253,12 @@ _version_: 1 The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218.001/) - **Last Updated**: 2021-02-11
- View + details #### Search ``` @@ -10142,16 +7277,16 @@ The following analytic identifies a renamed instance of hh.exe (HTML Help) execu #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed hh.exe may be used. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.001 | Compiled HTML File | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -10159,7 +7294,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/001/ @@ -10182,12 +7317,12 @@ _version_: 1 The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218.001/) - **Last Updated**: 2021-02-11
- View + details #### Search ``` @@ -10206,16 +7341,16 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.001 | Compiled HTML File | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -10223,7 +7358,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/001/ @@ -10250,12 +7385,12 @@ _version_: 1 The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218.001/) - **Last Updated**: 2021-02-11
- View + details #### Search ``` @@ -10274,16 +7409,16 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.001 | Compiled HTML File | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -10291,7 +7426,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/001/ @@ -10320,12 +7455,12 @@ _version_: 1 The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218.001/) - **Last Updated**: 2021-02-11
- View + details #### Search ``` @@ -10344,16 +7479,16 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.001 | Compiled HTML File | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -10361,7 +7496,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/001/ @@ -10390,12 +7525,12 @@ _version_: 1 By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557.002](https://attack.mitre.org/techniques/T1557.002/) - **Last Updated**: 2020-10-28
- View + details #### Search ``` @@ -10416,18 +7551,18 @@ By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organiz #### How To Implement This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1200 | x | x | -| T1498 | x | x | -| T1557.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1200 | Hardware Additions | Initial Access | +| T1498 | Network Denial of Service | Impact | +| T1557.002 | ARP Cache Poisoning | Collection, Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Reconnaissance @@ -10439,7 +7574,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne #### Known False Positives None currently known -#### References +#### Reference * https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf @@ -10470,12 +7605,12 @@ _version_: 1 This search detects a potential kerberoasting attack via service principal name requests - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1558.003](https://attack.mitre.org/techniques/T1558.003/) - **Last Updated**: 2020-10-21
- View + details #### Search ``` @@ -10495,7 +7630,7 @@ This search detects a potential kerberoasting attack via service principal name #### How To Implement The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2 -#### Required fields +#### Required field * service_name @@ -10512,11 +7647,11 @@ The test data is converted from Windows Security Event logs generated from Attac #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1558.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1558.003 | Kerberoasting | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -10524,7 +7659,7 @@ The test data is converted from Windows Security Event logs generated from Attac #### Known False Positives Older systems that support kerberos RC4 by default NetApp may generate false positives -#### References +#### Reference * Initial ESCU implementation by Jose Hernandez and Patrick Bareiss @@ -10541,12 +7676,12 @@ _version_: 1 This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic +- **Datamodel**: Network_Traffic - **ATT&CK**: [T1095](https://attack.mitre.org/techniques/T1095/) - **Last Updated**: 2018-06-01
- View + details #### Search ``` @@ -10566,16 +7701,16 @@ This search looks for outbound ICMP packets with a packet size larger than 1,000 #### How To Implement In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1095 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1095 | Non-Application Layer Protocol | Command and Control | -#### Kill Chain Phases +#### Kill Chain Phase * Command and Control @@ -10583,69 +7718,7 @@ In order to run this search effectively, we highly recommend that you leverage t #### Known False Positives ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list. -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Detect Long DNS TXT Record Response -This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution -- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type -| `drop_dm_object_name("DNS")` -| eval anslen=len(answer) -| search anslen>100 -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count -| table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" -| `detect_long_dns_txt_record_response_filter` -``` -#### Associated Analytic Story - -* Suspicious DNS Traffic - -* Command and Control - - -#### How To Implement -To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048.003 | x | x | - -#### Kill Chain Phases - -* Command and Control - - -#### Known False Positives -It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. - -#### References +#### Reference #### Test Dataset @@ -10660,12 +7733,12 @@ _version_: 2 This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-20
- View + details #### Search ``` @@ -10684,16 +7757,16 @@ This analytic identifies when Microsoft HTML Application Host (mshta.exe) utilit #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.005 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.005 | Mshta | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -10701,7 +7774,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives It is possible legitimate applications may perform this behavior and will need to be filtered. -#### References +#### Reference * https://github.com/redcanaryco/AtomicTestHarnesses @@ -10720,138 +7793,16 @@ _version_: 1 --- -### Detect Mimikatz Using Loaded Images -This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) -- **Last Updated**: 2019-12-03 - -
- View - -#### Search -``` -`sysmon` EventCode=7 -| stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image -| search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_mimikatz_using_loaded_images_filter` -``` -#### Associated Analytic Story - -* Credential Dumping - -* Detect Zerologon Attack - -* Cloud Federated Credential Abuse - - -#### How To Implement -This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Other tools can import the same DLLs. These tools should be part of a whitelist. - -#### References - -* https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Detect Mimikatz Via PowerShell And EventCode 4703 -This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) -- **Last Updated**: 2019-02-27 - -
- View - -#### Search -``` -`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe -| rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" -| where privs="SeDebugPrivilege" -| stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message -| rename privs as "Enabled Privilege" -| rename Process_Name as process -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_mimikatz_via_powershell_and_eventcode_4703_filter` -``` -#### Associated Analytic Story - -* Cloud Federated Credential Abuse - - -#### How To Implement -You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Detect New Local Admin account This search looks for newly created accounts that have been elevated to local administrators. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1136.001](https://attack.mitre.org/techniques/T1136.001/) - **Last Updated**: 2020-07-08
- View + details #### Search ``` @@ -10871,16 +7822,16 @@ This search looks for newly created accounts that have been elevated to local ad #### How To Implement You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732 -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1136.001 | Local Account | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -10890,7 +7841,7 @@ You must be ingesting Windows event logs using the Splunk Windows TA and collect #### Known False Positives The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives -#### References +#### Reference #### Test Dataset @@ -10907,73 +7858,16 @@ _version_: 2 --- -### Detect New Login Attempts to Routers -The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Authentication -- **ATT&CK**: -- **Last Updated**: 2017-09-12 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user -| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) -| where isOutlier=1 -| `security_content_ctime(earliest)` -| `security_content_ctime(latest)` -| `drop_dm_object_name("Authentication")` -| `detect_new_login_attempts_to_routers_filter` -``` -#### Associated Analytic Story - -* Router and Infrastructure Security - - -#### How To Implement -To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Legitimate router connections may appear as new connections - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Detect New Open GCP Storage Buckets This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2020-08-05
- View + details #### Search ``` @@ -10997,16 +7891,16 @@ This search looks for GCP PubSub events where a user has created an open/public #### How To Implement This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1530 | Data from Cloud Storage Object | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -11014,7 +7908,7 @@ This search relies on the Splunk Add-on for Google Cloud Platform, setting up a #### Known False Positives While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. -#### References +#### Reference #### Test Dataset @@ -11029,12 +7923,12 @@ _version_: 1 This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2021-01-12
- View + details #### Search ``` @@ -11054,16 +7948,16 @@ This search looks for CloudTrail events where a user has created an open/public #### How To Implement -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1530 | Data from Cloud Storage Object | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -11071,7 +7965,7 @@ This search looks for CloudTrail events where a user has created an open/public #### Known False Positives While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. -#### References +#### Reference #### Test Dataset @@ -11088,12 +7982,12 @@ _version_: 1 This search looks for CloudTrail events where a user has created an open/public S3 bucket. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2021-01-12
- View + details #### Search ``` @@ -11120,16 +8014,16 @@ This search looks for CloudTrail events where a user has created an open/public #### How To Implement You must install the AWS App for Splunk. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1530 | Data from Cloud Storage Object | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -11137,7 +8031,7 @@ You must install the AWS App for Splunk. #### Known False Positives While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. -#### References +#### Reference #### Test Dataset @@ -11150,85 +8044,16 @@ _version_: 2 --- -### Detect Oulook exe writing a zip file -This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| rename process_id as malicious_id -| rename parent_process_id as outlook_id -| join malicious_id type=inner[ -| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| rename process_id as malicious_id -| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] -| table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path -| where file_name != "" -| `detect_oulook_exe_writing_a__zip_file_filter` -``` -#### Associated Analytic Story - -* Phishing Payloads - - -#### How To Implement -You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1566.001 | x | x | - -#### Kill Chain Phases - -* Installation - -* Actions on Objectives - - -#### Known False Positives -It is not uncommon for outlook to write legitimate zip files to the disk. - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - ### Detect Outbound SMB Traffic This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic +- **Datamodel**: Network_Traffic - **ATT&CK**: [T1071.002](https://attack.mitre.org/techniques/T1071.002/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -11251,16 +8076,16 @@ This search looks for outbound SMB connections made by hosts within your network #### How To Implement In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1071.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1071.002 | File Transfer Protocols | Command and Control | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -11270,7 +8095,7 @@ In order to run this search effectively, we highly recommend that you leverage t #### Known False Positives It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. -#### References +#### Reference #### Test Dataset @@ -11285,12 +8110,12 @@ _version_: 3 This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1550.002](https://attack.mitre.org/techniques/T1550.002/) - **Last Updated**: 2020-10-21
- View + details #### Search ``` @@ -11310,7 +8135,7 @@ This search looks for specific authentication events from the Windows Security E #### How To Implement The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2 -#### Required fields +#### Required field * logon_process @@ -11327,11 +8152,11 @@ The test data is converted from Windows Security Event logs generated from Attac #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1550.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1550.002 | Pass the Hash | Defense Evasion, Lateral Movement | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -11339,7 +8164,7 @@ The test data is converted from Windows Security Event logs generated from Attac #### Known False Positives Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. -#### References +#### Reference * Initial ESCU implementation by Bhavin Patel and Patrick Bareiss @@ -11356,12 +8181,12 @@ _version_: 1 The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1574.009](https://attack.mitre.org/techniques/T1574.009/) - **Last Updated**: 2020-07-03
- View + details #### Search ``` @@ -11387,16 +8212,16 @@ The detection Detect Path Interception By Creation Of program exe is detecting t #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1574.009 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1574.009 | Path Interception by Unquoted Path | Defense Evasion, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -11404,7 +8229,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives unknown -#### References +#### Reference * https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae @@ -11423,12 +8248,12 @@ _version_: 3 By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557.002](https://attack.mitre.org/techniques/T1557.002/) - **Last Updated**: 2020-10-28
- View + details #### Search ``` @@ -11447,18 +8272,18 @@ By enabling Port Security on a Cisco switch you can restrict input to an interfa #### How To Implement This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1200 | x | x | -| T1498 | x | x | -| T1557.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1200 | Hardware Additions | Initial Access | +| T1498 | Network Denial of Service | Impact | +| T1557.002 | ARP Cache Poisoning | Collection, Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Reconnaissance @@ -11472,7 +8297,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne #### Known False Positives This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network. -#### References +#### Reference #### Test Dataset @@ -11487,12 +8312,12 @@ _version_: 1 This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059.003/) - **Last Updated**: 2020-11-10
- View + details #### Search ``` @@ -11518,16 +8343,16 @@ This search looks for executions of cmd.exe spawned by a process that is often a #### How To Implement You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059.003 | Windows Command Shell | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -11535,7 +8360,7 @@ You must be ingesting data that records process activity from your hosts and pop #### Known False Positives There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. -#### References +#### Reference #### Test Dataset @@ -11552,12 +8377,12 @@ _version_: 5 This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. This is a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe` by @bpatel. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/) - **Last Updated**: 2020-7-13
- View + details #### Search ``` @@ -11580,7 +8405,7 @@ This search looks for executions of cmd.exe spawned by a process that is often a #### How To Implement You must be ingesting sysmon logs. This search has been modified to process raw sysmon data from attack_range's nxlogs on DSP. -#### Required fields +#### Required field * process_name @@ -11595,11 +8420,11 @@ You must be ingesting sysmon logs. This search has been modified to process raw #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059 | Command and Scripting Interpreter | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -11607,7 +8432,7 @@ You must be ingesting sysmon logs. This search has been modified to process raw #### Known False Positives There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. -#### References +#### Reference #### Test Dataset @@ -11622,12 +8447,12 @@ _version_: 1 This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/) - **Last Updated**: 2020-11-10
- View + details #### Search ``` @@ -11648,16 +8473,16 @@ This search looks for events where `PsExec.exe` is run with the `accepteula` fla #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -11665,7 +8490,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine -#### References +#### Reference #### Test Dataset @@ -11682,12 +8507,12 @@ _version_: 3 This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: - **Last Updated**: 2020-03-16
- View + details #### Search ``` @@ -11717,15 +8542,15 @@ This search will return a table of rare processes, the names of the systems runn #### How To Implement To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_allow_list` searches two lookup files for allowed processes. These consist of `rare_process_allow_list_default.csv` and `rare_process_allow_list_local.csv`. To add your own processes to the allow list, add them to `rare_process_allow_list_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the allow_list value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase * Installation @@ -11737,7 +8562,7 @@ To successfully implement this search, you must be ingesting data that records p #### Known False Positives Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_allow_list_local.csv` to filter them out of your search results. -#### References +#### Reference #### Test Dataset @@ -11752,12 +8577,12 @@ _version_: 5 The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-12
- View + details #### Search ``` @@ -11776,16 +8601,16 @@ The following analytic identifies regasm.exe spawning a process. This particular #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.009 | Regsvcs/Regasm | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -11793,7 +8618,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/009/ @@ -11818,12 +8643,12 @@ _version_: 1 The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-16
- View + details #### Search ``` @@ -11842,16 +8667,16 @@ The following analytic identifies regasm.exe with a network connection to a publ #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.009 | Regsvcs/Regasm | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -11859,7 +8684,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/009/ @@ -11882,12 +8707,12 @@ _version_: 1 The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-12
- View + details #### Search ``` @@ -11907,16 +8732,16 @@ The following analytic identifies regasm.exe with no command line arguments. Thi #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.009 | Regsvcs/Regasm | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -11924,7 +8749,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/009/ @@ -11947,12 +8772,12 @@ _version_: 1 The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-12
- View + details #### Search ``` @@ -11971,16 +8796,16 @@ The following analytic identifies regsvcs.exe spawning a process. This particula #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.009 | Regsvcs/Regasm | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -11988,7 +8813,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/009/ @@ -12011,12 +8836,12 @@ _version_: 1 The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-16
- View + details #### Search ``` @@ -12035,16 +8860,16 @@ The following analytic identifies Regsvcs.exe with a network connection to a pub #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.009 | Regsvcs/Regasm | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -12052,7 +8877,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/009/ @@ -12075,12 +8900,12 @@ _version_: 1 The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-12
- View + details #### Search ``` @@ -12100,16 +8925,16 @@ The following analytic identifies regsvcs.exe with no command line arguments. Th #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.009 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.009 | Regsvcs/Regasm | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -12117,7 +8942,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/009/ @@ -12141,12 +8966,12 @@ Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr3 Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.010](https://attack.mitre.org/techniques/T1218.010/) - **Last Updated**: 2021-01-28
- View + details #### Search ``` @@ -12165,16 +8990,16 @@ Upon investigating, look for network connections to remote destinations (interna #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.010 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.010 | Regsvr32 | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -12182,7 +9007,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives Limited false positives related to third party software registering .DLL's. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/010/ @@ -12207,12 +9032,12 @@ _version_: 1 By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557](https://attack.mitre.org/techniques/T1557/) - **Last Updated**: 2020-08-11
- View + details #### Search ``` @@ -12230,18 +9055,18 @@ By enabling DHCP Snooping as a Layer 2 Security measure on the organization's ne #### How To Implement This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1200 | x | x | -| T1498 | x | x | -| T1557 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1200 | Hardware Additions | Initial Access | +| T1498 | Network Denial of Service | Impact | +| T1557 | Man-in-the-Middle | Collection, Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Reconnaissance @@ -12253,7 +9078,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne #### Known False Positives This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface. -#### References +#### Reference #### Test Dataset @@ -12268,12 +9093,12 @@ _version_: 1 The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-04
- View + details #### Search ``` @@ -12292,16 +9117,16 @@ The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.011 | Rundll32 | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -12309,7 +9134,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/011/ @@ -12336,12 +9161,12 @@ _version_: 1 The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-04
- View + details #### Search ``` @@ -12360,16 +9185,16 @@ The following analytic identifies rundll32.exe loading setupapi.dll and iesetupa #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.011 | Rundll32 | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -12377,7 +9202,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, some legitimate applications may use setupapi triggering a false positive. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/011/ @@ -12404,12 +9229,12 @@ _version_: 1 The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-04
- View + details #### Search ``` @@ -12428,16 +9253,16 @@ The following analytic identifies rundll32.exe loading syssetup.dll by calling t #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.011 | Rundll32 | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -12445,7 +9270,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/011/ @@ -12472,12 +9297,12 @@ _version_: 1 The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-20
- View + details #### Search ``` @@ -12496,16 +9321,16 @@ The following analytic identifies "rundll32.exe" execution with inline protocol #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.005 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.005 | Mshta | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -12513,7 +9338,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. -#### References +#### Reference * https://github.com/redcanaryco/AtomicTestHarnesses @@ -12536,12 +9361,12 @@ _version_: 1 This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2018-06-28
- View + details #### Search ``` @@ -12568,16 +9393,16 @@ This search looks at S3 bucket-access logs and detects new or previously unseen #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1530 | Data from Cloud Storage Object | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -12585,7 +9410,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Known False Positives S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour -#### References +#### Reference #### Test Dataset @@ -12600,12 +9425,12 @@ _version_: 1 This search looks for commands that the SNICat tool uses in the TLS SNI field. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1041](https://attack.mitre.org/techniques/T1041/) - **Last Updated**: 2020-10-21
- View + details #### Search ``` @@ -12634,16 +9459,16 @@ This search looks for commands that the SNICat tool uses in the TLS SNI field. #### How To Implement You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1041 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1041 | Exfiltration Over C2 Channel | Exfiltration | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -12651,7 +9476,7 @@ You must be ingesting Zeek SSL data into Splunk. Zeek data should also be gettin #### Known False Positives Unknown -#### References +#### Reference * https://www.mnemonic.no/blog/introducing-snicat/ @@ -12672,12 +9497,12 @@ _version_: 1 Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic +- **Datamodel**: Network_Traffic - **ATT&CK**: [T1542.005](https://attack.mitre.org/techniques/T1542.005/) - **Last Updated**: 2020-10-28
- View + details #### Search ``` @@ -12696,16 +9521,16 @@ Adversaries may abuse netbooting to load an unauthorized network device operatin #### How To Implement This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1542.005 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1542.005 | TFTP Boot | Defense Evasion, Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Delivery @@ -12713,7 +9538,7 @@ This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from #### Known False Positives This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices. -#### References +#### Reference #### Test Dataset @@ -12724,92 +9549,16 @@ _version_: 1 --- -### Detect Spike in AWS API Activity -This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall -| spath output=arn path=userIdentity.arn -| stats count as apiCalls by arn -| inputlookup api_call_by_user_baseline append=t -| fields - latestCount -| stats values(*) as * by arn -| rename apiCalls as latestCount -| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 -| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) -| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) -| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls -| outputlookup api_call_by_user_baseline -| eval dataPointThreshold = 15, deviationThreshold = 3 -| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) -| where isSpike=1 -| rename arn as userIdentity.arn -| table userIdentity.arn] -| spath output=user userIdentity.arn -| stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user -| `detect_spike_in_aws_api_activity_filter` -``` -#### Associated Analytic Story - -* AWS User Monitoring - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ -This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ -1. \ -1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\ -1. \ -1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives - - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Detect Spike in AWS Security Hub Alerts for EC2 Instance This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: - **Last Updated**: 2021-01-26
- View + details #### Search ``` @@ -12831,21 +9580,21 @@ This search looks for a spike in number of of AWS security Hub alerts for an EC2 #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives None -#### References +#### Reference #### Test Dataset @@ -12862,12 +9611,12 @@ _version_: 3 This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: - **Last Updated**: 2021-01-26
- View + details #### Search ``` @@ -12890,21 +9639,21 @@ This search looks for a spike in number of of AWS security Hub alerts for an AWS #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives None -#### References +#### Reference #### Test Dataset @@ -12915,86 +9664,16 @@ _version_: 3 --- -### Detect Spike in Network ACL Activity -This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562.007/) -- **Last Updated**: 2018-05-21 - -
- View - -#### Search -``` -`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` -| spath output=arn path=userIdentity.arn -| stats count as apiCalls by arn -| inputlookup network_acl_activity_baseline append=t -| fields - latestCount -| stats values(*) as * by arn -| rename apiCalls as latestCount -| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 -| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) -| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) -| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls -| outputlookup network_acl_activity_baseline -| eval dataPointThreshold = 15, deviationThreshold = 3 -| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) -| where isSpike=1 -| rename arn as userIdentity.arn -| table userIdentity.arn] -| spath output=user userIdentity.arn -| stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user -| `detect_spike_in_network_acl_activity_filter` -``` -#### Associated Analytic Story - -* AWS Network ACL Activity - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.007 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Detect Spike in S3 Bucket deletion This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2018-11-27
- View + details #### Search ``` @@ -13028,16 +9707,16 @@ This search detects users creating spikes in API activity related to deletion of #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1530 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1530 | Data from Cloud Storage Object | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -13045,77 +9724,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Known False Positives Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Detect Spike in Security Group Activity -This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) -- **Last Updated**: 2018-04-18 - -
- View - -#### Search -``` -`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` -| spath output=arn path=userIdentity.arn -| stats count as apiCalls by arn -| inputlookup security_group_activity_baseline append=t -| fields - latestCount -| stats values(*) as * by arn -| rename apiCalls as latestCount -| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 -| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) -| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) -| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls -| outputlookup security_group_activity_baseline -| eval dataPointThreshold = 15, deviationThreshold = 3 -| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) -| where isSpike=1 -| rename arn as userIdentity.arn -| table userIdentity.arn] -| spath output=user userIdentity.arn -| stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user -| `detect_spike_in_security_group_activity_filter` -``` -#### Associated Analytic Story - -* AWS User Monitoring - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. - -#### References +#### Reference #### Test Dataset @@ -13130,12 +9739,12 @@ _version_: 1 This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: - **Last Updated**: 2018-05-07
- View + details #### Search ``` @@ -13169,15 +9778,15 @@ This search will detect spike in blocked outbound network connections originatin #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -13187,7 +9796,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Known False Positives The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. -#### References +#### Reference #### Test Dataset @@ -13202,12 +9811,12 @@ _version_: 1 Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1020.001](https://attack.mitre.org/techniques/T1020.001/) - **Last Updated**: 2020-10-28
- View + details #### Search ``` @@ -13225,18 +9834,18 @@ Adversaries may leverage traffic mirroring in order to automate data exfiltratio #### How To Implement This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1200 | x | x | -| T1498 | x | x | -| T1020.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1200 | Hardware Additions | Initial Access | +| T1498 | Network Denial of Service | Impact | +| T1020.001 | Traffic Duplication | Exfiltration | -#### Kill Chain Phases +#### Kill Chain Phase * Delivery @@ -13246,64 +9855,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne #### Known False Positives This search will return false positives for any legitimate traffic captures by network administrators. -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Detect USB device insertion -The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change_Analysis -- **ATT&CK**: -- **Last Updated**: 2017-11-27 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest -| `drop_dm_object_name("All_Changes")` -| `security_content_ctime(earliest)` -| `security_content_ctime(latest)` -| `detect_usb_device_insertion_filter` -``` -#### Associated Analytic Story - -* Data Protection - - -#### How To Implement -To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Installation - -* Actions on Objectives - - -#### Known False Positives -Legitimate USB activity will also be detected. Please verify and investigate as appropriate. - -#### References +#### Reference #### Test Dataset @@ -13318,12 +9870,12 @@ _version_: 1 By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Sessions +- **Datamodel**: Network_Sessions - **ATT&CK**: - **Last Updated**: 2017-09-13
- View + details #### Search ``` @@ -13346,15 +9898,15 @@ By populating the organization's assets within the assets_by_str.csv, we will be #### How To Implement This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase * Reconnaissance @@ -13366,7 +9918,7 @@ This search uses the Network_Sessions data model shipped with Enterprise Securit #### Known False Positives This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. -#### References +#### Reference #### Test Dataset @@ -13381,12 +9933,12 @@ _version_: 1 This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059.003/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -13407,16 +9959,16 @@ This search looks for the execution of the cscript.exe or wscript.exe processes, #### How To Implement To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059.003 | Windows Command Shell | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -13424,7 +9976,7 @@ To successfully implement this search, you must be ingesting data that records p #### Known False Positives Some legitimate applications may exhibit this behavior. -#### References +#### Reference #### Test Dataset @@ -13441,12 +9993,12 @@ _version_: 4 This search detects SIGRed via Splunk Stream. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/) - **Last Updated**: 2020-07-28
- View + details #### Search ``` @@ -13469,16 +10021,16 @@ This search detects SIGRed via Splunk Stream. #### How To Implement You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1203 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1203 | Exploitation for Client Execution | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -13486,7 +10038,7 @@ You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting #### Known False Positives unknown -#### References +#### Reference * https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ @@ -13503,12 +10055,12 @@ _version_: 1 This search detects SIGRed via Zeek DNS and Zeek Conn data. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution +- **Datamodel**: Network_Resolution - **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/) - **Last Updated**: 2020-07-28
- View + details #### Search ``` @@ -13531,16 +10083,16 @@ This search detects SIGRed via Zeek DNS and Zeek Conn data. #### How To Implement You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The Network Resolution and Network Traffic datamodels are in use for this search. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1203 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1203 | Exploitation for Client Execution | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -13548,7 +10100,7 @@ You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should #### Known False Positives unknown -#### References +#### Reference * https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ @@ -13565,12 +10117,12 @@ _version_: 1 This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/) - **Last Updated**: 2020-09-15
- View + details #### Search ``` @@ -13588,16 +10140,16 @@ This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vul #### How To Implement You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json. These three operations are then correlated on the Zeek UID field. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1190 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1190 | Exploit Public-Facing Application | Initial Access | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -13605,7 +10157,7 @@ You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be ge #### Known False Positives unknown -#### References +#### Reference * https://www.secura.com/blog/zero-logon @@ -13617,64 +10169,6 @@ unknown #### Test Dataset -_version_: 1 -
- ---- - -### Detect attackers scanning for vulnerable JBoss servers -This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Web -- **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/) -- **Last Updated**: 2017-09-23 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest -| `drop_dm_object_name("Web")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` -``` -#### Associated Analytic Story - -* JBoss Vulnerability - -* SamSam Ransomware - - -#### How To Implement -You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1082 | x | x | - -#### Kill Chain Phases - -* Reconnaissance - - -#### Known False Positives -It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. - -#### References - - -#### Test Dataset - - _version_: 1
@@ -13684,12 +10178,12 @@ _version_: 1 Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution +- **Datamodel**: Network_Resolution - **ATT&CK**: [T1189](https://attack.mitre.org/techniques/T1189/) - **Last Updated**: 2021-01-14
- View + details #### Search ``` @@ -13724,16 +10218,16 @@ This search produces fields (query, answer, isDynDNS) that are not yet supported 1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1189 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1189 | Drive-by Compromise | Initial Access | -#### Kill Chain Phases +#### Kill Chain Phase * Command and Control @@ -13743,7 +10237,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Known False Positives Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. -#### References +#### Reference #### Test Dataset @@ -13756,75 +10250,16 @@ _version_: 3 --- -### Detect malicious requests to exploit JBoss servers -This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Web -- **ATT&CK**: -- **Last Updated**: 2017-09-23 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest -| search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 -| `drop_dm_object_name("Web")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| table src, dest_ip, http_method, url, firstTime, lastTime -| `detect_malicious_requests_to_exploit_jboss_servers_filter` -``` -#### Associated Analytic Story - -* JBoss Vulnerability - -* SamSam Ransomware - - -#### How To Implement -You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Delivery - - -#### Known False Positives -No known false positives for this detection. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Detect mshta inline hta execution The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-20
- View + details #### Search ``` @@ -13843,16 +10278,16 @@ The following analytic identifies "mshta.exe" execution with inline protocol han #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.005 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.005 | Mshta | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -13860,7 +10295,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. -#### References +#### Reference * https://github.com/redcanaryco/AtomicTestHarnesses @@ -13883,12 +10318,12 @@ _version_: 5 The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-20
- View + details #### Search ``` @@ -13907,16 +10342,16 @@ The following analytic identifies renamed instances of mshta.exe executing. Msht #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.005 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.005 | Mshta | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -13924,7 +10359,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive. -#### References +#### Reference * https://github.com/redcanaryco/AtomicTestHarnesses @@ -13941,139 +10376,16 @@ _version_: 1 --- -### Detect new API calls from user roles -This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) -- **Last Updated**: 2018-04-16 - -
- View - -#### Search -``` -`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole -| stats earliest(_time) as earliest latest(_time) as latest by userName eventName -| inputlookup append=t previously_seen_api_calls_from_user_roles -| stats min(earliest) as earliest, max(latest) as latest by userName eventName -| outputlookup previously_seen_api_calls_from_user_roles -| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) -| where newApiCallfromUserRole=1 -| `security_content_ctime(earliest)` -| `security_content_ctime(latest)` -| table eventName userName] -|rename userName as user -| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user -| `security_content_ctime(earliest)` -| `security_content_ctime(latest)` -| `detect_new_api_calls_from_user_roles_filter` -``` -#### Associated Analytic Story - -* AWS User Monitoring - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - - -#### Known False Positives -It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Detect new user AWS Console Login -This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=ConsoleLogin -| rename userIdentity.arn as user -| stats earliest(_time) as firstTime latest(_time) as lastTime by user -| inputlookup append=t previously_seen_users_console_logins_cloudtrail -| stats min(firstTime) as firstTime max(lastTime) as lastTime by user -| eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| where userStatus ="First Time Logging into AWS Console" -| `detect_new_user_aws_console_login_filter` -``` -#### Associated Analytic Story - -* Suspicious AWS Login Activities - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Detect processes used for System Network Configuration Discovery This search looks for fast execution of processes used for system network configuration discovery on the endpoint. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1016](https://attack.mitre.org/techniques/T1016/) - **Last Updated**: 2020-11-10
- View + details #### Search ``` @@ -14096,16 +10408,16 @@ This search looks for fast execution of processes used for system network config #### How To Implement You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1016 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1016 | System Network Configuration Discovery | Discovery | -#### Kill Chain Phases +#### Kill Chain Phase * Installation @@ -14117,7 +10429,7 @@ You must be ingesting data that records registry activity from your hosts to pop #### Known False Positives It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. -#### References +#### Reference #### Test Dataset @@ -14130,203 +10442,16 @@ _version_: 2 --- -### Detect web traffic to dynamic domain providers -This search looks for web connections to dynamic DNS providers. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Web -- **ATT&CK**: [T1071.001](https://attack.mitre.org/techniques/T1071.001/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status -| `drop_dm_object_name("Web")` -| `security_content_ctime(firstTime)` -| `dynamic_dns_web_traffic` -| `detect_web_traffic_to_dynamic_domain_providers_filter` -``` -#### Associated Analytic Story - -* Dynamic DNS - - -#### How To Implement -This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ -This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` Deprecated because duplicate. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1071.001 | x | x | - -#### Kill Chain Phases - -* Command and Control - -* Actions on Objectives - - -#### Known False Positives -It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Detection of DNS Tunnels -This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution -- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) -- **Last Updated**: 2017-09-18 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" -| rename "DNS.src" as src "DNS.query" as message -| eval length=len(message) -| stats sum(length) as length by src -| append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" -| rename "DNS.src" as src "DNS.answer" as message -| eval message=if(message=="unknown","", message) -| eval length=len(message) -| stats sum(length) as length by src ] -| stats sum(length) as length by src -| where length > 10000 -| `detection_of_dns_tunnels_filter` -``` -#### Associated Analytic Story - -* Data Protection - -* Suspicious DNS Traffic - -* Command and Control - - -#### How To Implement -To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048.003 | x | x | - -#### Kill Chain Phases - -* Command and Control - -* Actions on Objectives - - -#### Known False Positives -It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Detection of tools built by NirSoft -This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1072](https://attack.mitre.org/techniques/T1072/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `detection_of_tools_built_by_nirsoft_filter` -``` -#### Associated Analytic Story - -* Emotet Malware DHS Report TA18-201A - - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1072 | x | x | - -#### Kill Chain Phases - -* Installation - -* Actions on Objectives - - -#### Known False Positives -While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - ### Disabling Remote User Account Control The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1548.002](https://attack.mitre.org/techniques/T1548.002/) - **Last Updated**: 2020-11-18
- View + details #### Search ``` @@ -14345,16 +10470,16 @@ The search looks for modifications to registry keys that control the enforcement #### How To Implement To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1548.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1548.002 | Bypass User Account Control | Defense Evasion, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -14362,7 +10487,7 @@ To successfully implement this search, you must be ingesting data that records r #### Known False Positives This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. -#### References +#### Reference #### Test Dataset @@ -14379,12 +10504,12 @@ _version_: 4 Detect the usage of comsvcs.dll for dumping the lsass process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2020-02-21
- View + details #### Search ``` @@ -14405,16 +10530,16 @@ Detect the usage of comsvcs.dll for dumping the lsass process. #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.001 | LSASS Memory | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -14422,7 +10547,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives None identified. -#### References +#### Reference * https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/ @@ -14444,12 +10569,12 @@ Detect procdump.exe dumping the lsass process. This query looks for both -mm and During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2021-02-01
- View + details #### Search ``` @@ -14468,16 +10593,16 @@ During triage, confirm this is procdump.exe executing. If it is the first time a #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.001 | LSASS Memory | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -14485,7 +10610,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives None identified. -#### References +#### Reference * https://attack.mitre.org/techniques/T1003/001/ @@ -14509,12 +10634,12 @@ Detect a renamed instance of procdump.exe dumping the lsass process. This query During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) - **Last Updated**: 2021-02-01
- View + details #### Search ``` @@ -14533,16 +10658,16 @@ During triage, confirm this is procdump.exe executing. If it is the first time a #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.001 | LSASS Memory | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -14550,7 +10675,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives None identified. -#### References +#### Reference * https://attack.mitre.org/techniques/T1003/001/ @@ -14569,395 +10694,16 @@ _version_: 1 --- -### EC2 Instance Modified With Previously Unseen User -This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success -| stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn -| rename userIdentity.arn as arn -| inputlookup append=t previously_seen_ec2_modifications_by_user -| stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn -| outputlookup previously_seen_ec2_modifications_by_user -| eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) -| where newUser=1 -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| rename arn as userIdentity.arn -| table userIdentity.arn] -| spath output=dest responseElements.instancesSet.items{}.instanceId -| spath output=user userIdentity.arn -| table _time, user, dest -| `ec2_instance_modified_with_previously_unseen_user_filter` -``` -#### Associated Analytic Story - -* Unusual AWS EC2 Modifications - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - - -#### Known False Positives -It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - -### EC2 Instance Started In Previously Unseen Region -This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) -- **Last Updated**: 2018-02-23 - -
- View - -#### Search -``` -`cloudtrail` earliest=-1h StartInstances -| stats earliest(_time) as earliest latest(_time) as latest by awsRegion -| inputlookup append=t previously_seen_aws_regions.csv -| stats min(earliest) as earliest max(latest) as latest by awsRegion -| outputlookup previously_seen_aws_regions.csv -| eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") -| `security_content_ctime(earliest)` -| `security_content_ctime(latest)` -| where regionStatus="Instance Started in a New Region" -| `ec2_instance_started_in_previously_unseen_region_filter` -``` -#### Associated Analytic Story - -* AWS Cryptomining - -* Suspicious AWS EC2 Activities - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1535 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### EC2 Instance Started With Previously Unseen AMI -This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-03-12 - -
- View - -#### Search -``` -`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success -| stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId -| rename requestParameters.instancesSet.items{}.imageId as amiID -| inputlookup append=t previously_seen_ec2_amis.csv -| stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID -| outputlookup previously_seen_ec2_amis.csv -| eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| where newAMI=1 -| rename amiID as requestParameters.instancesSet.items{}.imageId -| table requestParameters.instancesSet.items{}.imageId] -| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID -| table firstTime, lastTime, arn, amiID, dest, instanceType -| `ec2_instance_started_with_previously_unseen_ami_filter` -``` -#### Associated Analytic Story - -* AWS Cryptomining - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - - -#### Known False Positives -After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### EC2 Instance Started With Previously Unseen Instance Type -This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-02-07 - -
- View - -#### Search -``` -`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success -| fillnull value="m1.small" requestParameters.instanceType -| stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType -| rename requestParameters.instanceType as instanceType -| inputlookup append=t previously_seen_ec2_instance_types.csv -| stats min(earliest) as earliest max(latest) as latest by instanceType -| outputlookup previously_seen_ec2_instance_types.csv -| eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) -| `security_content_ctime(earliest)` -| `security_content_ctime(latest)` -| where newType=1 -| rename instanceType as requestParameters.instanceType -| table requestParameters.instanceType] -| spath output=user userIdentity.arn -| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest -| table _time, user, dest, instanceType -| `ec2_instance_started_with_previously_unseen_instance_type_filter` -``` -#### Associated Analytic Story - -* AWS Cryptomining - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - - -#### Known False Positives -It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### EC2 Instance Started With Previously Unseen User -This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success -| stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn -| rename userIdentity.arn as arn -| inputlookup append=t previously_seen_ec2_launches_by_user.csv -| stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn -| outputlookup previously_seen_ec2_launches_by_user.csv -| eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) -| where newUser=1 -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| rename arn as userIdentity.arn -| table userIdentity.arn] -| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user -| table _time, user, dest, instanceType -| `ec2_instance_started_with_previously_unseen_user_filter` -``` -#### Associated Analytic Story - -* AWS Cryptomining - -* Suspicious AWS EC2 Activities - - -#### How To Implement -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.004 | x | x | - -#### Kill Chain Phases - - -#### Known False Positives -It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Email Attachments With Lots Of Spaces -Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Email -- **ATT&CK**: -- **Last Updated**: 2017-09-19 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name("All_Email")` -| eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) -| search space_ratio >= 0.1 -| rex field=recipient_address "(?.*)@" -| `email_attachments_with_lots_of_spaces_filter` -``` -#### Associated Analytic Story - -* Emotet Malware DHS Report TA18-201A - -* Suspicious Emails - - -#### How To Implement -You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ - **Splunk Phantom Playbook Integration**\ -If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Delivery - - -#### Known False Positives -None at this time - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Email files written outside of the Outlook directory The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1114.001](https://attack.mitre.org/techniques/T1114.001/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -14976,16 +10722,16 @@ The search looks at the change-analysis data model and detects email files creat #### How To Implement To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1114.001 | Local Email Collection | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -14993,186 +10739,7 @@ To successfully implement this search, you must be ingesting data that records t #### Known False Positives Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - -### Email servers sending high volume traffic to hosts -This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic -- **ATT&CK**: [T1114.002](https://attack.mitre.org/techniques/T1114.002/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d -| `drop_dm_object_name("All_Traffic")` -| eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out -| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip -| eval minimum_data_samples = 4, deviation_threshold = 3 -| where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") -| eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) -| table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average -| `email_servers_sending_high_volume_traffic_to_hosts_filter` -``` -#### Associated Analytic Story - -* Collection and Staging - - -#### How To Implement -This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.002 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Excessive DNS Failures -This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution -- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071.004/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query" -| `drop_dm_object_name("DNS")` -| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain -| where isnull(domain) -| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank -| where isnull(rank) -| stats sum(count) as count mode(queries) as queries by src -| `get_asset(src)` -| where count>50 -| `excessive_dns_failures_filter` -``` -#### Associated Analytic Story - -* Suspicious DNS Traffic - -* Command and Control - - -#### How To Implement -To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1071.004 | x | x | - -#### Kill Chain Phases - -* Command and Control - - -#### Known False Positives -It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Execution of File With Spaces Before Extension -This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036.003/) -- **Last Updated**: 2020-11-19 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name(Processes)` -| `execution_of_file_with_spaces_before_extension_filter` -``` -#### Associated Analytic Story - -* Windows File Extension and Association Abuse - - -#### How To Implement -To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1036.003 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -None identified. - -#### References +#### Reference #### Test Dataset @@ -15187,12 +10754,12 @@ _version_: 3 This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2020-11-18
- View + details #### Search ``` @@ -15211,16 +10778,16 @@ This search looks for processes launched from files that have double extensions #### How To Implement To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1036.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1036.003 | Rename System Utilities | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -15228,7 +10795,7 @@ To successfully implement this search, you must be ingesting data that records p #### Known False Positives None identified. -#### References +#### Reference #### Test Dataset @@ -15241,71 +10808,16 @@ _version_: 3 --- -### Extended Period Without Successful Netbackup Backups -This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-12 - -
- View - -#### Search -``` -`netbackup` MESSAGE="Disk/Partition backup completed successfully." -| stats latest(_time) as latestTime by COMPUTERNAME -| `security_content_ctime(latestTime)` -| rename COMPUTERNAME as dest -| eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) -| search isOutlier=1 -| table latestTime, dest -| `extended_period_without_successful_netbackup_backups_filter` -``` -#### Associated Analytic Story - -* Monitor Backup Solution - - -#### How To Implement -To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - - -#### Known False Positives -None identified - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### File with Samsam Extension The search looks for file writes with extensions consistent with a SamSam ransomware attack. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: - **Last Updated**: 2018-12-14
- View + details #### Search ``` @@ -15326,15 +10838,15 @@ The search looks for file writes with extensions consistent with a SamSam ransom #### How To Implement You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase * Installation @@ -15342,7 +10854,7 @@ You must be ingesting data that records file-system activity from your hosts to #### Known False Positives Because these extensions are not typically used in normal operations, you should investigate all results. -#### References +#### Reference #### Test Dataset @@ -15359,12 +10871,12 @@ _version_: 1 This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) - **Last Updated**: 2020-05-20
- View + details #### Search ``` @@ -15385,16 +10897,16 @@ This search looks for child processes spawned by zoom.exe or zoom.us that has no #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -15402,7 +10914,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. -#### References +#### Reference #### Test Dataset @@ -15415,79 +10927,16 @@ _version_: 1 --- -### First Time Seen Running Windows Service -This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1569.002](https://attack.mitre.org/techniques/T1569.002/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` -`wineventlog_system` EventCode=7036 -| rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" -| where state="running" -| lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen -| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) -| table _time dest service -| `first_time_seen_running_windows_service_filter` -``` -#### Associated Analytic Story - -* Windows Service Abuse - -* Orangeworm Attack Group - -* Sunburst Malware - - -#### How To Implement -While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1569.002 | x | x | - -#### Kill Chain Phases - -* Installation - -* Actions on Objectives - - -#### Known False Positives -A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. - -#### References - - -#### Test Dataset - - -_version_: 4 -
- ---- - ### First time seen command line argument This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/), [T1117](https://attack.mitre.org/techniques/T1117/), [T1202](https://attack.mitre.org/techniques/T1202/) - **Last Updated**: 2021-2-1
- View + details #### Search ``` @@ -15510,7 +10959,7 @@ This search looks for command-line arguments that use a `/c` parameter to execut #### How To Implement You must be populating the endpoint data model for SSA and specifically the process_name and the process fields -#### Required fields +#### Required field * process_name @@ -15525,13 +10974,13 @@ You must be populating the endpoint data model for SSA and specifically the proc #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059 | x | x | -| T1117 | x | x | -| T1202 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059 | Command and Scripting Interpreter | Execution | +| | | | +| T1202 | Indirect Command Execution | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Command and Control @@ -15541,7 +10990,7 @@ You must be populating the endpoint data model for SSA and specifically the proc #### Known False Positives Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name -#### References +#### Reference #### Test Dataset @@ -15552,433 +11001,16 @@ _version_: 2 --- -### First time seen command line argument -This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/), [T1059.003](https://attack.mitre.org/techniques/T1059.003/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| search [ -| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process -| `drop_dm_object_name(Processes)` -| inputlookup append=t previously_seen_cmd_line_arguments -| stats min(firstTime) as firstTime, max(lastTime) as lastTime by process -| outputlookup previously_seen_cmd_line_arguments -| eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) -| where newCmdLineArgument=1 -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| table process] -| `first_time_seen_command_line_argument_filter` -``` -#### Associated Analytic Story - -* DHS Report TA18-074A - -* Suspicious Command-Line Executions - -* Orangeworm Attack Group - -* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns - -* Hidden Cobra Malware - - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.001 | x | x | -| T1059.003 | x | x | - -#### Kill Chain Phases - -* Command and Control - -* Actions on Objectives - - -#### Known False Positives -Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name - -#### References - - -#### Test Dataset - - -_version_: 5 -
- ---- - -### GCP Detect accounts with high risk roles by project -This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) -- **Last Updated**: 2020-10-09 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}.role=roles/owner OR roles/editor OR roles/iam.serviceAccountUser OR roles/iam.serviceAccountAdmin OR roles/iam.serviceAccountTokenCreator OR roles/dataflow.developer OR roles/dataflow.admin OR roles/composer.admin OR roles/dataproc.admin OR roles/dataproc.editor -| table data.resource.type data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.authorizationInfo{}.resource data.protoPayload.response.bindings{}.role data.protoPayload.response.bindings{}.members{} -| `gcp_detect_accounts_with_high_risk_roles_by_project_filter` -``` -#### Associated Analytic Story - -* GCP Cross Account Activity - - -#### How To Implement -You must install splunk GCP add-on. This search works with gcp:pubsub:message logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Accounts with high risk roles should be reduced to the minimum number needed, however specific tasks and setups may be simply expected behavior within organization - -#### References - -* https://github.com/dxa4481/gcploit - -* https://www.youtube.com/watch?v=Ml09R38jpok - -* https://cloud.google.com/iam/docs/understanding-roles - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### GCP Detect gcploit framework -This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) -- **Last Updated**: 2020-10-08 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s -| table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent -| `gcp_detect_gcploit_framework_filter` -``` -#### Associated Analytic Story - -* GCP Cross Account Activity - - -#### How To Implement -You must install splunk GCP add-on. This search works with gcp:pubsub:message logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects - -#### References - -* https://github.com/dxa4481/gcploit - -* https://www.youtube.com/watch?v=Ml09R38jpok - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### GCP Detect high risk permissions by resource and account -This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) -- **Last Updated**: 2020-10-09 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.permission=iam.serviceAccounts.getaccesstoken OR iam.serviceAccounts.setIamPolicy OR iam.serviceAccounts.actas OR dataflow.jobs.create OR composer.environments.create OR dataproc.clusters.create -|table data.protoPayload.requestMetadata.callerIp data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.response.bindings{}.members{} data.resource.labels.project_id -| `gcp_detect_high_risk_permissions_by_resource_and_account_filter` -``` -#### Associated Analytic Story - -* GCP Cross Account Activity - - -#### How To Implement -You must install splunk GCP add-on. This search works with gcp:pubsub:message logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -High risk permissions are part of any GCP environment, however it is important to track resource and accounts usage, this search may produce false positives. - -#### References - -* https://github.com/dxa4481/gcploit - -* https://www.youtube.com/watch?v=Ml09R38jpok - -* https://cloud.google.com/iam/docs/permissions-reference - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### GCP GCR container uploaded -This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1525](https://attack.mitre.org/techniques/T1525/) -- **Last Updated**: 2020-02-20 - -
- View - -#### Search -``` - -|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path -| `drop_dm_object_name("Storage")` -| `gcp_gcr_container_uploaded_filter` -``` -#### Associated Analytic Story - -* Container Implantation Monitoring and Investigation - - -#### How To Implement -You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1525 | x | x | - -#### Kill Chain Phases - - -#### Known False Positives -Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### GCP Kubernetes cluster pod scan detection -This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) -- **Last Updated**: 2020-07-17 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` category=kube-audit -|spath input=properties.log -|search responseStatus.code=401 -|table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod -| `gcp_kubernetes_cluster_pod_scan_detection_filter` -``` -#### Associated Analytic Story - -* Kubernetes Scanning Activity - - -#### How To Implement -You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1526 | x | x | - -#### Kill Chain Phases - -* Reconnaissance - - -#### Known False Positives -Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### GCP Kubernetes cluster scan detection -This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) -- **Last Updated**: 2020-04-15 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" -| rename data.protoPayload.requestMetadata.callerIp as src_ip -| stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name -| rename data.resource.labels.cluster_name as cluster_name -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `gcp_kubernetes_cluster_scan_detection_filter` -``` -#### Associated Analytic Story - -* Kubernetes Scanning Activity - - -#### How To Implement -You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1526 | x | x | - -#### Kill Chain Phases - -* Reconnaissance - - -#### Known False Positives -Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Hiding Files And Directories With Attrib exe Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1222.001](https://attack.mitre.org/techniques/T1222.001/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -15999,16 +11031,16 @@ Attackers leverage an existing Windows binary, attrib.exe, to mark specific as h #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1222.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1222.001 | Windows File and Directory Permissions Modification | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16016,7 +11048,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives Some applications and users may legitimately use attrib.exe to interact with the files. -#### References +#### Reference #### Test Dataset @@ -16033,12 +11065,12 @@ _version_: 4 This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1110.001](https://attack.mitre.org/techniques/T1110.001/) - **Last Updated**: 2020-12-16
- View + details #### Search ``` @@ -16055,16 +11087,16 @@ This search will detect more than 5 login failures in Office365 Azure Active Dir #### How To Implement -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1110.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1110.001 | Password Guessing | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16072,123 +11104,7 @@ This search will detect more than 5 login failures in Office365 Azure Active Dir #### Known False Positives unknown -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Hosts receiving high volume of network traffic from email server -This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic -- **ATT&CK**: [T1114.002](https://attack.mitre.org/techniques/T1114.002/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d -| `drop_dm_object_name("All_Traffic")` -| eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in -| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip -| eval minimum_data_samples = 4, deviation_threshold = 3 -| where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") -| eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) -| table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average -| `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` -``` -#### Associated Analytic Story - -* Collection and Staging - - -#### How To Implement -This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.002 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Identify New User Accounts -This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078.002](https://attack.mitre.org/techniques/T1078.002/) -- **Last Updated**: 2017-09-12 - -
- View - -#### Search -``` - -| from datamodel Identity_Management.All_Identities -| eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") -| search empStatus="Accounts created in last week" -| `security_content_ctime(endDate)` -| `security_content_ctime(startDate)` -| table identity empStatus endDate startDate -| `identify_new_user_accounts_filter` -``` -#### Associated Analytic Story - -* Account Monitoring and Controls - - -#### How To Implement -To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.002 | x | x | - -#### Kill Chain Phases - - -#### Known False Positives -If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. - -#### References +#### Reference #### Test Dataset @@ -16203,12 +11119,12 @@ _version_: 1 This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1021](https://attack.mitre.org/techniques/T1021/), [T1113](https://attack.mitre.org/techniques/T1113/), [T1123](https://attack.mitre.org/techniques/T1123/), [T1563](https://attack.mitre.org/techniques/T1563/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -16227,7 +11143,7 @@ This detection identifies access to PowerSploit modules that enable illegaly acc #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -16240,14 +11156,14 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021 | x | x | -| T1113 | x | x | -| T1123 | x | x | -| T1563 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1021 | Remote Services | Lateral Movement | +| T1113 | Screen Capture | Collection | +| T1123 | Audio Capture | Collection | +| T1563 | Remote Service Session Hijacking | Lateral Movement | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16255,7 +11171,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -16272,12 +11188,12 @@ _version_: 1 This detection identifies access to PowerSploit modules that create accounts illegaly. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1585](https://attack.mitre.org/techniques/T1585/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -16296,7 +11212,7 @@ This detection identifies access to PowerSploit modules that create accounts ill #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -16309,11 +11225,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1585 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1585 | Establish Accounts | Resource Development | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16321,7 +11237,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -16338,12 +11254,12 @@ _version_: 1 This detection identifies access to PowerSploit modules that delete event logs. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1070](https://attack.mitre.org/techniques/T1070/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -16362,7 +11278,7 @@ This detection identifies access to PowerSploit modules that delete event logs. #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -16375,11 +11291,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1070 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1070 | Indicator Removal on Host | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16387,7 +11303,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -16404,12 +11320,12 @@ _version_: 1 This detection identifies use of DSInternals modules that enable or disable accounts illegaly. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -16428,7 +11344,7 @@ This detection identifies use of DSInternals modules that enable or disable acco #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -16441,12 +11357,12 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | -| T1098 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1098 | Account Manipulation | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16454,7 +11370,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/MichaelGrafnetter/DSInternals @@ -16471,12 +11387,12 @@ _version_: 1 This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1098](https://attack.mitre.org/techniques/T1098/), [T1207](https://attack.mitre.org/techniques/T1207/), [T1484](https://attack.mitre.org/techniques/T1484/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -16495,7 +11411,7 @@ This detection identifies use of DSInternals modules for illegal management of A #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -16508,13 +11424,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1098 | x | x | -| T1207 | x | x | -| T1484 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1098 | Account Manipulation | Persistence | +| T1207 | Rogue Domain Controller | Defense Evasion | +| T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16522,7 +11438,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/MichaelGrafnetter/DSInternals @@ -16539,12 +11455,12 @@ _version_: 1 This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1098](https://attack.mitre.org/techniques/T1098/), [T1207](https://attack.mitre.org/techniques/T1207/), [T1484](https://attack.mitre.org/techniques/T1484/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -16564,7 +11480,7 @@ This detection identifies access to PowerSploit modules that enable illegal mana #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -16577,13 +11493,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1098 | x | x | -| T1207 | x | x | -| T1484 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1098 | Account Manipulation | Persistence | +| T1207 | Rogue Domain Controller | Defense Evasion | +| T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16591,7 +11507,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -16608,12 +11524,12 @@ _version_: 1 This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1053](https://attack.mitre.org/techniques/T1053/), [T1134](https://attack.mitre.org/techniques/T1134/), [T1548](https://attack.mitre.org/techniques/T1548/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -16632,7 +11548,7 @@ This detection identifies access to PowerSploit modules that illegaly elevate ge #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -16645,13 +11561,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1053 | x | x | -| T1134 | x | x | -| T1548 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation | +| T1134 | Access Token Manipulation | Defense Evasion, Privilege Escalation | +| T1548 | Abuse Elevation Control Mechanism | Defense Evasion, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16659,7 +11575,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -16676,12 +11592,12 @@ _version_: 1 This detection identifies use of Mimikatz modules for illegal privilege elevation. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1134](https://attack.mitre.org/techniques/T1134/), [T1548](https://attack.mitre.org/techniques/T1548/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -16700,7 +11616,7 @@ This detection identifies use of Mimikatz modules for illegal privilege elevatio #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -16713,12 +11629,12 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1134 | x | x | -| T1548 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1134 | Access Token Manipulation | Defense Evasion, Privilege Escalation | +| T1548 | Abuse Elevation Control Mechanism | Defense Evasion, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16726,7 +11642,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -16743,12 +11659,12 @@ _version_: 1 This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1106](https://attack.mitre.org/techniques/T1106/), [T1569](https://attack.mitre.org/techniques/T1569/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -16767,7 +11683,7 @@ This detection identifies use of Mimikatz modules for illegal control over servi #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -16780,13 +11696,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1055 | x | x | -| T1106 | x | x | -| T1569 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1055 | Process Injection | Defense Evasion, Privilege Escalation | +| T1106 | Native API | Execution | +| T1569 | System Services | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16794,7 +11710,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -16811,12 +11727,12 @@ _version_: 1 This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1106](https://attack.mitre.org/techniques/T1106/), [T1569](https://attack.mitre.org/techniques/T1569/) - **Last Updated**: 2020-11-09
- View + details #### Search ``` @@ -16836,7 +11752,7 @@ This detection identifies access to PowerSploit modules that enable illegal cont #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -16849,13 +11765,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1055 | x | x | -| T1106 | x | x | -| T1569 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1055 | Process Injection | Defense Evasion, Privilege Escalation | +| T1106 | Native API | Execution | +| T1569 | System Services | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16863,7 +11779,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -16880,12 +11796,12 @@ _version_: 1 This search detects a potential kerberoasting attack via service principal name requests - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1558.003](https://attack.mitre.org/techniques/T1558.003/) - **Last Updated**: 2020-10-16
- View + details #### Search ``` @@ -16903,16 +11819,16 @@ This search detects a potential kerberoasting attack via service principal name #### How To Implement You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1558.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1558.003 | Kerberoasting | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -16920,7 +11836,7 @@ You must be ingesting endpoint data that tracks process activity, and include th #### Known False Positives Older systems that support kerberos RC4 by default NetApp may generate false positives -#### References +#### Reference * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md @@ -16937,1149 +11853,16 @@ _version_: 3 --- -### Kubernetes AWS detect RBAC authorization by account -This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* -| table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason -| stats count by user.username annotations.authorization.k8s.io/reason -| rare user.username annotations.authorization.k8s.io/reason -|`kubernetes_aws_detect_rbac_authorization_by_account_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Role Activity - - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes AWS detect most active service accounts by pod -This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts objectRef.resource=pods -| table sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision -| top sourceIPs{} user.username verb annotations.authorization.k8s.io/decision -|`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Role Activity - - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes AWS detect sensitive role access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 -| table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason -| dedup user.username user.groups{} -|`kubernetes_aws_detect_sensitive_role_access_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Role Activity - - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes AWS detect service accounts forbidden failure access -This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure -| table sourceIPs{} user.username userAgent verb responseStatus.status requestURI -| `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Object Access Activity - - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -This search can give false positives as there might be inherent issues with authentications and permissions at cluster. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes AWS detect suspicious kubectl calls -This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous -| table src_ip src_user verb userAgent requestURI -| stats count by src_ip src_user verb userAgent requestURI -|`kubernetes_aws_detect_suspicious_kubectl_calls_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Object Access Activity - - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes Azure detect RBAC authorization by account -This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-26 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit -| spath input=properties.log -| search annotations.authorization.k8s.io/reason=* -| table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason -|stats count by user.username annotations.authorization.k8s.io/reason -| rare user.username annotations.authorization.k8s.io/reason -|`kubernetes_azure_detect_rbac_authorization_by_account_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Role Activity - - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes Azure detect most active service accounts by pod namespace -This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-26 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit -| spath input=properties.log -| search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow -| table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace -| top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace -|`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Role Activity - - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes Azure detect sensitive object access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-20 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit -| spath input=properties.log -| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow -|table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason -|dedup user.username user.groups{} -|`kubernetes_azure_detect_sensitive_object_access_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Object Access Activity - - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes Azure detect sensitive role access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-20 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit -| spath input=properties.log -| search objectRef.resource=clusterroles OR clusterrolebindings -| table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason -| dedup user.username user.groups{} -|`kubernetes_azure_detect_sensitive_role_access_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Role Activity - - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes Azure detect service accounts forbidden failure access -This search provides information on Kubernetes service accounts with failure or forbidden access status - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-20 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit -| spath input=properties.log -| search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden -| table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace -|`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Object Access Activity - - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -This search can give false positives as there might be inherent issues with authentications and permissions at cluster. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes Azure detect suspicious kubectl calls -This search provides information on rare Kubectl calls with IP, verb namespace and object access context - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-26 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit -| spath input=properties.log -| spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration -| search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 -| table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI -| rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI -|`kubernetes_azure_detect_suspicious_kubectl_calls_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Object Access Activity - - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes Azure pod scan fingerprint -This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-05-20 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit -| spath input=properties.log -| search responseStatus.code=401 -| table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod -|`kubernetes_azure_pod_scan_fingerprint_filter` -``` -#### Associated Analytic Story - -* Kubernetes Scanning Activity - - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Reconnaissance - - -#### Known False Positives -Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes Azure scan fingerprint -This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) -- **Last Updated**: 2020-05-19 - -
- View - -#### Search -``` -`kubernetes_azure` category=kube-audit -| spath input=properties.log -| search responseStatus.code=401 -| table sourceIPs{} userAgent verb requestURI responseStatus.reason -|`kubernetes_azure_scan_fingerprint_filter` -``` -#### Associated Analytic Story - -* Kubernetes Scanning Activity - - -#### How To Implement -You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1526 | x | x | - -#### Kill Chain Phases - -* Reconnaissance - - -#### Known False Positives -Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes GCP detect RBAC authorizations by account -This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-07-11 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole -| table src_ip src_user data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason -| rare src_user data.labels.authorization.k8s.io/reason -|`kubernetes_gcp_detect_rbac_authorizations_by_account_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Role Activity - - -#### How To Implement -You must install splunk AWS add on for GCP. This search works with pubsub messaging service logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes GCP detect most active service accounts by pod -This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-07-10 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.request.spec.group{}=system:serviceaccounts -| table src_ip src_user http_user_agent data.protoPayload.request.spec.nonResourceAttributes.verb data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource -| top src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource -|`kubernetes_gcp_detect_most_active_service_accounts_by_pod_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Role Activity - - -#### How To Implement -You must install splunk GCP add on. This search works with pubsub messaging service logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes GCP detect sensitive object access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-07-11 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.resource=configmaps OR secrets -| table data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name data.protoPayload.request.metadata.namespace data.labels.authorization.k8s.io/decision -| dedup data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name -|`kubernetes_gcp_detect_sensitive_object_access_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Object Access Activity - - -#### How To Implement -You must install splunk add on for GCP . This search works with pubsub messaging service logs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes GCP detect sensitive role access -This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-07-11 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole dest=apis/rbac.authorization.k8s.io/v1 src_ip!=::1 -| table src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason -| dedup src_ip src_user -|`kubernetes_gcp_detect_sensitive_role_access_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Role Activity - - -#### How To Implement -You must install splunk add on for GCP. This search works with pubsub messaging servicelogs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Sensitive role resource access is necessary for cluster operation, however source IP, user agent, decision and reason may indicate possible malicious use. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes GCP detect service accounts forbidden failure access -This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-06-23 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` system:serviceaccounts data.protoPayload.response.status.allowed!=* -| table src_ip src_user http_user_agent data.protoPayload.response.spec.resourceAttributes.namespace data.resource.labels.cluster_name data.protoPayload.response.spec.resourceAttributes.verb data.protoPayload.request.status.allowed data.protoPayload.response.status.reason data.labels.authorization.k8s.io/decision -| dedup src_ip src_user -| `kubernetes_gcp_detect_service_accounts_forbidden_failure_access_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Object Access Activity - - -#### How To Implement -You must install splunk add on for GCP. This search works with pubsub messaging service logs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -This search can give false positives as there might be inherent issues with authentications and permissions at cluster. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Kubernetes GCP detect suspicious kubectl calls -This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-07-11 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous -| table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path -|dedup src_ip src_user -|`kubernetes_gcp_detect_suspicious_kubectl_calls_filter` -``` -#### Associated Analytic Story - -* Kubernetes Sensitive Object Access Activity - - -#### How To Implement -You must install splunk add on for GCP. This search works with pubsub messaging logs. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Large Volume of DNS ANY Queries -The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution -- **ATT&CK**: [T1498.002](https://attack.mitre.org/techniques/T1498.002/) -- **Last Updated**: 2017-09-20 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" -| `drop_dm_object_name("DNS")` -| where count>200 -| `large_volume_of_dns_any_queries_filter` -``` -#### Associated Analytic Story - -* DNS Amplification Attacks - - -#### How To Implement -To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1498.002 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### MacOS - Re-opened Applications -This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: -- **Last Updated**: 2020-02-07 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `macos___re_opened_applications_filter` -``` -#### Associated Analytic Story - - -#### How To Implement -In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Installation - -* Command and Control - - -#### Known False Positives -At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Malicious PowerShell Process - Connect To Internet With Hidden Window This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/) - **Last Updated**: 2020-11-20
- View + details #### Search ``` @@ -18100,16 +11883,16 @@ This search looks for PowerShell processes started with parameters to modify the #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059.001 | PowerShell | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Command and Control @@ -18119,7 +11902,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives Legitimate process can have this combination of command-line options, but it's not common. -#### References +#### Reference #### Test Dataset @@ -18136,12 +11919,12 @@ _version_: 5 This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1027](https://attack.mitre.org/techniques/T1027/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -18162,16 +11945,16 @@ This search looks for PowerShell processes that have encoded the script within t #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1027 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1027 | Obfuscated Files or Information | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Command and Control @@ -18181,7 +11964,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives System administrators may use this option, but it's not common. -#### References +#### Reference #### Test Dataset @@ -18198,12 +11981,12 @@ _version_: 4 This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -18222,16 +12005,16 @@ This search looks for PowerShell processes started with parameters used to bypas #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059.001 | PowerShell | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Command and Control @@ -18241,7 +12024,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. -#### References +#### Reference #### Test Dataset @@ -18254,75 +12037,16 @@ _version_: 4 --- -### Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments -This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/) -- **Last Updated**: 2021-01-19 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* -| `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter` -``` -#### Associated Analytic Story - -* Malicious PowerShell - - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.001 | x | x | - -#### Kill Chain Phases - -* Command and Control - -* Actions on Objectives - - -#### Known False Positives -Legitimate process can have this combination of command-line options, but it's not common. - -#### References - - -#### Test Dataset - - -_version_: 6 -
- ---- - ### Malicious PowerShell Process With Obfuscation Techniques This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/) - **Last Updated**: 2021-01-19
- View + details #### Search ``` @@ -18343,16 +12067,16 @@ This search looks for PowerShell processes launched with arguments that have cha #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059.001 | PowerShell | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Command and Control @@ -18362,7 +12086,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives These characters might be legitimately on the command-line, but it is not common. -#### References +#### Reference #### Test Dataset @@ -18375,135 +12099,16 @@ _version_: 4 --- -### Monitor DNS For Brand Abuse -This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Resolution -- **ATT&CK**: -- **Last Updated**: 2017-09-23 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query -| `drop_dm_object_name("DNS")` -| `security_content_ctime(firstTime)` -| `brand_abuse_dns` -| `monitor_dns_for_brand_abuse_filter` -``` -#### Associated Analytic Story - -* Brand Monitoring - - -#### How To Implement -You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Delivery - -* Actions on Objectives - - -#### Known False Positives -None at this time - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Monitor Email For Brand Abuse -This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Email -- **ATT&CK**: -- **Last Updated**: 2018-01-05 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id -| `drop_dm_object_name("All_Email")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| eval temp=split(src_user, "@") -| eval email_domain=mvindex(temp, 1) -| lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse -| search domain_abuse=true -| table message_id, src_user, email_domain, recipients, firstTime, lastTime -| `monitor_email_for_brand_abuse_filter` -``` -#### Associated Analytic Story - -* Brand Monitoring - -* Suspicious Emails - - -#### How To Implement -You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Delivery - - -#### Known False Positives -None at this time - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Monitor Registry Keys for Print Monitors This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1547.010](https://attack.mitre.org/techniques/T1547.010/) - **Last Updated**: 2020-11-23
- View + details #### Search ``` @@ -18522,16 +12127,16 @@ This search looks for registry activity associated with modifications to the reg #### How To Implement To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1547.010 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1547.010 | Port Monitors | Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -18539,7 +12144,7 @@ To successfully implement this search, you must be ingesting data that records r #### Known False Positives You will encounter noise from legitimate print-monitor registry entries. -#### References +#### Reference #### Test Dataset @@ -18552,71 +12157,16 @@ _version_: 2 --- -### Monitor Web Traffic For Brand Abuse -This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Web -- **ATT&CK**: -- **Last Updated**: 2017-09-23 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src -| `drop_dm_object_name("Web")` -| `security_content_ctime(firstTime)` -| `brand_abuse_web` -| `monitor_web_traffic_for_brand_abuse_filter` -``` -#### Associated Analytic Story - -* Brand Monitoring - - -#### How To Implement -You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Delivery - - -#### Known False Positives -None at this time - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### More than usual number of LOLBAS applications in short time period Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/), [T1053](https://attack.mitre.org/techniques/T1053/) - **Last Updated**: 2020-08-25
- View + details #### Search ``` @@ -18638,7 +12188,7 @@ Attacker activity may compromise executing several LOLBAS applications in conjun #### How To Implement Collect endpoint data such as sysmon or 4688 events. -#### Required fields +#### Required field * dest_device_id @@ -18649,12 +12199,12 @@ Collect endpoint data such as sysmon or 4688 events. #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059 | x | x | -| T1053 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059 | Command and Scripting Interpreter | Execution | +| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -18663,7 +12213,7 @@ Collect endpoint data such as sysmon or 4688 events. Some administrative tasks may involve multiple use of LOLBAS applications in a short period of time. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. -#### References +#### Reference * https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries @@ -18680,12 +12230,12 @@ _version_: 1 This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078.001/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -18705,22 +12255,22 @@ This search detects Okta login failures due to bad credentials for multiple user #### How To Implement This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.001 | Default Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search. -#### References +#### Reference #### Test Dataset @@ -18735,12 +12285,12 @@ _version_: 2 This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1482](https://attack.mitre.org/techniques/T1482/) - **Last Updated**: 2021-01-25
- View + details #### Search ``` @@ -18759,16 +12309,16 @@ This search looks for the execution of `nltest.exe` with command-line arguments #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1482 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1482 | Domain Trust Discovery | Discovery | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -18776,7 +12326,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Administrators may use nltest for troubleshooting purposes, otherwise, rarely used. -#### References +#### Reference * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md @@ -18807,12 +12357,12 @@ _version_: 1 This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1525](https://attack.mitre.org/techniques/T1525/) - **Last Updated**: 2020-02-20
- View + details #### Search ``` @@ -18829,80 +12379,22 @@ This searches show information on uploaded containers including source user, ima #### How To Implement You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1525 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1525 | Implant Container Image | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives Uploading container is a normal behavior from developers or users with access to container registry. -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### No Windows Updates in a time frame -This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Updates -- **ATT&CK**: -- **Last Updated**: 2017-09-15 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product -| rename Updates.dest as Host -| rename Updates.status as "Update Status" -| rename Updates.vendor_product as Product -| eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) -| `security_content_ctime(lastTime)` -| search isOutlier=1 -| rename lastTime as "Last Update Time", -| table Host, "Update Status", Product, "Last Update Time" -| `no_windows_updates_in_a_time_frame_filter` -``` -#### Associated Analytic Story - -* Monitor for Updates - - -#### How To Implement -To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - - -#### Known False Positives -None identified - -#### References +#### Reference #### Test Dataset @@ -18919,12 +12411,12 @@ ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003.003/) - **Last Updated**: 2021-01-28
- View + details #### Search ``` @@ -18943,16 +12435,16 @@ This technique uses "Install from Media" (IFM), which will extract a copy of the #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.003 | NTDS | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -18960,7 +12452,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives. -#### References +#### Reference * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil @@ -18985,12 +12477,12 @@ _version_: 1 This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136.003/) - **Last Updated**: 2021-01-26
- View + details #### Search ``` @@ -19010,16 +12502,16 @@ This search detects the creation of a new Federation setting by alerting about a #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1136.003 | Cloud Account | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objective @@ -19027,7 +12519,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Known False Positives The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. -#### References +#### Reference * https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf @@ -19048,12 +12540,12 @@ _version_: 1 This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136.003/) - **Last Updated**: 2021-01-26
- View + details #### Search ``` @@ -19073,16 +12565,16 @@ This search detects the creation of a new Federation setting by alerting about a #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1136.003 | Cloud Account | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objective @@ -19090,7 +12582,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Known False Positives The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. -#### References +#### Reference * https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf @@ -19115,12 +12607,12 @@ _version_: 1 This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562.007/) - **Last Updated**: 2021-01-12
- View + details #### Search ``` @@ -19143,16 +12635,16 @@ This search detects newly added IP addresses/CIDR blocks to the list of MFA Trus #### How To Implement You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.007 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objective @@ -19160,7 +12652,7 @@ You must install Splunk Microsoft Office 365 add-on. This search works with o365 #### Known False Positives Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration. -#### References +#### Reference * https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf @@ -19181,12 +12673,12 @@ _version_: 1 This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1556](https://attack.mitre.org/techniques/T1556/) - **Last Updated**: 2020-12-16
- View + details #### Search ``` @@ -19204,16 +12696,16 @@ This search detects when multi factor authentication has been disabled, what ent #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1556 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1556 | Modify Authentication Process | Credential Access, Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objective @@ -19221,7 +12713,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Known False Positives Unless it is a special case, it is uncommon to disable MFA or Strong Authentication -#### References +#### Reference * https://attack.mitre.org/techniques/T1556/ @@ -19240,12 +12732,12 @@ _version_: 1 This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1110](https://attack.mitre.org/techniques/T1110/) - **Last Updated**: 2020-12-16
- View + details #### Search ``` @@ -19264,16 +12756,16 @@ This search detects when an excessive number of authentication failures occur th #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1110 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1110 | Brute Force | Credential Access | -#### Kill Chain Phases +#### Kill Chain Phase * Not Applicable @@ -19281,7 +12773,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Known False Positives The threshold for alert is above 10 attempts and this should reduce the number of false positives. -#### References +#### Reference * https://attack.mitre.org/techniques/T1110/ @@ -19300,12 +12792,12 @@ _version_: 1 This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1556](https://attack.mitre.org/techniques/T1556/) - **Last Updated**: 2021-01-26
- View + details #### Search ``` @@ -19326,16 +12818,16 @@ This search detects accounts with high number of Single Sign ON (SSO) logon erro #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1556 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1556 | Modify Authentication Process | Credential Access, Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objective @@ -19343,7 +12835,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Known False Positives Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack. -#### References +#### Reference * https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/ @@ -19362,12 +12854,12 @@ _version_: 1 This search detects the addition of a new Federated domain. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136.003/) - **Last Updated**: 2021-01-26
- View + details #### Search ``` @@ -19387,16 +12879,16 @@ This search detects the addition of a new Federated domain. #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1136.003 | Cloud Account | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objective @@ -19404,7 +12896,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Known False Positives The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider. -#### References +#### Reference * https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf @@ -19431,12 +12923,12 @@ _version_: 1 This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1114](https://attack.mitre.org/techniques/T1114/) - **Last Updated**: 2020-12-16
- View + details #### Search ``` @@ -19454,16 +12946,16 @@ This search detects when a user has performed an Ediscovery search or exported a #### How To Implement You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1114 | Email Collection | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objective @@ -19471,7 +12963,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Known False Positives PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored. -#### References +#### Reference * https://attack.mitre.org/techniques/T1114/ @@ -19490,12 +12982,12 @@ _version_: 1 This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1114.003](https://attack.mitre.org/techniques/T1114.003/) - **Last Updated**: 2020-12-16
- View + details #### Search ``` @@ -19517,16 +13009,16 @@ This search detects when an admin configured a forwarding rule for multiple mail #### How To Implement -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1114.003 | Email Forwarding Rule | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -19534,7 +13026,7 @@ This search detects when an admin configured a forwarding rule for multiple mail #### Known False Positives unknown -#### References +#### Reference #### Test Dataset @@ -19551,12 +13043,12 @@ _version_: 1 This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1114.002](https://attack.mitre.org/techniques/T1114.002/) - **Last Updated**: 2020-12-15
- View + details #### Search ``` @@ -19577,16 +13069,16 @@ This search detects the assignment of rights to accesss content from another mai #### How To Implement -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1114.002 | Remote Email Collection | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -19594,7 +13086,7 @@ This search detects the assignment of rights to accesss content from another mai #### Known False Positives Service Accounts -#### References +#### Reference #### Test Dataset @@ -19611,12 +13103,12 @@ _version_: 1 This search detects when multiple user configured a forwarding rule to the same destination. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1114.003](https://attack.mitre.org/techniques/T1114.003/) - **Last Updated**: 2020-12-16
- View + details #### Search ``` @@ -19638,16 +13130,16 @@ This search detects when multiple user configured a forwarding rule to the same #### How To Implement -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1114.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1114.003 | Email Forwarding Rule | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -19655,7 +13147,7 @@ This search detects when multiple user configured a forwarding rule to the same #### Known False Positives unknown -#### References +#### Reference #### Test Dataset @@ -19672,12 +13164,12 @@ _version_: 1 Detect Okta user lockout events - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078.001/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -19694,22 +13186,22 @@ Detect Okta user lockout events #### How To Implement This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.001 | Default Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. -#### References +#### Reference #### Test Dataset @@ -19724,12 +13216,12 @@ _version_: 2 Detect failed Okta SSO events - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078.001/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -19747,22 +13239,22 @@ Detect failed Okta SSO events #### How To Implement This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.001 | Default Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives There may be a faulty config preventing legitmate users from accessing apps they should have access to. -#### References +#### Reference #### Test Dataset @@ -19777,12 +13269,12 @@ _version_: 2 This search detects logins from the same user from different cities in a 24 hour period. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078.001/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -19801,22 +13293,22 @@ This search detects logins from the same user from different cities in a 24 hour #### How To Implement This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.001 | Default Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. -#### References +#### Reference #### Test Dataset @@ -19827,125 +13319,16 @@ _version_: 2 --- -### Open Redirect in Splunk Web -This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-19 - -
- View - -#### Search -``` -index=_internal sourcetype=splunk_web_access return_to="/%09/*" -| `open_redirect_in_splunk_web_filter` -``` -#### Associated Analytic Story - -* Splunk Enterprise Vulnerability - - -#### How To Implement -No extra steps needed to implement this search. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Delivery - - -#### Known False Positives -None identified - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Osquery pack - ColdRoot detection -This search looks for ColdRoot events from the osx-attacks osquery pack. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2019-01-29 - -
- View - -#### Search -``` - -| from datamodel Alerts.Alerts -| search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) -| rename columns.path as path -| bucket _time span=30s -| stats count(path) by _time, host, user, path -| `osquery_pack___coldroot_detection_filter` -``` -#### Associated Analytic Story - -* ColdRoot MacOS RAT - - -#### How To Implement -In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Installation - -* Command and Control - - -#### Known False Positives -There are no known false positives. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Overwriting Accessibility Binaries Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1546.008](https://attack.mitre.org/techniques/T1546.008/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -19964,16 +13347,16 @@ Microsoft Windows contains accessibility features that can be launched with a ke #### How To Implement You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.008 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1546.008 | Accessibility Features | Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -19981,7 +13364,7 @@ You must be ingesting data that records the filesystem activity from your hosts #### Known False Positives Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. -#### References +#### Reference #### Test Dataset @@ -19994,76 +13377,16 @@ _version_: 4 --- -### Phishing Email Detection by Machine Learning Method - SSA -Malicious mails can conduct phishing that induces readers to open attachment, click links or trigger third party service. This detect uses Natural Language Processing (NLP) approach to analyze an email message's content (Sender, Subject and Body) and judge whether it is a phishing email. The detection adopts a deep learning (neural network) model that employs character level embeddings plus LSTM layers to perform classification. The model is pre-trained and then published as ONNX format. Current sample model is trained using the dataset published at https://github.com/splunk/attack_data/tree/master/datasets/T1566_Phishing_Email/splunk_train.json User are expected to re-train the model by combining with their own training data for better accuracy using the provided model file (SMLE notebook). DSP pipeline then processes the email message and passes it as an event to Apply ML Models function, which returns the probability of a phishing email. Current implementation assumes the email is fed to DSP in JSON format contains at least email's sender, subject and its message body, including reply content, if any. - -- **Product**: UEBA for Security Cloud -- **Data Models**: -- **ATT&CK**: [T1566](https://attack.mitre.org/techniques/T1566/) -- **Last Updated**: 2020-08-25 - -
- View - -#### Search -``` - -| from read_ssa_enriched_events() -| eval eventLine=concat(ucast(map_get(input_event, "From"), "string", " "), " ", ucast(map_get(input_event, "Subject"), "string", " "), " ", ucast(map_get(input_event, "Content"), "string", " "), " "), _time=map_get(input_event, "_time") -| where eventLine IS NOT NULL -| eval mapC={" ": 32, "!": 33, "\"": 34, "#": 35, "$": 36, "%": 37, "&": 38, "`": 39, "(": 40, ")": 41, "*": 42, "+": 43, ",": 44, "-": 45, ".": 46, "/": 47, "0": 48, "1": 49, "2": 50, "3": 51, "4": 52, "5": 53, "6": 54, "7": 55, "8": 56, "9": 57, ":": 58, ";": 59, "<": 60, "=": 61, ">": 62, "?": 63, "@": 64, "A": 65, "B": 66, "C": 67, "D": 68, "E": 69, "F": 70, "G": 71, "H": 72, "I": 73, "J": 74, "K": 75, "L": 76, "M": 77, "N": 78, "O": 79, "P": 80, "Q": 81, "R": 82, "S": 83, "T": 84, "U": 85, "V": 86, "W": 87, "X": 88, "Y": 89, "Z": 90, "[": 91, "\\": 92, "]": 93, "^": 94, "_": 95, "`": 96, "a": 97, "b": 98, "c": 99, "d": 100, "e": 101, "f": 102, "g": 103, "h": 104, "i": 105, "j": 106, "k": 107, "l": 108, "m": 109, "n": 110, "o": 111, "p": 112, "q": 113, "r": 114, "s": 115, "t": 116, "u": 117, "v": 118, "w": 119, "x": 120, "y": 121, "z": 122, "{": 123, " -|": 124, "}": 125, "~": 126}, ml_in = for_each(iterator(mvrange(1,129), "i"), cast(map_get(mapC, substr(eventLine, i, 1)), "float") ) -| apply_model connection_id="YOUR_S3_ONNX_CONNECTOR_ID" name="phishing_email_v8" path="s3://smle-experiments/models/phishing_email" -| eval probability = mvindex(ml_out, 0) -| where probability > 0.5 -| eval start_time=_time, end_time=_time, entities="TBD", body="TBD" -| select probability, body, entities, start_time, end_time -| into write_ssa_detected_events(); -``` -#### Associated Analytic Story - - -#### How To Implement -Events are fed to DSP contains at least email's sender, subject and its message body. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1566 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Because of imbalance of anomaly data in training, the model will less likely report false positive. Instead, the model is more prone to false negative. Current best recall score is ~85% - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Probing Access with Stolen Credentials via PowerSploit modules This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-04
- View + details #### Search ``` @@ -20082,7 +13405,7 @@ This detection identifies use of PowerSploit modules that facilitate access prob #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -20095,12 +13418,12 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | -| T1098 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1098 | Account Manipulation | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -20108,7 +13431,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -20125,12 +13448,12 @@ _version_: 1 This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1566.002](https://attack.mitre.org/techniques/T1566.002/) - **Last Updated**: 2021-01-28
- View + details #### Search ``` @@ -20156,16 +13479,16 @@ This search looks for a process launching an `*.lnk` file under `C:\User*` or `* #### How To Implement You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1566.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1566.002 | Spearphishing Link | Initial Access | -#### Kill Chain Phases +#### Kill Chain Phase * Installation @@ -20175,7 +13498,7 @@ You must be ingesting data that records filesystem and process activity from you #### Known False Positives This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories. -#### References +#### Reference * https://attack.mitre.org/techniques/T1566/001/ @@ -20196,12 +13519,12 @@ _version_: 4 This search looks for processes launched via WMI. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) - **Last Updated**: 2020-03-16
- View + details #### Search ``` @@ -20220,16 +13543,16 @@ This search looks for processes launched via WMI. #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1047 | Windows Management Instrumentation | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -20237,7 +13560,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives Although unlikely, administrators may use wmi to execute commands for legitimate purposes. -#### References +#### Reference #### Test Dataset @@ -20250,128 +13573,16 @@ _version_: 3 --- -### Processes Tapping Keyboard Events -This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2019-01-25 - -
- View - -#### Search -``` - -| from datamodel Alerts.Alerts -| search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps -| rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id -| dedup host,process_name -| table host,process_name, cmd, process_id -| `processes_tapping_keyboard_events_filter` -``` -#### Associated Analytic Story - -* ColdRoot MacOS RAT - - -#### How To Implement -In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Command and Control - - -#### Known False Positives -There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Processes created by netsh -This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1562.004](https://attack.mitre.org/techniques/T1562.004/) -- **Last Updated**: 2020-11-23 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `processes_created_by_netsh_filter` -``` -#### Associated Analytic Story - -* Netsh Abuse - - -#### How To Implement -To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.004 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft. - -#### References - - -#### Test Dataset - - -_version_: 5 -
- ---- - ### Processes launching netsh This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1562.004](https://attack.mitre.org/techniques/T1562.004/) - **Last Updated**: 2020-07-10
- View + details #### Search ``` @@ -20394,16 +13605,16 @@ This search looks for processes launching netsh.exe. Netsh is a command-line scr #### How To Implement To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.004 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1562.004 | Disable or Modify System Firewall | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -20411,7 +13622,7 @@ To successfully implement this search, you must be ingesting data that records p #### Known False Positives Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. -#### References +#### Reference #### Test Dataset @@ -20424,259 +13635,16 @@ _version_: 3 --- -### Prohibited Network Traffic Allowed -This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic -- **ATT&CK**: [T1048](https://attack.mitre.org/techniques/T1048/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action -| lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport -| search is_prohibited=true -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name("All_Traffic")` -| `prohibited_network_traffic_allowed_filter` -``` -#### Associated Analytic Story - -* Prohibited Traffic Allowed or Protocol Mismatch - -* Ransomware - -* Command and Control - - -#### How To Implement -In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048 | x | x | - -#### Kill Chain Phases - -* Delivery - -* Command and Control - - -#### Known False Positives -None identified - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Prohibited Software On Endpoint -This search looks for applications on the endpoint that you have marked as prohibited. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: -- **Last Updated**: 2019-10-11 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name(Processes)` -| `prohibited_softwares` -| `prohibited_software_on_endpoint_filter` -``` -#### Associated Analytic Story - -* Monitor for Unauthorized Software - -* Emotet Malware DHS Report TA18-201A - -* SamSam Ransomware - - -#### How To Implement -To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Installation - -* Command and Control - -* Actions on Objectives - - -#### Known False Positives -None identified - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Protocol or Port Mismatch -This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic -- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port -|`security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name("All_Traffic")` -| `protocol_or_port_mismatch_filter` -``` -#### Associated Analytic Story - -* Prohibited Traffic Allowed or Protocol Mismatch - -* Command and Control - - -#### How To Implement -Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1048.003 | x | x | - -#### Kill Chain Phases - -* Command and Control - - -#### Known False Positives -None identified - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - -### Protocols passing authentication in cleartext -This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic -- **ATT&CK**: -- **Last Updated**: 2020-11-04 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name("All_Traffic")` -| `protocols_passing_authentication_in_cleartext_filter` -``` -#### Associated Analytic Story - -* Use of Cleartext Protocols - - -#### How To Implement -This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Reconnaissance - -* Actions on Objectives - - -#### Known False Positives -Some networks may use kerberized FTP or telnet servers, however, this is rare. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Rare Parent-Child Process Relationship An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/), [T1059](https://attack.mitre.org/techniques/T1059/), [T1053](https://attack.mitre.org/techniques/T1053/), [T1072](https://attack.mitre.org/techniques/T1072/) - **Last Updated**: 2020-08-13
- View + details #### Search ``` @@ -20701,7 +13669,7 @@ An attacker may use LOLBAS tools spawned from vulnerable applications not typica #### How To Implement Collect endpoint data such as sysmon or 4688 events. -#### Required fields +#### Required field * process_name @@ -20716,14 +13684,14 @@ Collect endpoint data such as sysmon or 4688 events. #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1203 | x | x | -| T1059 | x | x | -| T1053 | x | x | -| T1072 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1203 | Exploitation for Client Execution | Execution | +| T1059 | Command and Scripting Interpreter | Execution | +| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation | +| T1072 | Software Deployment Tools | Execution, Lateral Movement | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -20732,7 +13700,7 @@ Collect endpoint data such as sysmon or 4688 events. Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. -#### References +#### Reference #### Test Dataset @@ -20747,12 +13715,12 @@ _version_: 1 This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1087](https://attack.mitre.org/techniques/T1087/), [T1484](https://attack.mitre.org/techniques/T1484/) - **Last Updated**: 2020-11-05
- View + details #### Search ``` @@ -20771,7 +13739,7 @@ This detection identifies access to PowerSploit modules that discover accounts, #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -20784,13 +13752,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | -| T1087 | x | x | -| T1484 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1087 | Account Discovery | Discovery | +| T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -20798,7 +13766,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -20815,12 +13783,12 @@ _version_: 1 This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1087](https://attack.mitre.org/techniques/T1087/), [T1484](https://attack.mitre.org/techniques/T1484/) - **Last Updated**: 2020-11-05
- View + details #### Search ``` @@ -20839,7 +13807,7 @@ This detection identifies use of Mimikatz modules for discovery of accounts and #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -20852,13 +13820,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | -| T1087 | x | x | -| T1484 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1087 | Account Discovery | Discovery | +| T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -20866,7 +13834,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -20883,12 +13851,12 @@ _version_: 1 This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1199](https://attack.mitre.org/techniques/T1199/), [T1482](https://attack.mitre.org/techniques/T1482/), [T1590](https://attack.mitre.org/techniques/T1590/), [T1591](https://attack.mitre.org/techniques/T1591/), [T1595](https://attack.mitre.org/techniques/T1595/) - **Last Updated**: 2020-11-06
- View + details #### Search ``` @@ -20907,7 +13875,7 @@ This detection identifies access to PowerSploit modules for reconnaissance and a #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -20920,15 +13888,15 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1199 | x | x | -| T1482 | x | x | -| T1590 | x | x | -| T1591 | x | x | -| T1595 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1199 | Trusted Relationship | Initial Access | +| T1482 | Domain Trust Discovery | Discovery | +| T1590 | Gather Victim Network Information | Reconnaissance | +| T1591 | Gather Victim Org Information | Reconnaissance | +| T1595 | Active Scanning | Reconnaissance | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -20936,7 +13904,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -20953,12 +13921,12 @@ _version_: 1 This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1592](https://attack.mitre.org/techniques/T1592/), [T1590](https://attack.mitre.org/techniques/T1590/), [T1087](https://attack.mitre.org/techniques/T1087/) - **Last Updated**: 2020-11-06
- View + details #### Search ``` @@ -20977,7 +13945,7 @@ This detection identifies access to PowerSploit modules that discover computers, #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -20990,13 +13958,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1592 | x | x | -| T1590 | x | x | -| T1087 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1592 | Gather Victim Host Information | Reconnaissance | +| T1590 | Gather Victim Network Information | Reconnaissance | +| T1087 | Account Discovery | Discovery | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21004,7 +13972,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -21021,12 +13989,12 @@ _version_: 1 This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1592](https://attack.mitre.org/techniques/T1592/) - **Last Updated**: 2020-11-06
- View + details #### Search ``` @@ -21045,7 +14013,7 @@ This detection identifies use of Mimikatz modules for discovery of computers and #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -21058,11 +14026,11 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1592 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1592 | Gather Victim Host Information | Reconnaissance | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21070,7 +14038,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -21087,12 +14055,12 @@ _version_: 1 This detection identifies access to PowerSploit modules that discover and access operating system elements, such as processes, services, registry locations, security packages and files. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1007](https://attack.mitre.org/techniques/T1007/), [T1012](https://attack.mitre.org/techniques/T1012/), [T1046](https://attack.mitre.org/techniques/T1046/), [T1047](https://attack.mitre.org/techniques/T1047/), [T1057](https://attack.mitre.org/techniques/T1057/), [T1083](https://attack.mitre.org/techniques/T1083/), [T1518](https://attack.mitre.org/techniques/T1518/), [T1592.002](https://attack.mitre.org/techniques/T1592.002/) - **Last Updated**: 2020-11-06
- View + details #### Search ``` @@ -21111,7 +14079,7 @@ This detection identifies access to PowerSploit modules that discover and access #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -21124,18 +14092,18 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1007 | x | x | -| T1012 | x | x | -| T1046 | x | x | -| T1047 | x | x | -| T1057 | x | x | -| T1083 | x | x | -| T1518 | x | x | -| T1592.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1007 | System Service Discovery | Discovery | +| T1012 | Query Registry | Discovery | +| T1046 | Network Service Scanning | Discovery | +| T1047 | Windows Management Instrumentation | Execution | +| T1057 | Process Discovery | Discovery | +| T1083 | File and Directory Discovery | Discovery | +| T1518 | Software Discovery | Discovery | +| T1592.002 | Software | Reconnaissance | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21143,7 +14111,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -21160,12 +14128,12 @@ _version_: 1 This detection identifies use of Mimikatz modules for discovery and access to services and processes. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1007](https://attack.mitre.org/techniques/T1007/), [T1046](https://attack.mitre.org/techniques/T1046/), [T1057](https://attack.mitre.org/techniques/T1057/) - **Last Updated**: 2020-11-06
- View + details #### Search ``` @@ -21184,7 +14152,7 @@ This detection identifies use of Mimikatz modules for discovery and access to se #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -21197,13 +14165,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1007 | x | x | -| T1046 | x | x | -| T1057 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1007 | System Service Discovery | Discovery | +| T1046 | Network Service Scanning | Discovery | +| T1057 | Process Discovery | Discovery | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21211,7 +14179,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -21228,12 +14196,12 @@ _version_: 1 This detection identifies use of Mimikatz modules for discovery and access to network shares. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/), [T1135](https://attack.mitre.org/techniques/T1135/), [T1039](https://attack.mitre.org/techniques/T1039/) - **Last Updated**: 2020-11-06
- View + details #### Search ``` @@ -21252,7 +14220,7 @@ This detection identifies use of Mimikatz modules for discovery and access to ne #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -21265,13 +14233,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.002 | x | x | -| T1135 | x | x | -| T1039 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | +| T1135 | Network Share Discovery | Discovery | +| T1039 | Data from Network Shared Drive | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21279,7 +14247,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -21296,12 +14264,12 @@ _version_: 1 This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/), [T1135](https://attack.mitre.org/techniques/T1135/), [T1039](https://attack.mitre.org/techniques/T1039/) - **Last Updated**: 2020-11-06
- View + details #### Search ``` @@ -21320,7 +14288,7 @@ This detection identifies access to PowerSploit modules that discover and access #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -21333,13 +14301,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.002 | x | x | -| T1135 | x | x | -| T1039 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | +| T1135 | Network Share Discovery | Discovery | +| T1039 | Data from Network Shared Drive | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21347,7 +14315,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -21364,12 +14332,12 @@ _version_: 1 This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1053](https://attack.mitre.org/techniques/T1053/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1543](https://attack.mitre.org/techniques/T1543/), [T1547](https://attack.mitre.org/techniques/T1547/), [T1574](https://attack.mitre.org/techniques/T1574/) - **Last Updated**: 2020-11-05
- View + details #### Search ``` @@ -21388,7 +14356,7 @@ This detection identifies use of PowerSploit modules that discover opportunities #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -21401,16 +14369,16 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1053 | x | x | -| T1068 | x | x | -| T1078 | x | x | -| T1543 | x | x | -| T1547 | x | x | -| T1574 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation | +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1543 | Create or Modify System Process | Persistence, Privilege Escalation | +| T1547 | Boot or Logon Autostart Execution | Persistence, Privilege Escalation | +| T1574 | Hijack Execution Flow | Defense Evasion, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21418,7 +14386,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -21435,12 +14403,12 @@ _version_: 1 This detection identifies access to PowerSploit modules for reconnaissance of connectivity. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/), [T1135](https://attack.mitre.org/techniques/T1135/), [T1039](https://attack.mitre.org/techniques/T1039/) - **Last Updated**: 2020-11-06
- View + details #### Search ``` @@ -21459,7 +14427,7 @@ This detection identifies access to PowerSploit modules for reconnaissance of co #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -21472,13 +14440,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.002 | x | x | -| T1135 | x | x | -| T1039 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | +| T1135 | Network Share Discovery | Discovery | +| T1039 | Data from Network Shared Drive | Collection | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21486,7 +14454,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -21503,12 +14471,12 @@ _version_: 1 This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1589.001](https://attack.mitre.org/techniques/T1589.001/), [T1590.001](https://attack.mitre.org/techniques/T1590.001/), [T1590.003](https://attack.mitre.org/techniques/T1590.003/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-03
- View + details #### Search ``` @@ -21527,7 +14495,7 @@ This detection identifies reconnaissance of credential stores and use of CryptoA #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -21540,16 +14508,16 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1589.001 | x | x | -| T1590.001 | x | x | -| T1590.003 | x | x | -| T1068 | x | x | -| T1078 | x | x | -| T1098 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1589.001 | Credentials | Reconnaissance | +| T1590.001 | Domain Properties | Reconnaissance | +| T1590.003 | Network Trust Dependencies | Reconnaissance | +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1098 | Account Manipulation | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21557,7 +14525,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -21574,12 +14542,12 @@ _version_: 1 This detection identifies use of PowerSploit modules for assessment of presence of defensive tools. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1595.002](https://attack.mitre.org/techniques/T1595.002/), [T1592.002](https://attack.mitre.org/techniques/T1592.002/) - **Last Updated**: 2020-11-05
- View + details #### Search ``` @@ -21598,7 +14566,7 @@ This detection identifies use of PowerSploit modules for assessment of presence #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -21611,12 +14579,12 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1595.002 | x | x | -| T1592.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1595.002 | Vulnerability Scanning | Reconnaissance | +| T1592.002 | Software | Reconnaissance | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21624,7 +14592,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -21641,12 +14609,12 @@ _version_: 1 This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-05
- View + details #### Search ``` @@ -21665,7 +14633,7 @@ This detection identifies use of PowerSploit modules for assessment of privilege #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -21678,13 +14646,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | -| T1078 | x | x | -| T1098 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1098 | Account Manipulation | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21692,7 +14660,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -21709,12 +14677,12 @@ _version_: 1 This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1543](https://attack.mitre.org/techniques/T1543/), [T1055](https://attack.mitre.org/techniques/T1055/), [T1574](https://attack.mitre.org/techniques/T1574/) - **Last Updated**: 2020-11-05
- View + details #### Search ``` @@ -21733,7 +14701,7 @@ This detection identifies use of Mimikatz modules for discovery of process or se #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * _time @@ -21746,13 +14714,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1543 | x | x | -| T1055 | x | x | -| T1574 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1543 | Create or Modify System Process | Persistence, Privilege Escalation | +| T1055 | Process Injection | Defense Evasion, Privilege Escalation | +| T1574 | Hijack Execution Flow | Defense Evasion, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21760,7 +14728,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -21779,12 +14747,12 @@ _version_: 1 The search looks for reg.exe modifying registry keys that define Windows services and their configurations. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1574.011](https://attack.mitre.org/techniques/T1574.011/) - **Last Updated**: 2020-11-26
- View + details #### Search ``` @@ -21805,16 +14773,16 @@ The search looks for reg.exe modifying registry keys that define Windows service #### How To Implement To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1574.011 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1574.011 | Services Registry Permissions Weakness | Defense Evasion, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Installation @@ -21822,7 +14790,7 @@ To successfully implement this search, you must be ingesting data that records r #### Known False Positives It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. -#### References +#### Reference #### Test Dataset @@ -21835,77 +14803,16 @@ _version_: 5 --- -### Reg exe used to hide files directories via registry keys -The search looks for command-line arguments used to hide a file or directory using the reg add command. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1564.001](https://attack.mitre.org/techniques/T1564.001/) -- **Last Updated**: 2019-02-27 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| regex process = "(/d\s+2)" -| `reg_exe_used_to_hide_files_directories_via_registry_keys_filter` -``` -#### Associated Analytic Story - -* Windows Defense Evasion Tactics - -* Suspicious Windows Registry Activities - -* Windows Persistence Techniques - - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1564.001 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -None at the moment - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Registry Keys Used For Persistence The search looks for modifications to registry keys that can be used to launch an application or service at system startup. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1547.001](https://attack.mitre.org/techniques/T1547.001/) - **Last Updated**: 2020-11-27
- View + details #### Search ``` @@ -21936,16 +14843,16 @@ The search looks for modifications to registry keys that can be used to launch a #### How To Implement To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1547.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -21953,7 +14860,7 @@ To successfully implement this search, you must be ingesting data that records r #### Known False Positives There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. -#### References +#### Reference #### Test Dataset @@ -21970,12 +14877,12 @@ _version_: 5 This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1546.012](https://attack.mitre.org/techniques/T1546.012/) - **Last Updated**: 2020-11-27
- View + details #### Search ``` @@ -21998,16 +14905,16 @@ This search looks for modifications to registry keys that can be used to elevate #### How To Implement To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.012 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1546.012 | Image File Execution Options Injection | Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -22015,7 +14922,7 @@ To successfully implement this search, you must be ingesting data that records r #### Known False Positives There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. -#### References +#### Reference * https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/ @@ -22034,12 +14941,12 @@ _version_: 4 This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1546.011](https://attack.mitre.org/techniques/T1546.011/) - **Last Updated**: 2020-11-26
- View + details #### Search ``` @@ -22060,16 +14967,16 @@ This search looks for registry activity associated with application compatibilit #### How To Implement To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response product, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.011 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1546.011 | Application Shimming | Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -22077,7 +14984,7 @@ To successfully implement this search, you must populate the Change_Analysis dat #### Known False Positives There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications -#### References +#### Reference #### Test Dataset @@ -22094,12 +15001,12 @@ _version_: 3 This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic +- **Datamodel**: Network_Traffic - **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021.001/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -22121,16 +15028,16 @@ This search looks for RDP application network traffic and filters any source/des #### How To Implement You must ensure that your network traffic data is populating the Network_Traffic data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1021.001 | Remote Desktop Protocol | Lateral Movement | -#### Kill Chain Phases +#### Kill Chain Phase * Reconnaissance @@ -22140,7 +15047,7 @@ You must ensure that your network traffic data is populating the Network_Traffic #### Known False Positives RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. -#### References +#### Reference #### Test Dataset @@ -22155,12 +15062,12 @@ _version_: 2 This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic +- **Datamodel**: Network_Traffic - **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021.001/) - **Last Updated**: 2020-07-07
- View + details #### Search ``` @@ -22185,16 +15092,16 @@ This search looks for network traffic on TCP/3389, the default port used by remo #### How To Implement To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1021.001 | Remote Desktop Protocol | Lateral Movement | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -22202,7 +15109,7 @@ To successfully implement this search you need to identify systems that commonly #### Known False Positives Remote Desktop may be used legitimately by users on the network. -#### References +#### Reference #### Test Dataset @@ -22213,74 +15120,16 @@ _version_: 3 --- -### Remote Desktop Process Running On System -This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021.001/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name(Processes)` -| `remote_desktop_process_running_on_system_filter` -``` -#### Associated Analytic Story - -* Hidden Cobra Malware - -* Lateral Movement - - -#### How To Implement -To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.001 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Remote Desktop may be used legitimately by users on the network. - -#### References - - -#### Test Dataset - - -_version_: 5 -
- ---- - ### Remote Process Instantiation via WMI This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) - **Last Updated**: 2020-11-30
- View + details #### Search ``` @@ -22301,16 +15150,16 @@ This search looks for wmic.exe being launched with parameters to spawn a process #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1047 | Windows Management Instrumentation | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -22318,7 +15167,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. -#### References +#### Reference #### Test Dataset @@ -22331,131 +15180,16 @@ _version_: 5 --- -### Remote Registry Key modifications -This search monitors for remote modifications to registry keys. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-03-02 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `drop_dm_object_name(Registry)` -| `remote_registry_key_modifications_filter` -``` -#### Associated Analytic Story - -* Windows Defense Evasion Tactics - -* Suspicious Windows Registry Activities - -* Windows Persistence Techniques - - -#### How To Implement -To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - -### Remote WMI Command Attempt -This search looks for wmic.exe being launched with parameters to operate on remote systems. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) -- **Last Updated**: 2018-12-03 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `remote_wmi_command_attempt_filter` -``` -#### Associated Analytic Story - -* Suspicious WMI Use - - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Administrators may use this legitimately to gather info from remote systems. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### RunDLL Loading DLL By Ordinal This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2020-11-30
- View + details #### Search ``` @@ -22474,16 +15208,16 @@ This search looks for executing scripts with rundll32. Adversaries may abuse run #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.011 | Rundll32 | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Installation @@ -22491,7 +15225,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process -#### References +#### Reference #### Test Dataset @@ -22508,12 +15242,12 @@ _version_: 4 The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/) - **Last Updated**: 2020-11-06
- View + details #### Search ``` @@ -22532,16 +15266,16 @@ The search looks for files that contain the key word *Ryuk* under any folder in #### How To Implement You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1486 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1486 | Data Encrypted for Impact | Impact | -#### Kill Chain Phases +#### Kill Chain Phase * Delivery @@ -22549,7 +15283,7 @@ You must be ingesting data that records the filesystem activity from your hosts #### Known False Positives If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs. -#### References +#### Reference #### Test Dataset @@ -22566,12 +15300,12 @@ _version_: 1 This search looks for spikes in the number of Server Message Block (SMB) traffic connections. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic +- **Datamodel**: Network_Traffic - **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/) - **Last Updated**: 2020-07-22
- View + details #### Search ``` @@ -22599,16 +15333,16 @@ This search looks for spikes in the number of Server Message Block (SMB) traffic #### How To Implement This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -22616,7 +15350,7 @@ This search requires you to be ingesting your network traffic logs and populatin #### Known False Positives A file server may experience high-demand loads that could cause this analytic to trigger. -#### References +#### Reference #### Test Dataset @@ -22631,12 +15365,12 @@ _version_: 3 This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic +- **Datamodel**: Network_Traffic - **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/) - **Last Updated**: 2020-07-22
- View + details #### Search ``` @@ -22669,16 +15403,16 @@ This search produces a field (Number of events,count) that are not yet supported 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1021.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -22686,7 +15420,7 @@ Detailed documentation on how to create a new field within Incident Review is fo #### Known False Positives If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results -#### References +#### Reference #### Test Dataset @@ -22697,72 +15431,16 @@ _version_: 3 --- -### SQL Injection with Long URLs -This search looks for long URLs that have several SQL commands visible within them. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Web -- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent -| `drop_dm_object_name("Web")` -| eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 -| where num_sql_cmds > 3 -| `sql_injection_with_long_urls_filter` -``` -#### Associated Analytic Story - -* SQL Injection - - -#### How To Implement -To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1190 | x | x | - -#### Kill Chain Phases - -* Delivery - - -#### Known False Positives -It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Samsam Test File Write The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/) - **Last Updated**: 2018-12-14
- View + details #### Search ``` @@ -22781,16 +15459,16 @@ The search looks for a file named "test.txt" written to the windows system direc #### How To Implement You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1486 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1486 | Data Encrypted for Impact | Impact | -#### Kill Chain Phases +#### Kill Chain Phase * Delivery @@ -22798,7 +15476,7 @@ You must be ingesting data that records the file-system activity from your hosts #### Known False Positives No false positives have been identified. -#### References +#### Reference #### Test Dataset @@ -22815,12 +15493,12 @@ _version_: 1 This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1543.003](https://attack.mitre.org/techniques/T1543.003/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -22849,16 +15527,16 @@ This search looks for arguments to sc.exe indicating the creation or modificatio #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1543.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1543.003 | Windows Service | Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Installation @@ -22866,7 +15544,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. -#### References +#### Reference #### Test Dataset @@ -22883,12 +15561,12 @@ _version_: 4 This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/) - **Last Updated**: 2020-12-17
- View + details #### Search ``` @@ -22909,16 +15587,16 @@ This search looks for flags passed to schtasks.exe on the command-line that indi #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1053.005 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -22926,7 +15604,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives Tasks should not be manually created via CLI, this is rarely done by admins as well -#### References +#### Reference #### Test Dataset @@ -22939,73 +15617,16 @@ _version_: 5 --- -### Scheduled tasks used in BadRabbit ransomware -This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/) -- **Last Updated**: 2020-07-21 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| search (process=*rhaegal* OR process=*drogon* OR *viserion_*) -| `scheduled_tasks_used_in_badrabbit_ransomware_filter` -``` -#### Associated Analytic Story - -* Ransomware - - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1053.005 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -No known false positives - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - ### Schtasks scheduling job on remote system This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/) - **Last Updated**: 2020-07-21
- View + details #### Search ``` @@ -23026,16 +15647,16 @@ This search looks for flags passed to schtasks.exe on the command-line that indi #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1053.005 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -23043,7 +15664,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. -#### References +#### Reference #### Test Dataset @@ -23060,12 +15681,12 @@ _version_: 4 This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/) - **Last Updated**: 2020-12-07
- View + details #### Search ``` @@ -23086,16 +15707,16 @@ This search looks for flags passed to schtasks.exe on the command-line that indi #### How To Implement To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1053.005 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -23103,7 +15724,7 @@ To successfully implement this search you need to be ingesting logs with both th #### Known False Positives Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. -#### References +#### Reference #### Test Dataset @@ -23120,12 +15741,12 @@ _version_: 4 This search looks for scripts launched via WMI. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) - **Last Updated**: 2020-03-16
- View + details #### Search ``` @@ -23144,16 +15765,16 @@ This search looks for scripts launched via WMI. #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1047 | Windows Management Instrumentation | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -23161,7 +15782,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. -#### References +#### Reference #### Test Dataset @@ -23178,12 +15799,12 @@ _version_: 3 This detection identifies illegal setting of credentials via DSInternals modules. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-03
- View + details #### Search ``` @@ -23202,7 +15823,7 @@ This detection identifies illegal setting of credentials via DSInternals modules #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -23221,13 +15842,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | -| T1078 | x | x | -| T1098 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1098 | Account Manipulation | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -23235,7 +15856,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/MichaelGrafnetter/DSInternals @@ -23252,12 +15873,12 @@ _version_: 1 This detection identifies illegal setting of credentials via Mimikatz modules. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-03
- View + details #### Search ``` @@ -23276,7 +15897,7 @@ This detection identifies illegal setting of credentials via Mimikatz modules. #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -23289,13 +15910,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | -| T1078 | x | x | -| T1098 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1098 | Account Manipulation | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -23303,7 +15924,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/gentilkiwi/mimikatz @@ -23320,12 +15941,12 @@ _version_: 1 This detection identifies illegal setting of credentials via PowerSploit modules. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/) - **Last Updated**: 2020-11-03
- View + details #### Search ``` @@ -23344,7 +15965,7 @@ This detection identifies illegal setting of credentials via PowerSploit modules #### How To Implement You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. -#### Required fields +#### Required field * dest_device_id @@ -23357,13 +15978,13 @@ You must be ingesting Windows Security logs from devices of interest, including #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1068 | x | x | -| T1078 | x | x | -| T1098 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1098 | Account Manipulation | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -23371,7 +15992,7 @@ You must be ingesting Windows Security logs from devices of interest, including #### Known False Positives None identified. -#### References +#### Reference * https://github.com/PowerShellMafia/PowerSploit @@ -23388,12 +16009,12 @@ _version_: 1 This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1546.011](https://attack.mitre.org/techniques/T1546.011/) - **Last Updated**: 2020-12-08
- View + details #### Search ``` @@ -23412,16 +16033,16 @@ This search looks for shim database files being written to default directories. #### How To Implement You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.011 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1546.011 | Application Shimming | Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -23429,7 +16050,7 @@ You must be ingesting data that records the filesystem activity from your hosts #### Known False Positives Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. -#### References +#### Reference #### Test Dataset @@ -23446,12 +16067,12 @@ _version_: 3 This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1546.011](https://attack.mitre.org/techniques/T1546.011/) - **Last Updated**: 2020-11-23
- View + details #### Search ``` @@ -23470,16 +16091,16 @@ This search detects the process execution and arguments required to silently cre #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.011 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1546.011 | Application Shimming | Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -23487,7 +16108,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives None identified -#### References +#### Reference #### Test Dataset @@ -23504,12 +16125,12 @@ _version_: 4 This search detects accounts that were created and deleted in a short time period. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Change +- **Datamodel**: Change - **ATT&CK**: [T1136.001](https://attack.mitre.org/techniques/T1136.001/) - **Last Updated**: 2020-07-06
- View + details #### Search ``` @@ -23531,22 +16152,22 @@ This search detects accounts that were created and deleted in a short time perio #### How To Implement This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/ -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1136.001 | Local Account | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. -#### References +#### Reference #### Test Dataset @@ -23567,12 +16188,12 @@ _version_: 2 This search looks for process names that consist only of a single letter. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1204.002](https://attack.mitre.org/techniques/T1204.002/) - **Last Updated**: 2020-12-08
- View + details #### Search ``` @@ -23594,16 +16215,16 @@ This search looks for process names that consist only of a single letter. #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1204.002 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1204.002 | Malicious File | Execution | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -23611,7 +16232,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. -#### References +#### Reference #### Test Dataset @@ -23624,596 +16245,16 @@ _version_: 3 --- -### Spectre and Meltdown Vulnerable Systems -The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Vulnerabilities -- **ATT&CK**: -- **Last Updated**: 2017-01-07 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest -| `drop_dm_object_name(Vulnerabilities)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `spectre_and_meltdown_vulnerable_systems_filter` -``` -#### Associated Analytic Story - -* Spectre And Meltdown Vulnerabilities - - -#### How To Implement -The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - - -#### Known False Positives -It is possible that your vulnerability scanner is not detecting that the patches have been applied. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Spike in File Writes -The search looks for a sharp increase in the number of files written to a particular host - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2020-03-16 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest -| `drop_dm_object_name(Filesystem)` -| eventstats max(_time) as maxtime -| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) -| search isOutlier=1 -| `spike_in_file_writes_filter` -``` -#### Associated Analytic Story - -* SamSam Ransomware - -* Ryuk Ransomware - -* Ransomware - - -#### How To Implement -In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - -### Splunk Enterprise Information Disclosure -This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-06-14 - -
- View - -#### Search -``` -index=_internal sourcetype=splunkd_ui_access server-info -| search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" -| rename clientip as src_ip, splunk_server as dest -| stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `splunk_enterprise_information_disclosure_filter` -``` -#### Associated Analytic Story - -* Splunk Enterprise Vulnerability CVE-2018-11409 - - -#### How To Implement -The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Delivery - - -#### Known False Positives -Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Sunburst Correlation DLL and Network Event -The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/) -- **Last Updated**: 2020-12-14 - -
- View - -#### Search -``` -(`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) -| eventstats dc(EventCode) AS dc_events -| where dc_events=2 -| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host -| rename host as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `sunburst_correlation_dll_and_network_event_filter` -``` -#### Associated Analytic Story - -* Sunburst Malware - - -#### How To Implement -This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1203 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -unknown - -#### References - -* https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Supernova Webshell -This search aims to detect the Supernova webshell used in the SUNBURST attack. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Web -- **ATT&CK**: [T1505.003](https://attack.mitre.org/techniques/T1505.003/) -- **Last Updated**: 2021-01-06 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count from datamodel=Web.Web where web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s -| `supernova_webshell_filter` -``` -#### Associated Analytic Story - -* Sunburst Malware - - -#### How To Implement -To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1505.003 | x | x | - -#### Kill Chain Phases - -* Exfiltration - - -#### Known False Positives -There might be false positives associted with this detection since items like args as a web argument is pretty generic. - -#### References - -* https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html - -* https://www.guidepointsecurity.com/supernova-solarwinds-net-webshell-analysis/ - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Suspicious Changes to File Associations -This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1546.001](https://attack.mitre.org/techniques/T1546.001/) -- **Last Updated**: 2020-07-22 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| join [ -| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest -| `drop_dm_object_name("Registry")` -| table process_id dest registry_path] -| `suspicious_changes_to_file_associations_filter` -``` -#### Associated Analytic Story - -* Suspicious Windows Registry Activities - -* Windows File Extension and Association Abuse - - -#### How To Implement -To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.001 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. - -#### References - - -#### Test Dataset - - -_version_: 4 -
- ---- - -### Suspicious Email - UBA Anomaly -This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: UEBA -- **ATT&CK**: [T1566](https://attack.mitre.org/techniques/T1566/) -- **Last Updated**: 2020-07-22 - -
- View - -#### Search -``` - -|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model -| `drop_dm_object_name(All_UEBA_Events)` -| `drop_dm_object_name(UEBA_Anomalies)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `suspicious_email___uba_anomaly_filter` -``` -#### Associated Analytic Story - -* Suspicious Emails - - -#### How To Implement -You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1566 | x | x | - -#### Kill Chain Phases - -* Delivery - - -#### Known False Positives -This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and add the URL to an allow list if you determine that it is a legitimate sender. - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - -### Suspicious Email Attachment Extensions -This search looks for emails that have attachments with suspicious file extensions. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Email -- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/) -- **Last Updated**: 2020-07-22 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name("All_Email")` -| `suspicious_email_attachments` -| `suspicious_email_attachment_extensions_filter` -``` -#### Associated Analytic Story - -* Emotet Malware DHS Report TA18-201A - -* Suspicious Emails - - -#### How To Implement -You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ - **Splunk Phantom Playbook Integration**\ -If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1566.001 | x | x | - -#### Kill Chain Phases - -* Delivery - - -#### Known False Positives -None identified - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - -### Suspicious File Write -The search looks for files created with names that have been linked to malicious activity. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2019-04-25 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `drop_dm_object_name(Filesystem)` -| `suspicious_writes` -| `suspicious_file_write_filter` -``` -#### Associated Analytic Story - -* Hidden Cobra Malware - - -#### How To Implement -You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. - -#### References - - -#### Test Dataset - - -_version_: 3 -
- ---- - -### Suspicious Java Classes -This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-12-06 - -
- View - -#### Search -``` -`stream_http` http_method=POST http_content_length>1 -| regex form_data="(?i)java\.lang\.(?:runtime -|processbuilder)" -| rename src_ip as src -| stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `suspicious_java_classes_filter` -``` -#### Associated Analytic Story - -* Apache Struts Vulnerability - - -#### How To Implement -In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Exploitation - - -#### Known False Positives -There are no known false positives. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Suspicious MSBuild Rename The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1127.001](https://attack.mitre.org/techniques/T1127.001/), [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2021-01-12
- View + details #### Search ``` @@ -24232,17 +16273,17 @@ The following analytic identifies renamed instances of msbuild.exe executing. Ms #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1127.001 | x | x | -| T1036.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1127.001 | MSBuild | Defense Evasion | +| T1036.003 | Rename System Utilities | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -24250,7 +16291,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive. -#### References +#### Reference * https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ @@ -24273,12 +16314,12 @@ _version_: 1 The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1127.001](https://attack.mitre.org/techniques/T1127.001/) - **Last Updated**: 2021-01-12
- View + details #### Search ``` @@ -24297,16 +16338,16 @@ The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavi #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1127.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1127.001 | MSBuild | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -24314,7 +16355,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. -#### References +#### Reference * https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ @@ -24335,12 +16376,12 @@ _version_: 1 This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1112](https://attack.mitre.org/techniques/T1112/) - **Last Updated**: 2020-07-22
- View + details #### Search ``` @@ -24371,16 +16412,16 @@ This search looks for reg.exe being launched from a command prompt not started b #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1112 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1112 | Modify Registry | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -24388,7 +16429,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. -#### References +#### Reference * https://car.mitre.org/wiki/CAR-2013-03-001 @@ -24407,12 +16448,12 @@ _version_: 4 Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.010](https://attack.mitre.org/techniques/T1218.010/) - **Last Updated**: 2021-01-28
- View + details #### Search ``` @@ -24431,16 +16472,16 @@ Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.010 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.010 | Regsvr32 | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -24448,7 +16489,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/010/ @@ -24475,12 +16516,12 @@ _version_: 1 The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/), [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2021-02-04
- View + details #### Search ``` @@ -24499,17 +16540,17 @@ The following analytic identifies renamed instances of rundll32.exe executing. r #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | -| T1036.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.011 | Rundll32 | Defense Evasion | +| T1036.003 | Rename System Utilities | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -24517,7 +16558,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/011/ @@ -24540,12 +16581,12 @@ _version_: 1 The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-04
- View + details #### Search ``` @@ -24566,16 +16607,16 @@ The following analytic identifies rundll32.exe executing a DLL function name, St #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.011 | Rundll32 | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -24583,7 +16624,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/011/ @@ -24610,12 +16651,12 @@ _version_: 1 The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-09
- View + details #### Search ``` @@ -24634,16 +16675,16 @@ The following analytic identifies rundll32.exe using dllregisterserver on the co #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.011 | Rundll32 | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -24651,7 +16692,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/011/ @@ -24682,12 +16723,12 @@ _version_: 1 The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-09
- View + details #### Search ``` @@ -24709,16 +16750,16 @@ The following analytic identifies rundll32.exe with no command line arguments. I #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.011 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.011 | Rundll32 | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -24726,7 +16767,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. -#### References +#### Reference * https://attack.mitre.org/techniques/T1218/011/ @@ -24751,12 +16792,12 @@ _version_: 1 The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1127, T1036.003](https://attack.mitre.org/techniques/T1127, T1036.003/) - **Last Updated**: 2021-01-12
- View + details #### Search ``` @@ -24775,16 +16816,16 @@ The following analytic identifies a renamed instance of microsoft.workflow.compi #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1127, T1036.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| | | | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -24792,7 +16833,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive. -#### References +#### Reference * https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/ @@ -24813,12 +16854,12 @@ _version_: 1 The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1127](https://attack.mitre.org/techniques/T1127/) - **Last Updated**: 2021-01-12
- View + details #### Search ``` @@ -24837,16 +16878,16 @@ The following analytic identifies microsoft.workflow.compiler.exe usage. microso #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1127 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1127 | Trusted Developer Utilities Proxy Execution | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -24854,7 +16895,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM. -#### References +#### Reference * https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ @@ -24875,12 +16916,12 @@ _version_: 1 The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1127.001](https://attack.mitre.org/techniques/T1127.001/), [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2021-01-12
- View + details #### Search ``` @@ -24899,17 +16940,17 @@ The following analytic identifies msbuild.exe executing from a non-standard path #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1127.001 | x | x | -| T1036.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1127.001 | MSBuild | Defense Evasion | +| T1036.003 | Rename System Utilities | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -24917,7 +16958,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on. -#### References +#### Reference * https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ @@ -24938,12 +16979,12 @@ _version_: 1 The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-12
- View + details #### Search ``` @@ -24962,16 +17003,16 @@ The following analytic identifies child processes spawning from "mshta.exe". Th #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.005 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.005 | Mshta | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -24979,7 +17020,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. -#### References +#### Reference * https://github.com/redcanaryco/AtomicTestHarnesses @@ -25000,12 +17041,12 @@ _version_: 1 The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218.005/) - **Last Updated**: 2021-01-20
- View + details #### Search ``` @@ -25024,16 +17065,16 @@ The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1218.005 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1218.005 | Mshta | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -25041,7 +17082,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. -#### References +#### Reference * https://codewhitesec.blogspot.com/2018/07/lethalhta.html @@ -25064,12 +17105,12 @@ _version_: 1 The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1070.001](https://attack.mitre.org/techniques/T1070.001/) - **Last Updated**: 2020-07-22
- View + details #### Search ``` @@ -25090,16 +17131,16 @@ The wevtutil.exe application is the windows event log utility. This searches for #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1070.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1070.001 | Clear Windows Event Logs | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -25107,7 +17148,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. -#### References +#### Reference #### Test Dataset @@ -25120,69 +17161,16 @@ _version_: 3 --- -### Suspicious writes to System Volume Information -This search detects writes to the 'System Volume Information' folder by something other than the System process. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1036](https://attack.mitre.org/techniques/T1036/) -- **Last Updated**: 2020-07-22 - -
- View - -#### Search -``` -(`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* -| stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `suspicious_writes_to_system_volume_information_filter` -``` -#### Associated Analytic Story - -* Collection and Staging - - -#### How To Implement -You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1036 | x | x | - -#### Kill Chain Phases - - -#### Known False Positives -It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. - -#### References - - -#### Test Dataset - - -_version_: 2 -
- ---- - ### Suspicious writes to windows Recycle Bin This search detects writes to the recycle bin by a process other than explorer.exe. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1036](https://attack.mitre.org/techniques/T1036/) - **Last Updated**: 2020-07-22
- View + details #### Search ``` @@ -25203,22 +17191,22 @@ This search detects writes to the recycle bin by a process other than explorer.e #### How To Implement To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1036 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1036 | Masquerading | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. -#### References +#### Reference #### Test Dataset @@ -25235,12 +17223,12 @@ _version_: 4 Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/) - **Last Updated**: 2020-10-12
- View + details #### Search ``` @@ -25262,16 +17250,16 @@ Detect system information discovery techniques used by attackers to understand c #### How To Implement To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1082 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1082 | System Information Discovery | Discovery | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -25279,7 +17267,7 @@ To successfully implement this search you need to be ingesting information on pr #### Known False Positives Administrators debugging servers -#### References +#### Reference * https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation @@ -25298,12 +17286,12 @@ _version_: 1 An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1036](https://attack.mitre.org/techniques/T1036/) - **Last Updated**: 2020-08-25
- View + details #### Search ``` @@ -25345,7 +17333,7 @@ $cond_6 = #### How To Implement Collect endpoint data such as sysmon or 4688 events. -#### Required fields +#### Required field * dest_device_id @@ -25360,11 +17348,11 @@ Collect endpoint data such as sysmon or 4688 events. #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1036 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1036 | Masquerading | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -25372,7 +17360,7 @@ Collect endpoint data such as sysmon or 4688 events. #### Known False Positives None -#### References +#### Reference #### Test Dataset @@ -25387,12 +17375,12 @@ _version_: 1 This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2020-12-08
- View + details #### Search ``` @@ -25416,16 +17404,16 @@ This search looks for system processes that normally run out of C:\Windows\Syste #### How To Implement To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1036.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1036.003 | Rename System Utilities | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -25433,7 +17421,7 @@ To successfully implement this search you need to ingest details about process e #### Known False Positives None identified -#### References +#### Reference #### Test Dataset @@ -25450,12 +17438,12 @@ _version_: 5 This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Network_Traffic +- **Datamodel**: Network_Traffic - **ATT&CK**: [T1071.001](https://attack.mitre.org/techniques/T1071.001/) - **Last Updated**: 2020-07-22
- View + details #### Search ``` @@ -25480,16 +17468,16 @@ This search looks for network traffic identified as The Onion Router (TOR), a be #### How To Implement In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1071.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1071.001 | Web Protocols | Command and Control | -#### Kill Chain Phases +#### Kill Chain Phase * Command and Control @@ -25497,7 +17485,7 @@ In order to properly run this search, Splunk needs to ingest data from firewalls #### Known False Positives None at this time -#### References +#### Reference #### Test Dataset @@ -25512,12 +17500,12 @@ _version_: 2 The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1070](https://attack.mitre.org/techniques/T1070/) - **Last Updated**: 2018-12-03
- View + details #### Search ``` @@ -25539,16 +17527,16 @@ The fsutil.exe application is a legitimate Windows utility used to perform tasks #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1070 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1070 | Indicator Removal on Host | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -25556,7 +17544,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives None identified -#### References +#### Reference #### Test Dataset @@ -25569,77 +17557,16 @@ _version_: 2 --- -### Uncommon Processes On Endpoint -This search looks for applications on the endpoint that you have marked as uncommon. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1204.002](https://attack.mitre.org/techniques/T1204.002/) -- **Last Updated**: 2020-07-22 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name(Processes)` -| `uncommon_processes` -|`uncommon_processes_on_endpoint_filter` -``` -#### Associated Analytic Story - -* Windows Privilege Escalation - -* Unusual Processes - -* Cloud Federated Credential Abuse - - -#### How To Implement -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1204.002 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -None identified - -#### References - - -#### Test Dataset - - -_version_: 4 -
- ---- - ### Unload Sysmon Filter Driver Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562.001/) - **Last Updated**: 2020-07-22
- View + details #### Search ``` @@ -25659,16 +17586,16 @@ Attackers often disable security tools to avoid detection. This search looks for #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1562.001 | Disable or Modify Tools | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -25676,7 +17603,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives -#### References +#### Reference #### Test Dataset @@ -25689,128 +17616,16 @@ _version_: 3 --- -### Unsigned Image Loaded by LSASS -This search detects loading of unsigned images by LSASS. Deprecated because too noisy. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) -- **Last Updated**: 2019-12-06 - -
- View - -#### Search -``` -`sysmon` EventID=7 Image=*lsass.exe Signed=false -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `unsigned_image_loaded_by_lsass_filter` -``` -#### Associated Analytic Story - -* Credential Dumping - - -#### How To Implement -This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1003.001 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. - -#### References - -* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Unsuccessful Netbackup backups -This search gives you the hosts where a backup was attempted and then failed. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2017-09-12 - -
- View - -#### Search -``` -`netbackup` -| stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE -| search MESSAGE="An error occurred, failed to backup." -| `security_content_ctime(latestTime)` -| rename COMPUTERNAME as dest, MESSAGE as signature -| table latestTime, dest, signature -| `unsuccessful_netbackup_backups_filter` -``` -#### Associated Analytic Story - -* Monitor Backup Solution - - -#### How To Implement -To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - - -#### Known False Positives -None identified - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Unusually Long Command Line Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez. - **Product**: UEBA for Security Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: - **Last Updated**: 2020-10-06
- View + details #### Search ``` @@ -25835,7 +17650,7 @@ Command lines that are extremely long may be indicative of malicious activity on #### How To Implement You must be ingesting sysmon endpoint data that monitors command lines. -#### Required fields +#### Required field * process_name @@ -25850,10 +17665,10 @@ You must be ingesting sysmon endpoint data that monitors command lines. #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -25861,7 +17676,7 @@ You must be ingesting sysmon endpoint data that monitors command lines. #### Known False Positives This detection may flag suspiciously long command lines when there is not sufficient evidence (samples) for a given process that this detection is tracking; or when there is high variability in the length of the command line for the tracked process. Also, some legitimate applications may use long command lines. Such is the case of Ansible, that encodes Powershell scripts using long base64. Attackers may use this technique to obfuscate their payloads. -#### References +#### Reference #### Test Dataset @@ -25876,12 +17691,12 @@ _version_: 1 Command lines that are extremely long may be indicative of malicious activity on your hosts. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: - **Last Updated**: 2020-12-08
- View + details #### Search ``` @@ -25911,15 +17726,15 @@ Command lines that are extremely long may be indicative of malicious activity on #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the process field in the Endpoint data model. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -25927,7 +17742,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives Some legitimate applications start with long command lines. -#### References +#### Reference #### Test Dataset @@ -25944,12 +17759,12 @@ _version_: 5 Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: - **Last Updated**: 2019-05-08
- View + details #### Search ``` @@ -25980,15 +17795,15 @@ Command lines that are extremely long may be indicative of malicious activity on #### How To Implement You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -25996,7 +17811,7 @@ You must be ingesting endpoint data that monitors command lines and populates th #### Known False Positives Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. -#### References +#### Reference #### Test Dataset @@ -26011,12 +17826,12 @@ _version_: 1 This search looks for unusually long strings in the Content-Type http header that the client sends the server. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: - **Last Updated**: 2017-10-13
- View + details #### Search ``` @@ -26034,15 +17849,15 @@ This search looks for unusually long strings in the Content-Type http header tha #### How To Implement This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase * Delivery @@ -26050,7 +17865,7 @@ This particular search leverages data extracted from Stream:HTTP. You must confi #### Known False Positives Very few legitimate Content-Type fields will have a length greater than 100 characters. -#### References +#### Reference #### Test Dataset @@ -26065,12 +17880,12 @@ _version_: 1 This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/) - **Last Updated**: 2021-01-22
- View + details #### Search ``` @@ -26091,16 +17906,16 @@ This search looks for flags passed to wbadmin.exe (Windows Backup Administrator #### How To Implement You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1490 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1490 | Inhibit System Recovery | Impact | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -26108,7 +17923,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Known False Positives Administrators may modify the boot configuration. -#### References +#### Reference * https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md @@ -26124,65 +17939,6 @@ Administrators may modify the boot configuration. * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log -_version_: 1 -
- ---- - -### WMI Permanent Event Subscription -This search looks for the creation of WMI permanent event subscriptions. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) -- **Last Updated**: 2018-10-23 - -
- View - -#### Search -``` -`wmi` EventCode=5861 Binding -| rex field=Message "Consumer =\s+(?[^; -|^$]+)" -| search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" -| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| rename ComputerName as dest -| `wmi_permanent_event_subscription_filter` -``` -#### Associated Analytic Story - -* Suspicious WMI Use - - -#### How To Implement -To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Although unlikely, administrators may use event subscriptions for legitimate purposes. - -#### References - - -#### Test Dataset - - _version_: 1
@@ -26192,12 +17948,12 @@ _version_: 1 This search looks for the creation of WMI permanent event subscriptions. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1546.003](https://attack.mitre.org/techniques/T1546.003/) - **Last Updated**: 2020-12-08
- View + details #### Search ``` @@ -26214,16 +17970,16 @@ This search looks for the creation of WMI permanent event subscriptions. #### How To Implement To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1546.003 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1546.003 | Windows Management Instrumentation Event Subscription | Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -26231,7 +17987,7 @@ To successfully implement this search, you must be collecting Sysmon data using #### Known False Positives Although unlikely, administrators may use event subscriptions for legitimate purposes. -#### References +#### Reference #### Test Dataset @@ -26244,74 +18000,16 @@ _version_: 2 --- -### WMI Temporary Event Subscription -This search looks for the creation of WMI temporary event subscriptions. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) -- **Last Updated**: 2018-10-23 - -
- View - -#### Search -``` -`wmi` EventCode=5860 Temporary -| rex field=Message "NotificationQuery =\s+(?[^; -|^$]+)" -| search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" -| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `wmi_temporary_event_subscription_filter` -``` -#### Associated Analytic Story - -* Suspicious WMI Use - - -#### How To Implement -To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1047 | x | x | - -#### Kill Chain Phases - -* Actions on Objectives - - -#### Known False Positives -Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - ### Web Fraud - Account Harvesting This search is used to identify the creation of multiple user accounts using the same email domain name. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1136](https://attack.mitre.org/techniques/T1136/) - **Last Updated**: 2018-10-08
- View + details #### Search ``` @@ -26333,16 +18031,16 @@ This search is used to identify the creation of multiple user accounts using the #### How To Implement We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1136 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1136 | Create Account | Persistence | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -26350,7 +18048,7 @@ We start with a dataset that provides visibility into the email address used for #### Known False Positives As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. -#### References +#### Reference * https://splunkbase.splunk.com/app/2734/ @@ -26369,12 +18067,12 @@ _version_: 1 This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2018-10-08
- View + details #### Search ``` @@ -26394,16 +18092,16 @@ This search is used to examine web sessions to identify those where the clicks a #### How To Implement Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -26411,7 +18109,7 @@ Start with a dataset that allows you to see clickstream data for each user click #### Known False Positives As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior. -#### References +#### Reference * https://en.wikipedia.org/wiki/Session_ID @@ -26434,12 +18132,12 @@ _version_: 1 This search is used to identify user accounts that share a common password. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: - **Last Updated**: 2018-10-08
- View + details #### Search ``` @@ -26460,21 +18158,21 @@ This search is used to identify user accounts that share a common password. #### How To Implement We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| -#### Kill Chain Phases +#### Kill Chain Phase #### Known False Positives As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. -#### References +#### Reference * https://en.wikipedia.org/wiki/Session_ID @@ -26497,12 +18195,12 @@ _version_: 1 This search looks for suspicious processes on all systems labeled as web servers. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/) - **Last Updated**: 2019-04-01
- View + details #### Search ``` @@ -26521,16 +18219,16 @@ This search looks for suspicious processes on all systems labeled as web servers #### How To Implement You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1082 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1082 | System Information Discovery | Discovery | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -26538,7 +18236,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Known False Positives Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. -#### References +#### Reference #### Test Dataset @@ -26553,12 +18251,12 @@ _version_: 1 This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint +- **Datamodel**: Endpoint - **ATT&CK**: [T1018](https://attack.mitre.org/techniques/T1018/) - **Last Updated**: 2020-12-16
- View + details #### Search ``` @@ -26577,16 +18275,16 @@ This search looks for the execution of `adfind.exe` with command-line arguments #### How To Implement To successfully implement this search, you need to be ingesting logs with the process name, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1018 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1018 | Remote System Discovery | Discovery | -#### Kill Chain Phases +#### Kill Chain Phase * Exploitation @@ -26594,7 +18292,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Known False Positives administrators rarely use adfind, usually not used for legitimate reasons -#### References +#### Reference * https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/ @@ -26606,62 +18304,6 @@ administrators rarely use adfind, usually not used for legitimate reasons * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log -_version_: 1 -
- ---- - -### Windows DisableAntiSpyware Registry -The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: Endpoint -- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562.001/) -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name -| `drop_dm_object_name(Registry)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `windows_disableantispyware_registry_filter` -``` -#### Associated Analytic Story - -* Ryuk Ransomware - - -#### How To Implement -You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1562.001 | x | x | - -#### Kill Chain Phases - -* Delivery - - -#### Known False Positives -It is unusual to turn this feature on a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. - -#### References - - -#### Test Dataset - - _version_: 1
@@ -26671,12 +18313,12 @@ _version_: 1 This search looks for Windows events that indicate one of the Windows event logs has been purged. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1070.001](https://attack.mitre.org/techniques/T1070.001/) - **Last Updated**: 2020-07-06
- View + details #### Search ``` @@ -26696,16 +18338,16 @@ This search looks for Windows events that indicate one of the Windows event logs #### How To Implement To successfully implement this search, you need to be ingesting Windows event logs from your hosts. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1070.001 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1070.001 | Clear Windows Event Logs | Defense Evasion | -#### Kill Chain Phases +#### Kill Chain Phase * Actions on Objectives @@ -26713,7 +18355,7 @@ To successfully implement this search, you need to be ingesting Windows event lo #### Known False Positives It is possible that these logs may be legitimately cleared by Administrators. -#### References +#### Reference #### Test Dataset @@ -26732,12 +18374,12 @@ _version_: 4 The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints. - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: +- **Datamodel**: - **ATT&CK**: [T1489](https://attack.mitre.org/techniques/T1489/) - **Last Updated**: 2020-11-06
- View + details #### Search ``` @@ -26756,16 +18398,16 @@ The search looks for a Windows Security Account Manager (SAM) was stopped via co #### How To Implement You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -#### Required fields +#### Required field #### ATT&CK -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1489 | x | x | +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1489 | Service Stop | Impact | -#### Kill Chain Phases +#### Kill Chain Phase * Delivery @@ -26773,7 +18415,7 @@ You must be ingesting data that records the process-system activity from your ho #### Known False Positives SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified. -#### References +#### Reference #### Test Dataset @@ -26781,445 +18423,6 @@ SAM is a critical windows service, stopping it would cause major issues on an en * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log -_version_: 1 -
- ---- - -### Windows connhost exe started forcefully -The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059.003/) -- **Last Updated**: 2020-11-06 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `windows_connhost_exe_started_forcefully_filter` -``` -#### Associated Analytic Story - -* Ryuk Ransomware - - -#### How To Implement -You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1059.003 | x | x | - -#### Kill Chain Phases - -* Delivery - - -#### Known False Positives -This process should not be ran forcefully, we have not see any false positives for this detection - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### Windows hosts file modification -The search looks for modifications to the hosts file on all Windows endpoints across your environment. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: -- **Last Updated**: 2018-11-02 - -
- View - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* -| `drop_dm_object_name(Filesystem)` -| `windows_hosts_file_modification_filter` -``` -#### Associated Analytic Story - -* Host Redirection - - -#### How To Implement -To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| - -#### Kill Chain Phases - -* Command and Control - - -#### Known False Positives -There may be legitimate reasons for system administrators to add entries to this file. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### aws detect attach to role policy -This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) -- **Last Updated**: 2020-07-27 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` attach policy -| spath requestParameters.policyArn -| table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate -| `aws_detect_attach_to_role_policy_filter` -``` -#### Associated Analytic Story - -* AWS Cross Account Activity - - -#### How To Implement -You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### aws detect permanent key creation -This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) -- **Last Updated**: 2020-07-27 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` CreateAccessKey -| spath eventName -| search eventName=CreateAccessKey "userIdentity.type"=IAMUser -| table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId -|`aws_detect_permanent_key_creation_filter` -``` -#### Associated Analytic Story - -* AWS Cross Account Activity - - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### aws detect role creation -This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) -- **Last Updated**: 2020-07-27 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* -| table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate -| `aws_detect_role_creation_filter` -``` -#### Associated Analytic Story - -* AWS Cross Account Activity - - -#### How To Implement -You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### aws detect sts assume role abuse -This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) -- **Last Updated**: 2020-07-27 - -
- View - -#### Search -``` -`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role -| table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate -| `aws_detect_sts_assume_role_abuse_filter` -``` -#### Associated Analytic Story - -* AWS Cross Account Activity - - -#### How To Implement -You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### aws detect sts get session token abuse -This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1550](https://attack.mitre.org/techniques/T1550/) -- **Last Updated**: 2020-07-27 - -
- View - -#### Search -``` -`aws_cloudwatchlogs_eks` ASIA userIdentity.type=IAMUser -| spath eventName -| search eventName=GetSessionToken -| table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region -| `aws_detect_sts_get_session_token_abuse_filter` -``` -#### Associated Analytic Story - -* AWS Cross Account Activity - - -#### How To Implement -You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1550 | x | x | - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used. - -#### References - - -#### Test Dataset - - -_version_: 1 -
- ---- - -### gcp detect oauth token abuse -This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Data Models**: -- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) -- **Last Updated**: 2020-09-01 - -
- View - -#### Search -``` -`google_gcp_pubsub_message` type.googleapis.com/google.cloud.audit.AuditLog -|table protoPayload.@type protoPayload.status.details{}.@type protoPayload.status.details{}.violations{}.callerIp protoPayload.status.details{}.violations{}.type protoPayload.status.message -| `gcp_detect_oauth_token_abuse_filter` -``` -#### Associated Analytic Story - -* GCP Cross Account Activity - - -#### How To Implement -You must install splunk GCP add-on. This search works with gcp:pubsub:message logs - -#### Required fields - - -#### ATT&CK - -| ID | technique | Tactic | -| ----------- | ----------- |:-------------:| -| T1078 | x | x | - -#### Kill Chain Phases - -* Lateral Movement - - -#### Known False Positives -GCP Oauth token abuse detection will only work if there are access policies in place along with audit logs. - -#### References - -* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-1 - -* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-2 - - -#### Test Dataset - - _version_: 1
diff --git a/docs/detections.wiki b/docs/detections.wiki new file mode 100644 index 0000000000..5c0d7d2647 --- /dev/null +++ b/docs/detections.wiki @@ -0,0 +1,17553 @@ +=Splunk Security Content Detections = + +---- +All the detections shipped to different Splunk products. Below is a breakdown by kind. +==Cloud== + + +* [[#AWS Cross Account Activity From Previously Unseen Account|AWS Cross Account Activity From Previously Unseen Account]] + + + +* [[#AWS Detect Users creating keys with encrypt policy without MFA|AWS Detect Users creating keys with encrypt policy without MFA]] + + + +* [[#AWS Detect Users with KMS keys performing encryption S3|AWS Detect Users with KMS keys performing encryption S3]] + + + +* [[#AWS Network Access Control List Created with All Open Ports|AWS Network Access Control List Created with All Open Ports]] + + + +* [[#AWS Network Access Control List Deleted|AWS Network Access Control List Deleted]] + + + +* [[#AWS SAML Access by Provider User and Principal|AWS SAML Access by Provider User and Principal]] + + + +* [[#AWS SAML Update identity provider|AWS SAML Update identity provider]] + + + +* [[#Abnormally High Number Of Cloud Infrastructure API Calls|Abnormally High Number Of Cloud Infrastructure API Calls]] + + + +* [[#Abnormally High Number Of Cloud Instances Destroyed|Abnormally High Number Of Cloud Instances Destroyed]] + + + +* [[#Abnormally High Number Of Cloud Instances Launched|Abnormally High Number Of Cloud Instances Launched]] + + + +* [[#Abnormally High Number Of Cloud Security Group API Calls|Abnormally High Number Of Cloud Security Group API Calls]] + + + + + + + + + + + + + + + + + + + + + + + + + + + +* [[#Cloud API Calls From Previously Unseen User Roles|Cloud API Calls From Previously Unseen User Roles]] + + + +* [[#Cloud Compute Instance Created By Previously Unseen User|Cloud Compute Instance Created By Previously Unseen User]] + + + +* [[#Cloud Compute Instance Created In Previously Unused Region|Cloud Compute Instance Created In Previously Unused Region]] + + + +* [[#Cloud Compute Instance Created With Previously Unseen Image|Cloud Compute Instance Created With Previously Unseen Image]] + + + +* [[#Cloud Compute Instance Created With Previously Unseen Instance Type|Cloud Compute Instance Created With Previously Unseen Instance Type]] + + + +* [[#Cloud Instance Modified By Previously Unseen User|Cloud Instance Modified By Previously Unseen User]] + + + +* [[#Cloud Provisioning Activity From Previously Unseen City|Cloud Provisioning Activity From Previously Unseen City]] + + + +* [[#Cloud Provisioning Activity From Previously Unseen Country|Cloud Provisioning Activity From Previously Unseen Country]] + + + +* [[#Cloud Provisioning Activity From Previously Unseen IP Address|Cloud Provisioning Activity From Previously Unseen IP Address]] + + + +* [[#Cloud Provisioning Activity From Previously Unseen Region|Cloud Provisioning Activity From Previously Unseen Region]] + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +* [[#Detect AWS Console Login by New User|Detect AWS Console Login by New User]] + + + +* [[#Detect AWS Console Login by User from New City|Detect AWS Console Login by User from New City]] + + + +* [[#Detect AWS Console Login by User from New Country|Detect AWS Console Login by User from New Country]] + + + +* [[#Detect AWS Console Login by User from New Region|Detect AWS Console Login by User from New Region]] + + + + + + + + + + + + + + + +* [[#Detect GCP Storage access from a new IP|Detect GCP Storage access from a new IP]] + + + + + + + + + + + + + + + + + + + + + +* [[#Detect New Open GCP Storage Buckets|Detect New Open GCP Storage Buckets]] + + + +* [[#Detect New Open S3 Buckets over AWS CLI|Detect New Open S3 Buckets over AWS CLI]] + + + +* [[#Detect New Open S3 buckets|Detect New Open S3 buckets]] + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +* [[#Detect S3 access from a new IP|Detect S3 access from a new IP]] + + + + + + + +* [[#Detect Spike in AWS Security Hub Alerts for EC2 Instance|Detect Spike in AWS Security Hub Alerts for EC2 Instance]] + + + +* [[#Detect Spike in AWS Security Hub Alerts for User|Detect Spike in AWS Security Hub Alerts for User]] + + + +* [[#Detect Spike in S3 Bucket deletion|Detect Spike in S3 Bucket deletion]] + + + +* [[#Detect Spike in blocked Outbound Traffic from your AWS|Detect Spike in blocked Outbound Traffic from your AWS]] + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +* [[#High Number of Login Failures from a single source|High Number of Login Failures from a single source]] + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +* [[#New container uploaded to AWS ECR|New container uploaded to AWS ECR]] + + + + + +* [[#O365 Add App Role Assignment Grant User|O365 Add App Role Assignment Grant User]] + + + +* [[#O365 Added Service Principal|O365 Added Service Principal]] + + + +* [[#O365 Bypass MFA via Trusted IP|O365 Bypass MFA via Trusted IP]] + + + +* [[#O365 Disable MFA|O365 Disable MFA]] + + + +* [[#O365 Excessive Authentication Failures Alert|O365 Excessive Authentication Failures Alert]] + + + +* [[#O365 Excessive SSO logon errors|O365 Excessive SSO logon errors]] + + + +* [[#O365 New Federated Domain Added|O365 New Federated Domain Added]] + + + +* [[#O365 PST export alert|O365 PST export alert]] + + + +* [[#O365 Suspicious Admin Email Forwarding|O365 Suspicious Admin Email Forwarding]] + + + +* [[#O365 Suspicious Rights Delegation|O365 Suspicious Rights Delegation]] + + + +* [[#O365 Suspicious User Email Forwarding|O365 Suspicious User Email Forwarding]] + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +==={{visible anchor|AWS Cross Account Activity From Previously Unseen Account|aws-cross-account-activity-from-previously-unseen-account}}=== +This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. This search is deprecated and have been translated to use the latest Authentication Datamodel. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': +* '''Last Updated''': 2020-05-28 + +
+
+ +====Search==== + +| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role +| `drop_dm_object_name(Authentication)` +| rex field=user_role "arn:aws:sts:*:(?.*):" +| where vendor_account != dest_account +| rename vendor_account as requestingAccountId dest_account as requestedAccountId +| lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime +| eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") +| where status = "New Cross Account Activity" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `aws_cross_account_activity_from_previously_unseen_account_filter` + +====Associated Analytic Story==== + +* Suspicious Cloud Authentication Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen AWS Cross Account Activity - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen AWS Cross Account Activity - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `aws_cross_account_activity_from_previously_unseen_account_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|AWS Detect Users creating keys with encrypt policy without MFA|aws-detect-users-creating-keys-with-encrypt-policy-without-mfa}}=== +This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1486/ T1486] +* '''Last Updated''': 2021-01-11 + +
+
+ +====Search==== +`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy +| spath input=requestParameters.policy output=key_policy_statements path=Statement{} +| mvexpand key_policy_statements +| spath input=key_policy_statements output=key_policy_action_1 path=Action +| spath input=key_policy_statements output=key_policy_action_2 path=Action{} +| eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) +| spath input=key_policy_statements output=key_policy_principal path=Principal.AWS +| search key_policy_action="kms:Encrypt" AND key_policy_principal="*" +| stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` + +====Associated Analytic Story==== + +* Ransomware Cloud + + +====How To Implement==== +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1486 +| Data Encrypted for Impact +| Impact +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +unknown + +====Reference==== + +* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ + +* https://github.com/d1vious/git-wild-hunt + +* https://www.youtube.com/watch?v=PgzNib37g0M + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|AWS Detect Users with KMS keys performing encryption S3|aws-detect-users-with-kms-keys-performing-encryption-s3}}=== +This search provides detection of users with KMS keys performing encryption specifically against S3 buckets. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1486/ T1486] +* '''Last Updated''': 2021-01-11 + +
+
+ +====Search==== +`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" +| rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file +| stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|`aws_detect_users_with_kms_keys_performing_encryption_s3_filter` + +====Associated Analytic Story==== + +* Ransomware Cloud + + +====How To Implement==== +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1486 +| Data Encrypted for Impact +| Impact +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +bucket with S3 encryption + +====Reference==== + +* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ + +* https://github.com/d1vious/git-wild-hunt + +* https://www.youtube.com/watch?v=PgzNib37g0M + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|AWS Network Access Control List Created with All Open Ports|aws-network-access-control-list-created-with-all-open-ports}}=== +The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] +* '''Last Updated''': 2021-01-11 + +
+
+ +====Search==== +`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 +| append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 +| eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' +| where port_range>1024] +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `aws_network_access_control_list_created_with_all_open_ports_filter` + +====Associated Analytic Story==== + +* AWS Network ACL Activity + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/aws_cloudtrail_events.json + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|AWS Network Access Control List Deleted|aws-network-access-control-list-deleted}}=== +Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== +`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `aws_network_access_control_list_deleted_filter` + +====Associated Analytic Story==== + +* AWS Network ACL Activity + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It's possible that a user has legitimately deleted a network ACL. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/aws_cloudtrail_events.json + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|AWS SAML Access by Provider User and Principal|aws-saml-access-by-provider-user-and-principal}}=== +This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`cloudtrail` eventName=Assumerolewithsaml +| stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|`aws_saml_access_by_provider_user_and_principal_filter` + +====Associated Analytic Story==== + +* Cloud Federated Credential Abuse + + +====How To Implement==== +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks. + +====Reference==== + +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + +* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/assume_role_with_saml/assume_role_with_saml.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|AWS SAML Update identity provider|aws-saml-update-identity-provider}}=== +This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`cloudtrail` eventName=UpdateSAMLProvider +| stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|`aws_saml_update_identity_provider_filter` + +====Associated Analytic Story==== + +* Cloud Federated Credential Abuse + + +====How To Implement==== +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored. + +====Reference==== + +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + +* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Abnormally High Number Of Cloud Infrastructure API Calls|abnormally-high-number-of-cloud-infrastructure-api-calls}}=== +This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-09-07 + +
+
+ +====Search==== + +| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] +| where cardinality >=16 +| apply cloud_excessive_api_calls_v1 threshold=0.005 +| rename "IsOutlier(api_calls)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| where api_calls > expected_upper_threshold +| eval distance_from_threshold = api_calls - expected_upper_threshold +| table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold +| `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` + +====Associated Analytic Story==== + +* Suspicious Cloud User Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to create the probability density function. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== + + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Abnormally High Number Of Cloud Instances Destroyed|abnormally-high-number-of-cloud-instances-destroyed}}=== +This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-08-21 + +
+
+ +====Search==== + +| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] +| where cardinality >=16 +| apply cloud_excessive_instances_destroyed_v1 threshold=0.005 +| rename "IsOutlier(instances_destroyed)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| eval distance_from_threshold = instances_destroyed - expected_upper_threshold +| table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id +| `abnormally_high_number_of_cloud_instances_destroyed_filter` + +====Associated Analytic Story==== + +* Suspicious Cloud Instance Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability density function. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Many service accounts configured within a cloud infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Abnormally High Number Of Cloud Instances Launched|abnormally-high-number-of-cloud-instances-launched}}=== +This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-08-21 + +
+
+ +====Search==== + +| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] +| where cardinality >=16 +| apply cloud_excessive_instances_created_v1 threshold=0.005 +| rename "IsOutlier(instances_launched)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| eval distance_from_threshold = instances_launched - expected_upper_threshold +| table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id +| `abnormally_high_number_of_cloud_instances_launched_filter` + +====Associated Analytic Story==== + +* Cloud Cryptomining + +* Suspicious Cloud Instance Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Launched` to create the probability density function. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Abnormally High Number Of Cloud Security Group API Calls|abnormally-high-number-of-cloud-security-group-api-calls}}=== +This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-09-07 + +
+
+ +====Search==== + +| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] +| where cardinality >=16 +| apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 +| rename "IsOutlier(security_group_api_calls)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| where security_group_api_calls > expected_upper_threshold +| eval distance_from_threshold = security_group_api_calls - expected_upper_threshold +| table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold +| `abnormally_high_number_of_cloud_security_group_api_calls_filter` + +====Associated Analytic Story==== + +* Suspicious Cloud User Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to create the probability density function model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== + + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Access LSASS Memory for Dump Creation|access-lsass-memory-for-dump-creation}}=== +Detect memory dumping of the LSASS process. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] +* '''Last Updated''': 2019-12-06 + +
+
+ +====Search==== +`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `access_lsass_memory_for_dump_creation_filter` + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. + +====Reference==== + +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Applying Stolen Credentials via Mimikatz modules|applying-stolen-credentials-via-mimikatz-modules}}=== +This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1547/ T1547], [https://attack.mitre.org/techniques/T1548/ T1548], [https://attack.mitre.org/techniques/T1554/ T1554], [https://attack.mitre.org/techniques/T1556/ T1556], [https://attack.mitre.org/techniques/T1558/ T1558] +* '''Last Updated''': 2020-11-03 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)kerberos::ptt/)=true OR match_regex(cmd_line, /(?i)kerberos::golden/)=true OR match_regex(cmd_line, /(?i)kerberos::silver/)=true OR match_regex(cmd_line, /(?i)misc::skeleton/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1055 +| Process Injection +| Defense Evasion, Privilege Escalation +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1098 +| Account Manipulation +| Persistence +|- +| T1134 +| Access Token Manipulation +| Defense Evasion, Privilege Escalation +|- +| T1543 +| Create or Modify System Process +| Persistence, Privilege Escalation +|- +| T1547 +| Boot or Logon Autostart Execution +| Persistence, Privilege Escalation +|- +| T1548 +| Abuse Elevation Control Mechanism +| Defense Evasion, Privilege Escalation +|- +| T1554 +| Compromise Client Software Binary +| Persistence +|- +| T1556 +| Modify Authentication Process +| Credential Access, Defense Evasion +|- +| T1558 +| Steal or Forge Kerberos Tickets +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + +* https://adsecurity.org/?p=1275 + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Applying Stolen Credentials via PowerSploit modules|applying-stolen-credentials-via-powersploit-modules}}=== +Stolen credentials are applied by methods such as user impersonation, credential injection, spoofing of authentication processes or getting hold of critical accounts. This detection indicates such activities carried out by PowerSploit exploit kit APIs. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1547/ T1547], [https://attack.mitre.org/techniques/T1548/ T1548], [https://attack.mitre.org/techniques/T1554/ T1554], [https://attack.mitre.org/techniques/T1556/ T1556], [https://attack.mitre.org/techniques/T1558/ T1558] +* '''Last Updated''': 2020-11-03 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-CredentialInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-TokenManipulation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserImpersonation/)=true OR match_regex(cmd_line, /(?i)Get-System/)=true OR match_regex(cmd_line, /(?i)Invoke-RevertToSelf/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1055 +| Process Injection +| Defense Evasion, Privilege Escalation +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1098 +| Account Manipulation +| Persistence +|- +| T1134 +| Access Token Manipulation +| Defense Evasion, Privilege Escalation +|- +| T1543 +| Create or Modify System Process +| Persistence, Privilege Escalation +|- +| T1547 +| Boot or Logon Autostart Execution +| Persistence, Privilege Escalation +|- +| T1548 +| Abuse Elevation Control Mechanism +| Defense Evasion, Privilege Escalation +|- +| T1554 +| Compromise Client Software Binary +| Persistence +|- +| T1556 +| Modify Authentication Process +| Credential Access, Defense Evasion +|- +| T1558 +| Steal or Forge Kerberos Tickets +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Assessment of Credential Strength via DSInternals modules|assessment-of-credential-strength-via-dsinternals-modules}}=== +This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1087/ T1087], [https://attack.mitre.org/techniques/T1201/ T1201], [https://attack.mitre.org/techniques/T1552/ T1552], [https://attack.mitre.org/techniques/T1555/ T1555] +* '''Last Updated''': 2020-11-03 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-PasswordQuality/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1098 +| Account Manipulation +| Persistence +|- +| T1087 +| Account Discovery +| Discovery +|- +| T1201 +| Password Policy Discovery +| Discovery +|- +| T1552 +| Unsecured Credentials +| Credential Access +|- +| T1555 +| Credentials from Password Stores +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/MichaelGrafnetter/DSInternals + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Attempt To Add Certificate To Untrusted Store|attempt-to-add-certificate-to-untrusted-store}}=== +Attempt to add a certificate to the certificate store + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1553.004/ T1553.004] +* '''Last Updated''': 2020-11-03 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `attempt_to_add_certificate_to_untrusted_store_filter` + +====Associated Analytic Story==== + +* Disabling Security Tools + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1553.004 +| Install Root Certificate +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Installation + +* Actions on Objectives + + +====Known False Positives==== +There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log + + +''version'': 6 +
+
+ +---- + +==={{visible anchor|Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass|attempt-to-set-default-powershell-execution-policy-to-unrestricted-or-bypass}}=== +Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.001/ T1059.001] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest +| `drop_dm_object_name(Registry)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter` + +====Associated Analytic Story==== + +* Malicious PowerShell + +* Credential Dumping + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|} + +====Kill Chain Phase==== + +* Installation + +* Actions on Objectives + + +====Known False Positives==== +Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log + + +''version'': 6 +
+
+ +---- + +==={{visible anchor|Attempt To Stop Security Service|attempt-to-stop-security-service}}=== +This search looks for attempts to stop security-related services on the endpoint. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.001/ T1562.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|lookup security_services_lookup service as process OUTPUTNEW category, description +| search category=security +| `attempt_to_stop_security_service_filter` + +====Associated Analytic Story==== + +* Disabling Security Tools + + +====How To Implement==== +You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.001 +| Disable or Modify Tools +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Installation + +* Actions on Objectives + + +====Known False Positives==== +None identified. Attempts to disable security-related services should be identified and understood. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Attempted Credential Dump From Registry via Reg exe|attempted-credential-dump-from-registry-via-reg-exe}}=== +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.002/ T1003.002] +* '''Last Updated''': 2019-12-02 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.002 +| Security Account Manager +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Attempted Credential Dump From Registry via Reg exe|attempted-credential-dump-from-registry-via-reg-exe}}=== +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] +* '''Last Updated''': 2020-6-04 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) +| where process_name="cmd.exe" OR process_name="reg.exe" +| where cmd_line != null AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +You must be ingesting windows endpoint data that tracks process activity, including parent-child relationships from your endpoints. + +====Required field==== + +* process_name + +* _time + +* dest_device_id + +* dest_user_id + +* process + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/splunk/security_content/blob/55a17c65f9f56c2220000b62701765422b46125d/detections/attempted_credential_dump_from_registry_via_reg_exe.yml + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|BCDEdit Failure Recovery Modification|bcdedit-failure-recovery-modification}}=== +This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1490/ T1490] +* '''Last Updated''': 2020-12-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `bcdedit_failure_recovery_modification_filter` + +====Associated Analytic Story==== + +* Ryuk Ransomware + +* Ransomware + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1490 +| Inhibit System Recovery +| Impact +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators may modify the boot configuration. + +====Reference==== + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Batch File Write to System32|batch-file-write-to-system32}}=== +The search looks for a batch file (.bat) written to the Windows system directory tree. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1204.002/ T1204.002] +* '''Last Updated''': 2018-12-14 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| rex field=file_name "(?\.[^\.]+)$" +| search file_path=*system32* AND file_extension=.bat +| `batch_file_write_to_system32_filter` + +====Associated Analytic Story==== + +* SamSam Ransomware + + +====How To Implement==== +You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1204.002 +| Malicious File +| Execution +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/batch_file_in_system32/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Certutil exe certificate extraction|certutil-exe-certificate-extraction}}=== +This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `certutil_exe_certificate_extraction_filter` + +====Associated Analytic Story==== + +* Windows Persistence Techniques + +* Cloud Federated Credential Abuse + + +====How To Implement==== + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Installation + + +====Known False Positives==== +Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Cloud API Calls From Previously Unseen User Roles|cloud-api-calls-from-previously-unseen-user-roles}}=== +This search looks for new commands from each user role. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-09-04 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUserApiCall=min(firstTimeSeen) +| where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") +| table firstTime, user, object, command +|`security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `cloud_api_calls_from_previously_unseen_user_roles_filter` + +====Associated Analytic Story==== + +* Suspicious Cloud User Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Cloud Compute Instance Created By Previously Unseen User|cloud-compute-instance-created-by-previously-unseen-user}}=== +This search looks for cloud compute instances created by users who have not created them before. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-08-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUser=min(firstTimeSeen) +| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count vendor_region +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_by_previously_unseen_user_filter` + +====Associated Analytic Story==== + +* Cloud Cryptomining + + +====How To Implement==== +You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Cloud Compute Instance Created In Previously Unused Region|cloud-compute-instance-created-in-previously-unused-region}}=== +This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2020-09-02 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenRegion=min(firstTimeSeen) +| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count , vendor_region +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_in_previously_unused_region_filter` + +====Associated Analytic Story==== + +* Cloud Cryptomining + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Cloud Compute Instance Created With Previously Unseen Image|cloud-compute-instance-created-with-previously-unseen-image}}=== +This search looks for cloud compute instances being created with previously unseen image IDs. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': +* '''Last Updated''': 2018-10-12 + +
+
+ +====Search==== + +| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| `drop_dm_object_name("Instance_Changes")` +| where image_id != "unknown" +| lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenImage=min(firstTimeSeen) +| where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") +| table firstTime, user, image_id, count, dest +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_with_previously_unseen_image_filter` + +====Associated Analytic Story==== + +* Cloud Cryptomining + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Cloud Compute Instance Created With Previously Unseen Instance Type|cloud-compute-instance-created-with-previously-unseen-instance-type}}=== +Find EC2 instances being created with previously unseen instance types. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': +* '''Last Updated''': 2020-09-12 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| `drop_dm_object_name("Instance_Changes")` +| where instance_type != "unknown" +| lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenInstanceType=min(firstTimeSeen) +| where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count, instance_type +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` + +====Associated Analytic Story==== + +* Cloud Cryptomining + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Cloud Instance Modified By Previously Unseen User|cloud-instance-modified-by-previously-unseen-user}}=== +This search looks for cloud instances being modified by users who have not previously modified them. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-07-29 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUser=min(firstTimeSeen) +| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") +| table firstTime user command object_id count +| `security_content_ctime(firstTime)` +| `cloud_instance_modified_by_previously_unseen_user_filter` + +====Associated Analytic Story==== + +* Suspicious Cloud Instance Activities + + +====How To Implement==== +This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Cloud Provisioning Activity From Previously Unseen City|cloud-provisioning-activity-from-previously-unseen-city}}=== +This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-10-09 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(City) +| lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenCity=min(firstTimeSeen) +| where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, City, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_city_filter` +| `security_content_ctime(firstTime)` + +====Associated Analytic Story==== + +* Suspicious Cloud Provisioning Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Cloud Provisioning Activity From Previously Unseen Country|cloud-provisioning-activity-from-previously-unseen-country}}=== +This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-10-09 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(Country) +| lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenCountry=min(firstTimeSeen) +| where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") +| table firstTime, src, Country, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_country_filter` +| `security_content_ctime(firstTime)` + +====Associated Analytic Story==== + +* Suspicious Cloud Provisioning Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Cloud Provisioning Activity From Previously Unseen IP Address|cloud-provisioning-activity-from-previously-unseen-ip-address}}=== +This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-08-16 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenSrc=min(firstTimeSeen) +| where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, user, object_id, command +| `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` +| `security_content_ctime(firstTime)` + +====Associated Analytic Story==== + +* Suspicious Cloud Provisioning Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Cloud Provisioning Activity From Previously Unseen Region|cloud-provisioning-activity-from-previously-unseen-region}}=== +This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-08-16 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(Region) +| lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenRegion=min(firstTimeSeen) +| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, Region, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_region_filter` +| `security_content_ctime(firstTime)` + +====Associated Analytic Story==== + +* Suspicious Cloud Provisioning Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Common Ransomware Extensions|common-ransomware-extensions}}=== +The search looks for file modifications with extensions commonly used by Ransomware + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1485/ T1485] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| rex field=file_name "(?\.[^\.]+)$" +| `ransomware_extensions` +| `common_ransomware_extensions_filter` + +====Associated Analytic Story==== + +* SamSam Ransomware + +* Ryuk Ransonware + +* Ransomware + + +====How To Implement==== +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\ +This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ +1. \ +1. **Label:** File Extension, **Field:** file_extension\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1485 +| Data Destruction +| Impact +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_extensions/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Common Ransomware Notes|common-ransomware-notes}}=== +The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1485/ T1485] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `ransomware_notes` +| `common_ransomware_notes_filter` + +====Associated Analytic Story==== + +* SamSam Ransomware + +* Ransomware + +* Ryuk Ransomware + + +====How To Implement==== +You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1485 +| Data Destruction +| Impact +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It's possible that a legitimate file could be created with the same name used by ransomware note files. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Create Remote Thread into LSASS|create-remote-thread-into-lsass}}=== +Detect remote thread creation into LSASS consistent with credential dumping. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] +* '''Last Updated''': 2019-12-06 + +
+
+ +====Search==== +`sysmon` EventID=8 TargetImage=*lsass.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `create_remote_thread_into_lsass_filter` + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. + +====Reference==== + +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Create local admin accounts using net exe|create-local-admin-accounts-using-net-exe}}=== +This search looks for the creation of local administrator accounts using net.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.001/ T1136.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|`create_local_admin_accounts_using_net_exe_filter` + +====Associated Analytic Story==== + +* DHS Report TA18-074A + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.001 +| Local Account +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators often leverage net.exe to create admin accounts. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Create or delete windows shares using net exe|create-or-delete-windows-shares-using-net-exe}}=== +This search looks for the creation or deletion of hidden shares using net.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1070.005/ T1070.005] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search process=*share* +| `create_or_delete_windows_shares_using_net_exe_filter` + +====Associated Analytic Story==== + +* Hidden Cobra Malware + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070.005 +| Network Share Connection Removal +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. + +====Reference==== + +* https://attack.mitre.org/techniques/T1070/005 + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|Creation of Shadow Copy|creation-of-shadow-copy}}=== +Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.003/ T1003.003] +* '''Last Updated''': 2019-12-10 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `creation_of_shadow_copy_filter` + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Legitimate administrator usage of Vssadmin or Wmic will create false positives. + +====Reference==== + +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Creation of Shadow Copy with wmic and powershell|creation-of-shadow-copy-with-wmic-and-powershell}}=== +This search detects the use of wmic and Powershell to create a shadow copy. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.003/ T1003.003] +* '''Last Updated''': 2019-12-10 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `creation_of_shadow_copy_with_wmic_and_powershell_filter` + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Legtimate administrator usage of wmic to create a shadow copy. + +====Reference==== + +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Creation of lsass Dump with Taskmgr|creation-of-lsass-dump-with-taskmgr}}=== +Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] +* '''Last Updated''': 2020-02-03 + +
+
+ +====Search==== +`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `creation_of_lsass_dump_with_taskmgr_filter` + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. + +====Reference==== + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager + +* https://attack.mitre.org/techniques/T1003/001/ + +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Dumping via Copy Command from Shadow Copy|credential-dumping-via-copy-command-from-shadow-copy}}=== +This search detects credential dumping using copy command from a shadow copy. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.003/ T1003.003] +* '''Last Updated''': 2019-12-10 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `credential_dumping_via_copy_command_from_shadow_copy_filter` + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +unknown + +====Reference==== + +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Dumping via Symlink to Shadow Copy|credential-dumping-via-symlink-to-shadow-copy}}=== +This search detects the creation of a symlink to a shadow copy. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.003/ T1003.003] +* '''Last Updated''': 2019-12-10 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `credential_dumping_via_symlink_to_shadow_copy_filter` + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +unknown + +====Reference==== + +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Extraction indicative of FGDump and CacheDump with s option|credential-extraction-indicative-of-fgdump-and-cachedump-with-s-option}}=== +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] +* '''Last Updated''': 2020-10-18 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| where cmd_line != null AND process_name != null AND parent_process_name != null AND match_regex(parent_process_name, /(?i)System32\\services.exe/)=true AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-s/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* process_name + +* parent_process_name + +* _time + +* process_path + +* dest_user_id + +* process + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Extraction indicative of FGDump and CacheDump with v option|credential-extraction-indicative-of-fgdump-and-cachedump-with-v-option}}=== +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] +* '''Last Updated''': 2020-10-18 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null) +| where cmd_line != null AND process_name != null AND process_path != null AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-v/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* process_name + +* _time + +* process_path + +* dest_user_id + +* process + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Extraction indicative of Lazagne command line options|credential-extraction-indicative-of-lazagne-command-line-options}}=== +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003], [https://attack.mitre.org/techniques/T1555/ T1555] +* '''Last Updated''': 2020-10-18 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND match_regex(cmd_line, /(?i)all\s+\-oA\s+\-output/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|- +| T1555 +| Credentials from Password Stores +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Extraction indicative of use of DSInternals credential conversion modules|credential-extraction-indicative-of-use-of-dsinternals-credential-conversion-modules}}=== +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] +* '''Last Updated''': 2020-10-21 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)ConvertFrom-ADManagedPasswordBlob/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-UnicodePassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-KerberosKey/)=true OR match_regex(cmd_line, /(?i)ConvertTo-LMHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-NTHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-OrgIdHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-UnicodePassword/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* process_name + +* parent_process_name + +* _time + +* process_path + +* dest_user_id + +* process + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/MichaelGrafnetter/DSInternals + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Extraction indicative of use of DSInternals modules|credential-extraction-indicative-of-use-of-dsinternals-modules}}=== +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] +* '''Last Updated''': 2020-10-21 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ADDBBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Get-ADDBKdsRootKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBSchemaAttribute/)=true OR match_regex(cmd_line, /(?i)Get-ADKeyCredential/)=true OR match_regex(cmd_line, /(?i)Get-ADReplAccount/)=true OR match_regex(cmd_line, /(?i)Get-ADReplBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADSIAccount/)=true OR match_regex(cmd_line, /(?i)Get-AzureADUserEx/)=true OR match_regex(cmd_line, /(?i)Get-BootKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaPolicyInformation/)=true OR match_regex(cmd_line, /(?i)Get-SamPasswordPolicy/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* process_name + +* parent_process_name + +* _time + +* process_path + +* dest_user_id + +* process + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/MichaelGrafnetter/DSInternals + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Extraction indicative of use of Mimikatz modules|credential-extraction-indicative-of-use-of-mimikatz-modules}}=== +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] +* '''Last Updated''': 2020-10-21 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)CRYPTO::Certificates/)=true OR match_regex(cmd_line, /(?i)CRYPTO::keys/)=true OR match_regex(cmd_line, /(?i)kerberos::list/)=true OR match_regex(cmd_line, /(?i)kerberos::tgt/)=true OR match_regex(cmd_line, /(?i)lsadump::sam/)=true OR match_regex(cmd_line, /(?i)lsadump::secrets/)=true OR match_regex(cmd_line, /(?i)lsadump::cache/)=true OR match_regex(cmd_line, /(?i)lsadump::lsa/)=true OR match_regex(cmd_line, /(?i)lsadump::trust/)=true OR match_regex(cmd_line, /(?i)lsadump::backupkeys/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Extraction indicative of use of PowerSploit modules|credential-extraction-indicative-of-use-of-powersploit-modules}}=== +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] +* '''Last Updated''': 2020-10-21 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ApplicationHost/)=true OR match_regex(cmd_line, /(?i)Get-CachedGPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-GPPAutologon/)=true OR match_regex(cmd_line, /(?i)Get-GPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-RegistryAutoLogon/)=true OR match_regex(cmd_line, /(?i)Get-SiteListPassword/)=true OR match_regex(cmd_line, /(?i)Get-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Request-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Get-VaultCredential/)=true OR match_regex(cmd_line, /(?i)Invoke-Kerberoast/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Extraction native Microsoft debuggers peek into the kernel|credential-extraction-native-microsoft-debuggers-peek-into-the-kernel}}=== +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] +* '''Last Updated''': 2020-10-18 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| where cmd_line != null AND parent_process_name != null AND process_name != null AND ( match_regex(parent_process_name, /(?i)ntkd\.exe/)=true OR match_regex(parent_process_name, /(?i)livekd\.exe/)=true ) AND match_regex(process_name, /(?i)conhost\.exe/)=true AND match_regex(cmd_line, /(?i)0xffffffff/)=true AND match_regex(cmd_line, /(?i)\-ForceV1/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* process_name + +* parent_process_name + +* _time + +* dest_device_id + +* dest_user_id + +* process + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. + +====Reference==== + +* https://medium.com/@clermont1050/covid-19-cyber-infection-c615ead7c29 + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Extraction native Microsoft debuggers via z command line option|credential-extraction-native-microsoft-debuggers-via-z-command-line-option}}=== +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] +* '''Last Updated''': 2020-10-18 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) +| where cmd_line != null AND process_name != null AND ( match_regex(process_name, /^(?i)ntkd\.exe/)=true OR match_regex(process_name, /^(?i)kd\.exe/)=true ) AND match_regex(cmd_line, /(?i)\-z\s+/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* process_name + +* _time + +* dest_device_id + +* dest_user_id + +* process + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals|credential-extraction-via-get-addbaccount-module-present-in-powersploit-and-dsinternals}}=== +Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] +* '''Last Updated''': 2020-10-18 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND match_regex(cmd_line, /(?i)Get-ADDBAccount/)=true AND match_regex(cmd_line, /(?i)\-dbpath[\s;:\.\ +|]+/)=true + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|DNS Query Length Outliers - MLTK|dns-query-length-outliers---mltk}}=== +This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.004/ T1071.004] +* '''Last Updated''': 2020-01-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type +| search DNS.record_type=* +| `drop_dm_object_name(DNS)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval query_length = len(query) +| apply dns_query_pdfmodel threshold=0.01 +| rename "IsOutlier(query_length)" as isOutlier +| search isOutlier > 0 +| sort -query_length +| table start_time end_time query record_type count src dest query_length +| `dns_query_length_outliers___mltk_filter` + +====Associated Analytic Story==== + +* Hidden Cobra Malware + +* Suspicious DNS Traffic + +* Command and Control + + +====How To Implement==== +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +1. \ +1. **Label:** DNS Query Length, **Field:** query_length\ +1. \ +1. **Label:** Number of events, **Field:** count\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.004 +| DNS +| Command and Control +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|DNS Query Length With High Standard Deviation|dns-query-length-with-high-standard-deviation}}=== +This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003] +* '''Last Updated''': 2021-01-18 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query +| `drop_dm_object_name("DNS")` +| eval query_length = len(query) +| table query query_length record_type count +| eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50 +| where query_length>(avg+stdev*2) +| eval z_score=(query_length-avg)/stdev +| `dns_query_length_with_high_standard_deviation_filter` + +====Associated Analytic Story==== + +* Hidden Cobra Malware + +* Suspicious DNS Traffic + +* Command and Control + + +====How To Implement==== +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +It's possible there can be long domain names that are legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Deleting Shadow Copies|deleting-shadow-copies}}=== +The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1490/ T1490] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `deleting_shadow_copies_filter` + +====Associated Analytic Story==== + +* Windows Log Manipulation + +* SamSam Ransomware + +* Ransomware + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1490 +| Inhibit System Recovery +| Impact +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Detect AWS Console Login by New User|detect-aws-console-login-by-new-user}}=== +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': +* '''Last Updated''': 2020-05-28 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user +| `drop_dm_object_name(Authentication)` +| inputlookup append=t previously_seen_users_console_logins +| stats min(firstTime) as firstTime max(lastTime) as lastTime by user +| eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") +|where userStatus="First Time Logging into AWS Console" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_aws_console_login_by_new_user_filter` + +====Associated Analytic Story==== + +* Suspicious Cloud Authentication Activities + + +====How To Implement==== +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect AWS Console Login by User from New City|detect-aws-console-login-by-user-from-new-city}}=== +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2020-10-07 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user City +| join user type=outer [ +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user City +| fields earliestseen user City] +| eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") +| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userCity = "New City" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user City userStatus userCity +| `detect_aws_console_login_by_user_from_new_city_filter` + +====Associated Analytic Story==== + +* Suspicious AWS Login Activities + +* Suspicious Cloud Authentication Activities + + +====How To Implement==== +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect AWS Console Login by User from New Country|detect-aws-console-login-by-user-from-new-country}}=== +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2020-10-07 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user Country +| join user type=outer [ +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user Country +| fields earliestseen user Country] +| eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") +| eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userCountry = "New Country" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user Country userStatus userCountry +| `detect_aws_console_login_by_user_from_new_country_filter` + +====Associated Analytic Story==== + +* Suspicious AWS Login Activities + +* Suspicious Cloud Authentication Activities + + +====How To Implement==== +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect AWS Console Login by User from New Region|detect-aws-console-login-by-user-from-new-region}}=== +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2020-10-07 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user Region +| join user type=outer [ +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user Region +| fields earliestseen user Region] +| eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") +| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userRegion = "New Region" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user Region userStatus userRegion +| `detect_aws_console_login_by_user_from_new_region_filter` + +====Associated Analytic Story==== + +* Suspicious AWS Login Activities + +* Suspicious Cloud Authentication Activities + + +====How To Implement==== +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Activity Related to Pass the Hash Attacks|detect-activity-related-to-pass-the-hash-attacks}}=== +This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1550.002/ T1550.002] +* '''Last Updated''': 2020-10-15 + +
+
+ +====Search==== +`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_activity_related_to_pass_the_hash_attacks_filter` + +====Associated Analytic Story==== + +* Lateral Movement + + +====How To Implement==== +To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1550.002 +| Pass the Hash +| Defense Evasion, Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.002/atomic_red_team/windows-security.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|Detect Computer Changed with Anonymous Account|detect-computer-changed-with-anonymous-account}}=== +This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1210/ T1210] +* '''Last Updated''': 2020-09-18 + +
+
+ +====Search==== +`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 +| stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user +| `detect_computer_changed_with_anonymous_account_filter` + +====Associated Analytic Story==== + +* Detect Zerologon Attack + + +====How To Implement==== +This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event Logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1210 +| Exploitation of Remote Services +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None thus far found + +====Reference==== + +* https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Credential Dumping through LSASS access|detect-credential-dumping-through-lsass-access}}=== +This search looks for reading lsass memory consistent with credential dumping. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] +* '''Last Updated''': 2019-12-03 + +
+
+ +====Search==== +`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_credential_dumping_through_lsass_access_filter` + +====Associated Analytic Story==== + +* Credential Dumping + +* Detect Zerologon Attack + + +====How To Implement==== +This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Detect Dump LSASS Memory using comsvcs|detect-dump-lsass-memory-using-comsvcs}}=== +This search detects the memory of lsass.exe being dumped for offline credential theft attack. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.003/ T1003.003] +* '''Last Updated''': 2020-09-15 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() +| eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)) +| where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including Windows command line logging. You can see how we test this with [Event Code 4688](https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4688a) on the [attack_range](https://github.com/splunk/attack_range/blob/develop/ansible/roles/windows_common/tasks/windows-enable-4688-cmd-line-audit.yml). + +====Required field==== + +* process_name + +* _tenant + +* _time + +* dest_device_id + +* process + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Excessive Account Lockouts From Endpoint|detect-excessive-account-lockouts-from-endpoint}}=== +This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.002/ T1078.002] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result +|`drop_dm_object_name("All_Changes")` +|`drop_dm_object_name("Account_Management")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search count > 5 +| `detect_excessive_account_lockouts_from_endpoint_filter` + +====Associated Analytic Story==== + +* Account Monitoring and Controls + + +====How To Implement==== +You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +(Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\ + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.002 +| Domain Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|Detect Excessive User Account Lockouts|detect-excessive-user-account-lockouts}}=== +This search detects user accounts that have been locked out a relatively high number of times in a short period. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.003/ T1078.003] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result +|`drop_dm_object_name("All_Changes")` +|`drop_dm_object_name("Account_Management")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search count > 5 +| `detect_excessive_user_account_lockouts_filter` + +====Associated Analytic Story==== + +* Account Monitoring and Controls + + +====How To Implement==== +ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.003 +| Local Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Detect GCP Storage access from a new IP|detect-gcp-storage-access-from-a-new-ip}}=== +This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2020-08-10 + +
+
+ +====Search==== +`google_gcp_pubsub_message` +| multikv +| rename sc_status_ as status +| rename cs_object_ as bucket_name +| rename c_ip_ as remote_ip +| rename cs_uri_ as request_uri +| rename cs_method_ as operation +| search status="\"200\"" +| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri +| table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri +| inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri +| outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv +| eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) +| where newIP=1 +| eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") +| eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") +| table first_time last_time bucket_name remote_ip operation request_uri +| `detect_gcp_storage_access_from_a_new_ip_filter` + +====Associated Analytic Story==== + +* Suspicious GCP Storage Activities + + +====How To Implement==== +This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour. It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect HTML Help Renamed|detect-html-help-renamed}}=== +The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.001/ T1218.001] +* '''Last Updated''': 2021-02-11 + +
+
+ +====Search==== +`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_renamed_filter` + +====Associated Analytic Story==== + +* Suspicious Compiled HTML Activity + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed hh.exe may be used. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.001 +| Compiled HTML File +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/001/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect HTML Help Spawn Child Process|detect-html-help-spawn-child-process}}=== +The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.001/ T1218.001] +* '''Last Updated''': 2021-02-11 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_spawn_child_process_filter` + +====Associated Analytic Story==== + +* Suspicious Compiled HTML Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.001 +| Compiled HTML File +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/001/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ + +* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 + +* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect HTML Help URL in Command Line|detect-html-help-url-in-command-line}}=== +The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.001/ T1218.001] +* '''Last Updated''': 2021-02-11 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_url_in_command_line_filter` + +====Associated Analytic Story==== + +* Suspicious Compiled HTML Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.001 +| Compiled HTML File +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/001/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ + +* https://blog.sevagas.com/?Hacking-around-HTA-files + +* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 + +* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect HTML Help Using InfoTech Storage Handlers|detect-html-help-using-infotech-storage-handlers}}=== +The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.001/ T1218.001] +* '''Last Updated''': 2021-02-11 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_using_infotech_storage_handlers_filter` + +====Associated Analytic Story==== + +* Suspicious Compiled HTML Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.001 +| Compiled HTML File +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/001/ + +* https://www.kb.cert.org/vuls/id/851869 + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + +* https://lolbas-project.github.io/lolbas/Binaries/Hh/ + +* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 + +* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect IPv6 Network Infrastructure Threats|detect-ipv6-network-infrastructure-threats}}=== +By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557.002/ T1557.002] +* '''Last Updated''': 2020-10-28 + +
+
+ +====Search==== +`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") +| eval src_interface=src_int_prefix_long+src_int_suffix +| eval dest_interface=dest_int_prefix_long+dest_int_suffix +| stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface +| table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_ipv6_network_infrastructure_threats_filter` + +====Associated Analytic Story==== + +* Router and Infrastructure Security + + +====How To Implement==== +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1200 +| Hardware Additions +| Initial Access +|- +| T1498 +| Network Denial of Service +| Impact +|- +| T1557.002 +| ARP Cache Poisoning +| Collection, Credential Access +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Delivery + +* Actions on Objectives + + +====Known False Positives==== +None currently known + +====Reference==== + +* https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Kerberoasting|detect-kerberoasting}}=== +This search detects a potential kerberoasting attack via service principal name requests + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1558.003/ T1558.003] +* '''Last Updated''': 2020-10-21 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() +| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id") +| where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" +| first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] +| where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID +| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" +| select start_time, end_time, entities, body +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2 + +====Required field==== + +* service_name + +* _time + +* event_code + +* ticket_encryption_type + +* service_id + +* ticket_options + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1558.003 +| Kerberoasting +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Older systems that support kerberos RC4 by default NetApp may generate false positives + +====Reference==== + +* Initial ESCU implementation by Jose Hernandez and Patrick Bareiss + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Large Outbound ICMP Packets|detect-large-outbound-icmp-packets}}=== +This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1095/ T1095] +* '''Last Updated''': 2018-06-01 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip +| `drop_dm_object_name("All_Traffic")` +| search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_large_outbound_icmp_packets_filter` + +====Associated Analytic Story==== + +* Command and Control + + +====How To Implement==== +In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1095 +| Non-Application Layer Protocol +| Command and Control +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Detect MSHTA Url in Command Line|detect-mshta-url-in-command-line}}=== +This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.005/ T1218.005] +* '''Last Updated''': 2021-01-20 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mshta_url_in_command_line_filter` + +====Associated Analytic Story==== + +* Suspicious MSHTA Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +It is possible legitimate applications may perform this behavior and will need to be filtered. + +====Reference==== + +* https://github.com/redcanaryco/AtomicTestHarnesses + +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect New Local Admin account|detect-new-local-admin-account}}=== +This search looks for newly created accounts that have been elevated to local administrators. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.001/ T1136.001] +* '''Last Updated''': 2020-07-08 + +
+
+ +====Search==== +`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) +| transaction member_id connected=false maxspan=180m +| rename member_id as user +| stats count min(_time) as firstTime max(_time) as lastTime by user dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_new_local_admin_account_filter` + +====Associated Analytic Story==== + +* DHS Report TA18-074A + + +====How To Implement==== +You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732 + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.001 +| Local Account +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Known False Positives==== +The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Detect New Open GCP Storage Buckets|detect-new-open-gcp-storage-buckets}}=== +This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2020-08-05 + +
+
+ +====Search==== +`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions +| spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action +| spath output=user path=data.protoPayload.authenticationInfo.principalEmail +| spath output=location path=data.protoPayload.resourceLocation.currentLocations{} +| spath output=src path=data.protoPayload.requestMetadata.callerIp +| spath output=bucketName path=data.protoPayload.resourceName +| spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role +| spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member +| search (member=allUsers AND action=ADD) +| table _time, bucketName, src, user, location, action, role, member +| search `detect_new_open_gcp_storage_buckets_filter` + +====Associated Analytic Story==== + +* Suspicious GCP Storage Activities + + +====How To Implement==== +This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect New Open S3 Buckets over AWS CLI|detect-new-open-s3-buckets-over-aws-cli}}=== +This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== +`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") +| rename requestParameters.bucketName AS bucketName +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_new_open_s3_buckets_over_aws_cli_filter` + +====Associated Analytic Story==== + +* Suspicious AWS S3 Activities + + +====How To Implement==== + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect New Open S3 buckets|detect-new-open-s3-buckets}}=== +This search looks for CloudTrail events where a user has created an open/public S3 bucket. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== +`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl +| rex field=_raw "(?{.+})" +| spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} +| search grantees=* +| mvexpand grantees +| spath input=grantees output=uri path=Grantee.URI +| spath input=grantees output=permission path=Permission +| search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") +| search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") +| rename requestParameters.bucketName AS bucketName +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission bucketName +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_new_open_s3_buckets_filter` + +====Associated Analytic Story==== + +* Suspicious AWS S3 Activities + + +====How To Implement==== +You must install the AWS App for Splunk. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Detect Outbound SMB Traffic|detect-outbound-smb-traffic}}=== +This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.002/ T1071.002] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip +| `drop_dm_object_name("All_Traffic")` +| `security_content_ctime(start_time)` +| `security_content_ctime(end_time)` +| `detect_outbound_smb_traffic_filter` + +====Associated Analytic Story==== + +* Hidden Cobra Malware + +* DHS Report TA18-074A + +* Sunburst Malware + + +====How To Implement==== +In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.002 +| File Transfer Protocols +| Command and Control +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Known False Positives==== +It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Detect Pass the Hash|detect-pass-the-hash}}=== +This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1550.002/ T1550.002] +* '''Last Updated''': 2020-10-21 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() +| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), LogonType=map_get(input_event, "logon_type"), LogonProcess=map_get(input_event, "logon_process"), ComputerName=map_get(input_event, "dest_ip_primary_artifact"), AccountName=map_get(input_event, "dest_user_primary_artifact") +| where (LogonType="3" AND LogonProcess="NtLmSsp" AND AccountName IS NOT NULL) OR (LogonType="9" AND LogonProcess="seclogo") +| first_time_event input_columns=["EventCode","LogonProcess","ComputerName"] +| where first_time_EventCode_LogonProcess_ComputerName +| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" +| select start_time, end_time, entities, body +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2 + +====Required field==== + +* logon_process + +* dest_user_primary_artifact + +* _time + +* event_code + +* dest_ip_primary_artifact + +* logon_type + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1550.002 +| Pass the Hash +| Defense Evasion, Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. + +====Reference==== + +* Initial ESCU implementation by Bhavin Patel and Patrick Bareiss + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Path Interception By Creation Of program exe|detect-path-interception-by-creation-of-program-exe}}=== +The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1574.009/ T1574.009] +* '''Last Updated''': 2020-07-03 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest index +| `drop_dm_object_name(Processes)` +| rex field=process "^.*?\\\\(?[^\\\\]*\.(?:exe +|bat +|com +|ps1))" +| eval process_name = lower(process_name) +| eval service_process = lower(service_process) +| where process_name != service_process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_path_interception_by_creation_of_program_exe_filter` + +====Associated Analytic Story==== + +* Windows Persistence Techniques + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1574.009 +| Path Interception by Unquoted Path +| Defense Evasion, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +unknown + +====Reference==== + +* https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Detect Port Security Violation|detect-port-security-violation}}=== +By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557.002/ T1557.002] +* '''Last Updated''': 2020-10-28 + +
+
+ +====Search==== +`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") +| eval src_interface=src_int_prefix_long+src_int_suffix +| stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_port_security_violation_filter` + +====Associated Analytic Story==== + +* Router and Infrastructure Security + + +====How To Implement==== +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1200 +| Hardware Additions +| Initial Access +|- +| T1498 +| Network Denial of Service +| Impact +|- +| T1557.002 +| ARP Cache Poisoning +| Collection, Credential Access +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Delivery + +* Exploitation + +* Actions on Objectives + + +====Known False Positives==== +This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Prohibited Applications Spawning cmd exe|detect-prohibited-applications-spawning-cmd-exe}}=== +This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.003/ T1059.003] +* '''Last Updated''': 2020-11-10 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|search [`prohibited_apps_launching_cmd`] +| `detect_prohibited_applications_spawning_cmd_exe_filter` + +====Associated Analytic Story==== + +* Suspicious Command-Line Executions + +* Suspicious MSHTA Activity + +* Suspicious Zoom Child Processes + +* Sunburst Malware + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.003 +| Windows Command Shell +| Execution +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/powershell_spawn_cmd/windows-sysmon.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|Detect Prohibited Applications Spawning cmd exe|detect-prohibited-applications-spawning-cmd-exe}}=== +This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. This is a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe` by @bpatel. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059/ T1059] +* '''Last Updated''': 2020-7-13 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) + +| where process_name="cmd.exe" +| rex field=parent_process "(?[^\\\\]+)$" +| where field0="winword.exe" OR field0="excel.exe" OR field0="outlook.exe" OR field0="powerpnt.exe" OR field0="visio.exe" OR field0="mspub.exe" OR field0="acrobat.exe" OR field0="acrord32.exe" OR field0="chrome.exe" OR field0="iexplore.exe" OR field0="opera.exe" OR field0="firefox.exe" OR field0="java.exe" OR field0="powershell.exe" + +| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body="TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting sysmon logs. This search has been modified to process raw sysmon data from attack_range's nxlogs on DSP. + +====Required field==== + +* process_name + +* parent_process_name + +* _time + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059 +| Command and Scripting Interpreter +| Execution +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect PsExec With accepteula Flag|detect-psexec-with-accepteula-flag}}=== +This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002] +* '''Last Updated''': 2020-11-10 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest Processes.parent_process_name +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_psexec_with_accepteula_flag_filter` + +====Associated Analytic Story==== + +* SamSam Ransomware + +* DHS Report TA18-074A + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Detect Rare Executables|detect-rare-executables}}=== +This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': +* '''Last Updated''': 2020-03-16 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name +| rename Processes.process_name as process +| rex field=user "(?.*)\\\\(?.*)" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search [ +| tstats count from datamodel=Endpoint.Processes by Processes.process_name +| rare Processes.process_name limit=30 +| rename Processes.process_name as process +| `filter_rare_process_allow_list` +| table process ] +| `detect_rare_executables_filter` + +====Associated Analytic Story==== + +* Emotet Malware DHS Report TA18-201A + +* Unusual Processes + +* Cloud Federated Credential Abuse + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_allow_list` searches two lookup files for allowed processes. These consist of `rare_process_allow_list_default.csv` and `rare_process_allow_list_local.csv`. To add your own processes to the allow list, add them to `rare_process_allow_list_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the allow_list value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Installation + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_allow_list_local.csv` to filter them out of your search results. + +====Reference==== + + +====Test Dataset==== + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|Detect Regasm Spawning a Process|detect-regasm-spawning-a-process}}=== +The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] +* '''Last Updated''': 2021-02-12 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regasm_spawning_a_process_filter` + +====Associated Analytic Story==== + +* Suspicious Regsvcs Regasm Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/009/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + +* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Regasm with Network Connection|detect-regasm-with-network-connection}}=== +The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] +* '''Last Updated''': 2021-02-16 + +
+
+ +====Search==== +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regasm_with_network_connection_filter` + +====Associated Analytic Story==== + +* Suspicious Regsvcs Regasm Activity + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/009/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + +* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Regasm with no Command Line Arguments|detect-regasm-with-no-command-line-arguments}}=== +The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] +* '''Last Updated''': 2021-02-12 + +
+
+ +====Search==== +`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) +| regex CommandLine="(regasm\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regasm_with_no_command_line_arguments_filter` + +====Associated Analytic Story==== + +* Suspicious Regsvcs Regasm Activity + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/009/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + +* https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Regsvcs Spawning a Process|detect-regsvcs-spawning-a-process}}=== +The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] +* '''Last Updated''': 2021-02-12 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regsvcs_spawning_a_process_filter` + +====Associated Analytic Story==== + +* Suspicious Regsvcs Regasm Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/009/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Regsvcs with Network Connection|detect-regsvcs-with-network-connection}}=== +The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] +* '''Last Updated''': 2021-02-16 + +
+
+ +====Search==== +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regsvcs_with_network_connection_filter` + +====Associated Analytic Story==== + +* Suspicious Regsvcs Regasm Activity + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/009/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Regsvcs with No Command Line Arguments|detect-regsvcs-with-no-command-line-arguments}}=== +The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] +* '''Last Updated''': 2021-02-12 + +
+
+ +====Search==== +`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) +| regex CommandLine="(regsvcs\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regsvcs_with_no_command_line_arguments_filter` + +====Associated Analytic Story==== + +* Suspicious Regsvcs Regasm Activity + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/009/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + +* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Regsvr32 Application Control Bypass|detect-regsvr32-application-control-bypass}}=== +Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ +Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.010/ T1218.010] +* '''Last Updated''': 2021-01-28 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regsvr32_application_control_bypass_filter` + +====Associated Analytic Story==== + +* Suspicious Regsvr32 Activity + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.010 +| Regsvr32 +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Limited false positives related to third party software registering .DLL's. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/010/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md + +* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ + +* https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Rogue DHCP Server|detect-rogue-dhcp-server}}=== +By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557/ T1557] +* '''Last Updated''': 2020-08-11 + +
+
+ +====Search==== +`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" +| stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_rogue_dhcp_server_filter` + +====Associated Analytic Story==== + +* Router and Infrastructure Security + + +====How To Implement==== +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1200 +| Hardware Additions +| Initial Access +|- +| T1498 +| Network Denial of Service +| Impact +|- +| T1557 +| Man-in-the-Middle +| Collection, Credential Access +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Delivery + +* Actions on Objectives + + +====Known False Positives==== +This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Rundll32 Application Control Bypass - advpack|detect-rundll32-application-control-bypass---advpack}}=== +The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''Last Updated''': 2021-02-04 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_rundll32_application_control_bypass___advpack_filter` + +====Associated Analytic Story==== + +* Suspicious Rundll32 Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/011/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + +* https://lolbas-project.github.io/lolbas/Libraries/Advpack/ + +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Rundll32 Application Control Bypass - setupapi|detect-rundll32-application-control-bypass---setupapi}}=== +The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''Last Updated''': 2021-02-04 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_rundll32_application_control_bypass___setupapi_filter` + +====Associated Analytic Story==== + +* Suspicious Rundll32 Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, some legitimate applications may use setupapi triggering a false positive. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/011/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + +* https://lolbas-project.github.io/lolbas/Libraries/Setupapi/ + +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Rundll32 Application Control Bypass - syssetup|detect-rundll32-application-control-bypass---syssetup}}=== +The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''Last Updated''': 2021-02-04 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_rundll32_application_control_bypass___syssetup_filter` + +====Associated Analytic Story==== + +* Suspicious Rundll32 Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/011/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + +* https://lolbas-project.github.io/lolbas/Libraries/Syssetup/ + +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Rundll32 Inline HTA Execution|detect-rundll32-inline-hta-execution}}=== +The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.005/ T1218.005] +* '''Last Updated''': 2021-01-20 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_rundll32_inline_hta_execution_filter` + +====Associated Analytic Story==== + +* Suspicious MSHTA Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +====Reference==== + +* https://github.com/redcanaryco/AtomicTestHarnesses + +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect S3 access from a new IP|detect-s3-access-from-a-new-ip}}=== +This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2018-06-28 + +
+
+ +====Search==== +`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 +| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip +| inputlookup append=t previously_seen_S3_access_from_remote_ip.csv +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip +| outputlookup previously_seen_S3_access_from_remote_ip.csv +| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newIP=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table bucket_name remote_ip] +| iplocation remote_ip +|rename remote_ip as src_ip +| table _time bucket_name src_ip City Country operation request_uri +| `detect_s3_access_from_a_new_ip_filter` + +====Associated Analytic Story==== + +* Suspicious AWS S3 Activities + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect SNICat SNI Exfiltration|detect-snicat-sni-exfiltration}}=== +This search looks for commands that the SNICat tool uses in the TLS SNI field. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1041/ T1041] +* '''Last Updated''': 2020-10-21 + +
+
+ +====Search==== +`zeek_ssl` +| rex field=server_name "(?(LIST +|LS +|SIZE +|LD +|CB +|CD +|EX +|ALIVE +|EXIT +|WHERE +|finito)-[A-Za-z0-9]{16}\.)" +| stats count by src_ip dest_ip server_name snicat +| where count>0 +| table src_ip dest_ip server_name snicat +| `detect_snicat_sni_exfiltration_filter` + +====Associated Analytic Story==== + +* Data Exfiltration + + +====How To Implement==== +You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1041 +| Exfiltration Over C2 Channel +| Exfiltration +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Unknown + +====Reference==== + +* https://www.mnemonic.no/blog/introducing-snicat/ + +* https://github.com/mnemonic-no/SNIcat + +* https://attack.mitre.org/techniques/T1041/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Software Download To Network Device|detect-software-download-to-network-device}}=== +Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1542.005/ T1542.005] +* '''Last Updated''': 2020-10-28 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| `drop_dm_object_name("All_Traffic")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_software_download_to_network_device_filter` + +====Associated Analytic Story==== + +* Router and Infrastructure Security + + +====How To Implement==== +This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1542.005 +| TFTP Boot +| Defense Evasion, Persistence +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Spike in AWS Security Hub Alerts for EC2 Instance|detect-spike-in-aws-security-hub-alerts-for-ec2-instance}}=== +This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance +| bucket span=4h _time +| stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest +| eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev +| eval threshold_value = 3 +| eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) +| search isOutlier=1 +| table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg +| `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` + +====Associated Analytic Story==== + +* AWS Security Hub Alerts + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +None + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Detect Spike in AWS Security Hub Alerts for User|detect-spike-in-aws-security-hub-alerts-for-user}}=== +This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser +| rename findings{}.Resources{}.Id as user +| bucket span=4h _time +| stats count AS alerts by _time user +| eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev +| eval threshold_value = 2 +| eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) +| search isOutlier=1 +| table _time user alerts +|`detect_spike_in_aws_security_hub_alerts_for_user_filter` + +====Associated Analytic Story==== + +* AWS Security Hub Alerts + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +None + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Detect Spike in S3 Bucket deletion|detect-spike-in-s3-bucket-deletion}}=== +This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2018-11-27 + +
+
+ +====Search==== +`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup s3_deletion_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup s3_deletion_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| spath output=bucketName path=requestParameters.bucketName +| stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_s3_bucket_deletion_filter` + +====Associated Analytic Story==== + +* Suspicious AWS S3 Activities + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Spike in blocked Outbound Traffic from your AWS|detect-spike-in-blocked-outbound-traffic-from-your-aws}}=== +This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2018-05-07 + +
+
+ +====Search==== +`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) +| stats count as numberOfBlockedConnections by src_ip +| inputlookup baseline_blocked_outbound_connections append=t +| fields - latestCount +| stats values(*) as * by src_ip +| rename numberOfBlockedConnections as latestCount +| eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 +| eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) +| eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections +| outputlookup baseline_blocked_outbound_connections +| eval dataPointThreshold = 5, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| table src_ip] +| stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip +| `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` + +====Associated Analytic Story==== + +* AWS Network ACL Activity + +* Suspicious AWS Traffic + +* Command and Control + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Known False Positives==== +The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Traffic Mirroring|detect-traffic-mirroring}}=== +Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1020.001/ T1020.001] +* '''Last Updated''': 2020-10-28 + +
+
+ +====Search==== +`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") +| stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_traffic_mirroring_filter` + +====Associated Analytic Story==== + +* Router and Infrastructure Security + + +====How To Implement==== +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1200 +| Hardware Additions +| Initial Access +|- +| T1498 +| Network Denial of Service +| Impact +|- +| T1020.001 +| Traffic Duplication +| Exfiltration +|} + +====Kill Chain Phase==== + +* Delivery + +* Actions on Objectives + + +====Known False Positives==== +This search will return false positives for any legitimate traffic captures by network administrators. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Unauthorized Assets by MAC address|detect-unauthorized-assets-by-mac-address}}=== +By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Sessions +* '''ATT&CK''': +* '''Last Updated''': 2017-09-13 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac +| dedup All_Sessions.dest_mac +| `drop_dm_object_name("Network_Sessions")` +|`drop_dm_object_name("All_Sessions")` +| search NOT [ +| inputlookup asset_lookup_by_str +|rename mac as dest_mac +| fields + dest_mac] +| `detect_unauthorized_assets_by_mac_address_filter` + +====Associated Analytic Story==== + +* Asset Tracking + + +====How To Implement==== +This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Delivery + +* Actions on Objectives + + +====Known False Positives==== +This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Use of cmd exe to Launch Script Interpreters|detect-use-of-cmd-exe-to-launch-script-interpreters}}=== +This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.003/ T1059.003] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` + +====Associated Analytic Story==== + +* Emotet Malware DHS Report TA18-201A + +* Suspicious Command-Line Executions + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.003 +| Windows Command Shell +| Execution +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Some legitimate applications may exhibit this behavior. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/cmd_spawns_cscript/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Detect Windows DNS SIGRed via Splunk Stream|detect-windows-dns-sigred-via-splunk-stream}}=== +This search detects SIGRed via Splunk Stream. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203] +* '''Last Updated''': 2020-07-28 + +
+
+ +====Search==== +`stream_dns` +| spath "query_type{}" +| search "query_type{}" IN (SIG,KEY) +| spath protocol_stack +| search protocol_stack="ip:tcp:dns" +| append [search `stream_tcp` bytes_out>65000] +| `detect_windows_dns_sigred_via_splunk_stream_filter` +| stats count by flow_id +| where count>1 +| fields - count + +====Associated Analytic Story==== + +* Windows DNS SIGRed CVE-2020-1350 + + +====How To Implement==== +You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1203 +| Exploitation for Client Execution +| Execution +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +unknown + +====Reference==== + +* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Windows DNS SIGRed via Zeek|detect-windows-dns-sigred-via-zeek}}=== +This search detects SIGRed via Zeek DNS and Zeek Conn data. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203] +* '''Last Updated''': 2020-07-28 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id +| rename DNS.flow_id as flow_id +| append [ +| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id +| rename All_Traffic.flow_id as flow_id] +| `detect_windows_dns_sigred_via_zeek_filter` +| stats count by flow_id +| where count>1 +| fields - count + +====Associated Analytic Story==== + +* Windows DNS SIGRed CVE-2020-1350 + + +====How To Implement==== +You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The Network Resolution and Network Traffic datamodels are in use for this search. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1203 +| Exploitation for Client Execution +| Execution +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +unknown + +====Reference==== + +* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect Zerologon via Zeek|detect-zerologon-via-zeek}}=== +This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1190/ T1190] +* '''Last Updated''': 2020-09-15 + +
+
+ +====Search==== +`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) +| bin span=5m _time +| stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip +| search opscount=3 authcount>4 passcount>0 +| search `detect_zerologon_via_zeek_filter` + +====Associated Analytic Story==== + +* Detect Zerologon Attack + + +====How To Implement==== +You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json. These three operations are then correlated on the Zeek UID field. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +unknown + +====Reference==== + +* https://www.secura.com/blog/zero-logon + +* https://github.com/SecuraBV/CVE-2020-1472 + +* https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472 + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect hosts connecting to dynamic domain providers|detect-hosts-connecting-to-dynamic-domain-providers}}=== +Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1189/ T1189] +* '''Last Updated''': 2021-01-14 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host +| `drop_dm_object_name("DNS")` +| `security_content_ctime(firstTime)` +| `dynamic_dns_providers` +| `detect_hosts_connecting_to_dynamic_domain_providers_filter` + +====Associated Analytic Story==== + +* Data Protection + +* Prohibited Traffic Allowed or Protocol Mismatch + +* DNS Hijacking + +* Suspicious DNS Traffic + +* Dynamic DNS + +* Command and Control + + +====How To Implement==== +First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ +This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +1. \ +1. **Label:** DNS Answer, **Field:** answer\ +1. \ +1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1189 +| Drive-by Compromise +| Initial Access +|} + +====Kill Chain Phase==== + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Detect mshta inline hta execution|detect-mshta-inline-hta-execution}}=== +The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.005/ T1218.005] +* '''Last Updated''': 2021-01-20 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mshta_inline_hta_execution_filter` + +====Associated Analytic Story==== + +* Suspicious MSHTA Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +====Reference==== + +* https://github.com/redcanaryco/AtomicTestHarnesses + +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|Detect mshta renamed|detect-mshta-renamed}}=== +The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.005/ T1218.005] +* '''Last Updated''': 2021-01-20 + +
+
+ +====Search==== +`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mshta_renamed_filter` + +====Associated Analytic Story==== + +* Suspicious MSHTA Activity + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive. + +====Reference==== + +* https://github.com/redcanaryco/AtomicTestHarnesses + +* https://redcanary.com/blog/introducing-atomictestharnesses/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Detect processes used for System Network Configuration Discovery|detect-processes-used-for-system-network-configuration-discovery}}=== +This search looks for fast execution of processes used for system network configuration discovery on the endpoint. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1016/ T1016] +* '''Last Updated''': 2020-11-10 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| search `system_network_configuration_discovery_tools` +| transaction dest connected=false maxpause=5m +|where eventcount>=5 +| table firstTime lastTime dest user process_name process parent_process eventcount +| `detect_processes_used_for_system_network_configuration_discovery_filter` + +====Associated Analytic Story==== + +* Unusual Processes + + +====How To Implement==== +You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1016 +| System Network Configuration Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Installation + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/discovery_commands/windows-sysmon.log + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Disabling Remote User Account Control|disabling-remote-user-account-control}}=== +The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1548.002/ T1548.002] +* '''Last Updated''': 2020-11-18 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action +| `drop_dm_object_name(Registry)` +| `disabling_remote_user_account_control_filter` + +====Associated Analytic Story==== + +* Windows Defense Evasion Tactics + +* Suspicious Windows Registry Activities + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1548.002 +| Bypass User Account Control +| Defense Evasion, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Dump LSASS via comsvcs DLL|dump-lsass-via-comsvcs-dll}}=== +Detect the usage of comsvcs.dll for dumping the lsass process. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] +* '''Last Updated''': 2020-02-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `dump_lsass_via_comsvcs_dll_filter` + +====Associated Analytic Story==== + +* Credential Dumping + +* Suspicious Rundll32 Activity + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/ + +* https://twitter.com/SBousseaden/status/1167417096374050817 + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Dump LSASS via procdump|dump-lsass-via-procdump}}=== +Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\ +During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] +* '''Last Updated''': 2021-02-01 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `dump_lsass_via_procdump_filter` + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://attack.mitre.org/techniques/T1003/001/ + +* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Dump LSASS via procdump Rename|dump-lsass-via-procdump-rename}}=== +Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\ +During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] +* '''Last Updated''': 2021-02-01 + +
+
+ +====Search==== +`sysmon` OriginalFileName=procdump process_name!=procdump*.exe EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `dump_lsass_via_procdump_rename_filter` + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://attack.mitre.org/techniques/T1003/001/ + +* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Email files written outside of the Outlook directory|email-files-written-outside-of-the-outlook-directory}}=== +The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.001/ T1114.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest +| `drop_dm_object_name("Filesystem")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `email_files_written_outside_of_the_outlook_directory_filter` + +====Associated Analytic Story==== + +* Collection and Staging + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.001 +| Local Email Collection +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Execution of File with Multiple Extensions|execution-of-file-with-multiple-extensions}}=== +This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036.003/ T1036.003] +* '''Last Updated''': 2020-11-18 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `execution_of_file_with_multiple_extensions_filter` + +====Associated Analytic Story==== + +* Windows File Extension and Association Abuse + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|File with Samsam Extension|file-with-samsam-extension}}=== +The search looks for file writes with extensions consistent with a SamSam ransomware attack. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': +* '''Last Updated''': 2018-12-14 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| rex field=file_name "(?\.[^\.]+)$" +| search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml +| `file_with_samsam_extension_filter` + +====Associated Analytic Story==== + +* SamSam Ransomware + + +====How To Implement==== +You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Installation + + +====Known False Positives==== +Because these extensions are not typically used in normal operations, you should investigate all results. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/samsam_extension/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|First Time Seen Child Process of Zoom|first-time-seen-child-process-of-zoom}}=== +This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068] +* '''Last Updated''': 2020-05-20 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest +| `drop_dm_object_name(Processes)` +| lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen +| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") +| `security_content_ctime(firstTime)` +| table firstTime dest, process_id, process_name, parent_process_id, parent_process_name +|`first_time_seen_child_process_of_zoom_filter` + +====Associated Analytic Story==== + +* Suspicious Zoom Child Processes + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/zoom_child_process/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|First time seen command line argument|first-time-seen-command-line-argument}}=== +This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059/ T1059], [https://attack.mitre.org/techniques/T1117/ T1117], [https://attack.mitre.org/techniques/T1202/ T1202] +* '''Last Updated''': 2021-2-1 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), cmd_line_norm=lower(cmd_line), cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/, "GUID"), cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s +|$)+)/, "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s +|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N") +| where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true +| select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id +| first_time_event input_columns=["cmd_line_norm"] +| where first_time_cmd_line_norm +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be populating the endpoint data model for SSA and specifically the process_name and the process fields + +====Required field==== + +* process_name + +* _time + +* dest_device_id + +* dest_user_id + +* process + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059 +| Command and Scripting Interpreter +| Execution +|- +| +| +| +|- +| T1202 +| Indirect Command Execution +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Hiding Files And Directories With Attrib exe|hiding-files-and-directories-with-attrib-exe}}=== +Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1222.001/ T1222.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `hiding_files_and_directories_with_attrib_exe_filter` + +====Associated Analytic Story==== + +* Windows Defense Evasion Tactics + +* Windows Persistence Techniques + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1222.001 +| Windows File and Directory Permissions Modification +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Some applications and users may legitimately use attrib.exe to interact with the files. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|High Number of Login Failures from a single source|high-number-of-login-failures-from-a-single-source}}=== +This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1110.001/ T1110.001] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory +| stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app +| search accounts_locked >= 5 +| `high_number_of_login_failures_from_a_single_source_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1110.001 +| Password Guessing +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +unknown + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Illegal Access To User Content via PowerSploit modules|illegal-access-to-user-content-via-powersploit-modules}}=== +This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021/ T1021], [https://attack.mitre.org/techniques/T1113/ T1113], [https://attack.mitre.org/techniques/T1123/ T1123], [https://attack.mitre.org/techniques/T1563/ T1563] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-HttpStatus/)=true OR match_regex(cmd_line, /(?i)Get-Keystrokes/)=true OR match_regex(cmd_line, /(?i)Get-MicrophoneAudio/)=true OR match_regex(cmd_line, /(?i)Get-NetRDPSession/)=true OR match_regex(cmd_line, /(?i)Get-TimedScreenshot/)=true OR match_regex(cmd_line, /(?i)Get-WebConfig/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021 +| Remote Services +| Lateral Movement +|- +| T1113 +| Screen Capture +| Collection +|- +| T1123 +| Audio Capture +| Collection +|- +| T1563 +| Remote Service Session Hijacking +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Illegal Account Creation via PowerSploit modules|illegal-account-creation-via-powersploit-modules}}=== +This detection identifies access to PowerSploit modules that create accounts illegaly. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1585/ T1585] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)New-DomainUser/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1585 +| Establish Accounts +| Resource Development +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Illegal Deletion of Logs via Mimikatz modules|illegal-deletion-of-logs-via-mimikatz-modules}}=== +This detection identifies access to PowerSploit modules that delete event logs. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1070/ T1070] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)event::drop/)=true OR match_regex(cmd_line, /(?i)event::clear/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070 +| Indicator Removal on Host +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Illegal Enabling or Disabling of Accounts via DSInternals modules|illegal-enabling-or-disabling-of-accounts-via-dsinternals-modules}}=== +This detection identifies use of DSInternals modules that enable or disable accounts illegaly. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Disable-ADDBAccount/)=true OR match_regex(cmd_line, /(?i)Enable-ADDBAccount/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1098 +| Account Manipulation +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/MichaelGrafnetter/DSInternals + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Illegal Management of Active Directory Elements and Policies via DSInternals modules|illegal-management-of-active-directory-elements-and-policies-via-dsinternals-modules}}=== +This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1207/ T1207], [https://attack.mitre.org/techniques/T1484/ T1484] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Remove-ADDBObject/)=true OR match_regex(cmd_line, /(?i)Set-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Set-ADDBPrimaryGroup/)=true OR match_regex(cmd_line, /(?i)Set-LsaPolicyInformation/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1098 +| Account Manipulation +| Persistence +|- +| T1207 +| Rogue Domain Controller +| Defense Evasion +|- +| T1484 +| Domain Policy Modification +| Defense Evasion, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/MichaelGrafnetter/DSInternals + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Illegal Management of Computers and Active Directory Elements via PowerSploit modules|illegal-management-of-computers-and-active-directory-elements-via-powersploit-modules}}=== +This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1207/ T1207], [https://attack.mitre.org/techniques/T1484/ T1484] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainObject/)=true OR match_regex(cmd_line, /(?i)Set-ADObject/)=true OR match_regex(cmd_line, /(?i)Set-DomainObjectOwner/)=true OR match_regex(cmd_line, /(?i)Set-MasterBootRecord/)=true ) + + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1098 +| Account Manipulation +| Persistence +|- +| T1207 +| Rogue Domain Controller +| Defense Evasion +|- +| T1484 +| Domain Policy Modification +| Defense Evasion, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Illegal Privilege Elevation and Persistence via PowerSploit modules|illegal-privilege-elevation-and-persistence-via-powersploit-modules}}=== +This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1053/ T1053], [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1548/ T1548] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Add-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Enable-Privilege/)=true OR match_regex(cmd_line, /(?i)New-ElevatedPersistenceOption/)=true OR match_regex(cmd_line, /(?i)New-UserPersistenceOption/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1053 +| Scheduled Task/Job +| Execution, Persistence, Privilege Escalation +|- +| T1134 +| Access Token Manipulation +| Defense Evasion, Privilege Escalation +|- +| T1548 +| Abuse Elevation Control Mechanism +| Defense Evasion, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Illegal Privilege Elevation via Mimikatz modules|illegal-privilege-elevation-via-mimikatz-modules}}=== +This detection identifies use of Mimikatz modules for illegal privilege elevation. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1548/ T1548] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)privilege::debug/)=true OR match_regex(cmd_line, /(?i)token::elevate/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1134 +| Access Token Manipulation +| Defense Evasion, Privilege Escalation +|- +| T1548 +| Abuse Elevation Control Mechanism +| Defense Evasion, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Illegal Service and Process Control via Mimikatz modules|illegal-service-and-process-control-via-mimikatz-modules}}=== +This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1106/ T1106], [https://attack.mitre.org/techniques/T1569/ T1569] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::start/)=true OR match_regex(cmd_line, /(?i)service::\+/)=true OR match_regex(cmd_line, /(?i)service::\-/)=true OR match_regex(cmd_line, /(?i)service::start/)=true OR match_regex(cmd_line, /(?i)service::stop/)=true OR match_regex(cmd_line, /(?i)service::suspend/)=true OR match_regex(cmd_line, /(?i)misc::memssp/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1055 +| Process Injection +| Defense Evasion, Privilege Escalation +|- +| T1106 +| Native API +| Execution +|- +| T1569 +| System Services +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Illegal Service and Process Control via PowerSploit modules|illegal-service-and-process-control-via-powersploit-modules}}=== +This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1106/ T1106], [https://attack.mitre.org/techniques/T1569/ T1569] +* '''Last Updated''': 2020-11-09 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Install-SSP/)=true OR match_regex(cmd_line, /(?i)Set-CriticalProcess/)=true OR match_regex(cmd_line, /(?i)Install-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Restore-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Write-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Set-ServiceBinaryPath/)=true OR match_regex(cmd_line, /(?i)Invoke-ReflectivePEInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-DllInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-ServiceAbuse/)=true OR match_regex(cmd_line, /(?i)Invoke-Shellcode/)=true OR match_regex(cmd_line, /(?i)Invoke-WScriptUACBypass/)=true OR match_regex(cmd_line, /(?i)Invoke-WmiCommand/)=true OR match_regex(cmd_line, /(?i)Write-HijackDll/)=true OR match_regex(cmd_line, /(?i)Add-ServiceDacl/)=true ) + + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1055 +| Process Injection +| Defense Evasion, Privilege Escalation +|- +| T1106 +| Native API +| Execution +|- +| T1569 +| System Services +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Kerberoasting spn request with RC4 encryption|kerberoasting-spn-request-with-rc4-encryption}}=== +This search detects a potential kerberoasting attack via service principal name requests + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1558.003/ T1558.003] +* '''Last Updated''': 2020-10-16 + +
+
+ +====Search==== +`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 +| stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `kerberoasting_spn_request_with_rc4_encryption_filter` + +====Associated Analytic Story==== + +* Lateral Movement + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1558.003 +| Kerberoasting +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Older systems that support kerberos RC4 by default NetApp may generate false positives + +====Reference==== + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md + +* https://www.trimarcsecurity.com/post/trimarcresearch-detecting-kerberoasting-activity + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-security.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Malicious PowerShell Process - Connect To Internet With Hidden Window|malicious-powershell-process---connect-to-internet-with-hidden-window}}=== +This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.001/ T1059.001] +* '''Last Updated''': 2020-11-20 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` + +====Associated Analytic Story==== + +* Malicious PowerShell + +* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|} + +====Kill Chain Phase==== + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +Legitimate process can have this combination of command-line options, but it's not common. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/hidden_powershell/windows-sysmon.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|Malicious PowerShell Process - Encoded Command|malicious-powershell-process---encoded-command}}=== +This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1027/ T1027] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `malicious_powershell_process___encoded_command_filter` + +====Associated Analytic Story==== + +* Malicious PowerShell + +* Sunburst Malware + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1027 +| Obfuscated Files or Information +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +System administrators may use this option, but it's not common. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Malicious PowerShell Process - Execution Policy Bypass|malicious-powershell-process---execution-policy-bypass}}=== +This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.001/ T1059.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `malicious_powershell_process___execution_policy_bypass_filter` + +====Associated Analytic Story==== + +* DHS Report TA18-074A + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|} + +====Kill Chain Phase==== + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Malicious PowerShell Process With Obfuscation Techniques|malicious-powershell-process-with-obfuscation-techniques}}=== +This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.001/ T1059.001] +* '''Last Updated''': 2021-01-19 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) +| `malicious_powershell_process_with_obfuscation_techniques_filter` +| search num_obfuscation > 10 + +====Associated Analytic Story==== + +* Malicious PowerShell + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|} + +====Kill Chain Phase==== + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +These characters might be legitimately on the command-line, but it is not common. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/obfuscated_powershell/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Monitor Registry Keys for Print Monitors|monitor-registry-keys-for-print-monitors}}=== +This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1547.010/ T1547.010] +* '''Last Updated''': 2020-11-23 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action +| `drop_dm_object_name(Registry)` +| `monitor_registry_keys_for_print_monitors_filter` + +====Associated Analytic Story==== + +* Suspicious Windows Registry Activities + +* Windows Persistence Techniques + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1547.010 +| Port Monitors +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +You will encounter noise from legitimate print-monitor registry entries. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.010/atomic_red_team/windows-sysmon.log + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|More than usual number of LOLBAS applications in short time period|more-than-usual-number-of-lolbas-applications-in-short-time-period}}=== +Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059/ T1059], [https://attack.mitre.org/techniques/T1053/ T1053] +* '''Last Updated''': 2020-08-25 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() +| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" +| stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) +| eval lolbas_counter=lolbas_counter*1.0 +| rename window_end as timestamp +| adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L +| where label AND quantile>0.99 +| eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +Collect endpoint data such as sysmon or 4688 events. + +====Required field==== + +* dest_device_id + +* _time + +* process_name + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059 +| Command and Scripting Interpreter +| Execution +|- +| T1053 +| Scheduled Task/Job +| Execution, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Some administrative tasks may involve multiple use of LOLBAS applications in a short period of time. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. + + +====Reference==== + +* https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Multiple Okta Users With Invalid Credentials From The Same IP|multiple-okta-users-with-invalid-credentials-from-the-same-ip}}=== +This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`okta` outcome.reason=INVALID_CREDENTIALS +| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city +| stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search distinct_users > 5 +| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` + +====Associated Analytic Story==== + +* Suspicious Okta Activity + + +====How To Implement==== +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|NLTest Domain Trust Discovery|nltest-domain-trust-discovery}}=== +This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1482/ T1482] +* '''Last Updated''': 2021-01-25 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `nltest_domain_trust_discovery_filter` + +====Associated Analytic Story==== + +* Ryuk Ransomware + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1482 +| Domain Trust Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Administrators may use nltest for troubleshooting purposes, otherwise, rarely used. + +====Reference==== + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md + +* https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104 + +* https://attack.mitre.org/techniques/T1482/ + +* https://www.owasp.org/images/4/4b/Red_Team_Operating_in_a_Modern_Environment.pdf + +* https://ss64.com/nt/nltest.html + +* https://redcanary.com/threat-detection-report/techniques/domain-trust-discovery/ + +* https://thedfirreport.com/2020/10/08/ryuks-return/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|New container uploaded to AWS ECR|new-container-uploaded-to-aws-ecr}}=== +This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1525/ T1525] +* '''Last Updated''': 2020-02-20 + +
+
+ +====Search==== + +| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type +| `drop_dm_object_name("Compute")` +| `new_container_uploaded_to_aws_ecr_filter` + +====Associated Analytic Story==== + +* Container Implantation Monitoring and Investigation + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1525 +| Implant Container Image +| Persistence +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +Uploading container is a normal behavior from developers or users with access to container registry. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Ntdsutil export ntds|ntdsutil-export-ntds}}=== +Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \ +ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ +This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.003/ T1003.003] +* '''Last Updated''': 2021-01-28 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `ntdsutil_export_ntds_filter` + +====Associated Analytic Story==== + +* Credential Dumping + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives. + +====Reference==== + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil + +* https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11) + +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +* https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|O365 Add App Role Assignment Grant User|o365-add-app-role-assignment-grant-user}}=== +This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." +| stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_add_app_role_assignment_grant_user_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + +* Cloud Federated Credential Abuse + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.003 +| Cloud Account +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. + +====Reference==== + +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|O365 Added Service Principal|o365-added-service-principal}}=== +This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." +| stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_added_service_principal_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + +* Cloud Federated Credential Abuse + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.003 +| Cloud Account +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. + +====Reference==== + +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + +* https://www.sygnia.co/golden-saml-advisory + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|O365 Bypass MFA via Trusted IP|o365-bypass-mfa-via-trusted-ip}}=== +This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== +`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy +| rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" +| rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" +| eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") +| mvexpand ip_addresses_new_added +| where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) +|stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account status user_id action +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_bypass_mfa_via_trusted_ip_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== +You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration. + +====Reference==== + +* https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf + +* https://attack.mitre.org/techniques/T1562/007/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|O365 Disable MFA|o365-disable-mfa}}=== +This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1556/ T1556] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Operation="Disable Strong Authentication." +| stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `o365_disable_mfa_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1556 +| Modify Authentication Process +| Credential Access, Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +Unless it is a special case, it is uncommon to disable MFA or Strong Authentication + +====Reference==== + +* https://attack.mitre.org/techniques/T1556/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|O365 Excessive Authentication Failures Alert|o365-excessive-authentication-failures-alert}}=== +This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1110/ T1110] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed +| stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user +| where count > 10 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `o365_excessive_authentication_failures_alert_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1110 +| Brute Force +| Credential Access +|} + +====Kill Chain Phase==== + +* Not Applicable + + +====Known False Positives==== +The threshold for alert is above 10 attempts and this should reduce the number of false positives. + +====Reference==== + +* https://attack.mitre.org/techniques/T1110/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|O365 Excessive SSO logon errors|o365-excessive-sso-logon-errors}}=== +This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1556/ T1556] +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired +| stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId +| where count > 5 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_excessive_sso_logon_errors_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + +* Cloud Federated Credential Abuse + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1556 +| Modify Authentication Process +| Credential Access, Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack. + +====Reference==== + +* https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|O365 New Federated Domain Added|o365-new-federated-domain-added}}=== +This search detects the addition of a new Federated domain. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" +| stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_new_federated_domain_added_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + +* Cloud Federated Credential Abuse + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.003 +| Cloud Account +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider. + +====Reference==== + +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + +* https://www.sygnia.co/golden-saml-advisory + +* https://o365blog.com/post/aadbackdoor/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|O365 PST export alert|o365-pst-export-alert}}=== +This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114/ T1114] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" +| stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `o365_pst_export_alert_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114 +| Email Collection +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored. + +====Reference==== + +* https://attack.mitre.org/techniques/T1114/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|O365 Suspicious Admin Email Forwarding|o365-suspicious-admin-email-forwarding}}=== +This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.003/ T1114.003] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Operation=Set-Mailbox +| spath input=Parameters +| rename Identity AS src_user +| search ForwardingAddress=* +| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress +| where count_src_user > 1 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`o365_suspicious_admin_email_forwarding_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.003 +| Email Forwarding Rule +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +unknown + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|O365 Suspicious Rights Delegation|o365-suspicious-rights-delegation}}=== +This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.002/ T1114.002] +* '''Last Updated''': 2020-12-15 + +
+
+ +====Search==== +`o365_management_activity` Operation=Add-MailboxPermission +| spath input=Parameters +| rename User AS src_user, Identity AS dest_user +| search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf +| stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`o365_suspicious_rights_delegation_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.002 +| Remote Email Collection +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Service Accounts + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|O365 Suspicious User Email Forwarding|o365-suspicious-user-email-forwarding}}=== +This search detects when multiple user configured a forwarding rule to the same destination. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.003/ T1114.003] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Operation=Set-Mailbox +| spath input=Parameters +| rename Identity AS src_user +| search ForwardingSmtpAddress=* +| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress +| where count_src_user > 1 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`o365_suspicious_user_email_forwarding_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.003 +| Email Forwarding Rule +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +unknown + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Okta Account Lockout Events|okta-account-lockout-events}}=== +Detect Okta user lockout events + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`okta` displayMessage="Max sign in attempts exceeded" +| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city +| table _time, user, country, state, city, src_ip +| `okta_account_lockout_events_filter` + +====Associated Analytic Story==== + +* Suspicious Okta Activity + + +====How To Implement==== +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Okta Failed SSO Attempts|okta-failed-sso-attempts}}=== +Detect failed Okta SSO events + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`okta` displayMessage="User attempted unauthorized access to app" +| stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `okta_failed_sso_attempts_filter` + +====Associated Analytic Story==== + +* Suspicious Okta Activity + + +====How To Implement==== +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +There may be a faulty config preventing legitmate users from accessing apps they should have access to. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Okta User Logins From Multiple Cities|okta-user-logins-from-multiple-cities}}=== +This search detects logins from the same user from different cities in a 24 hour period. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null +| stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `okta_user_logins_from_multiple_cities_filter` +| search locations > 1 + +====Associated Analytic Story==== + +* Suspicious Okta Activity + + +====How To Implement==== +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Overwriting Accessibility Binaries|overwriting-accessibility-binaries}}=== +Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.008/ T1546.008] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `overwriting_accessibility_binaries_filter` + +====Associated Analytic Story==== + +* Windows Privilege Escalation + + +====How To Implement==== +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.008 +| Accessibility Features +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.008/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Probing Access with Stolen Credentials via PowerSploit modules|probing-access-with-stolen-credentials-via-powersploit-modules}}=== +This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] +* '''Last Updated''': 2020-11-04 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-AdminAccess/)=true OR match_regex(cmd_line, /(?i)Invoke-CheckLocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Test-ServiceDaclPermission/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_user_id + +* dest_device_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1098 +| Account Manipulation +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Process Creating LNK file in Suspicious Location|process-creating-lnk-file-in-suspicious-location}}=== +This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1566.002/ T1566.002] +* '''Last Updated''': 2021-01-28 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user +| `drop_dm_object_name(Filesystem)` +| rename process_id as lnk_pid +| join lnk_pid, _time [ +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process +| `drop_dm_object_name(Processes)` +| rename parent_process_id as lnk_pid +| fields _time lnk_pid process_id dest process_name process_path process] +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash +| `process_creating_lnk_file_in_suspicious_location_filter` + +====Associated Analytic Story==== + +* Phishing Payloads + + +====How To Implement==== +You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566.002 +| Spearphishing Link +| Initial Access +|} + +====Kill Chain Phase==== + +* Installation + +* Actions on Objectives + + +====Known False Positives==== +This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories. + +====Reference==== + +* https://attack.mitre.org/techniques/T1566/001/ + +* https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Process Execution via WMI|process-execution-via-wmi}}=== +This search looks for processes launched via WMI. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1047/ T1047] +* '''Last Updated''': 2020-03-16 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `process_execution_via_wmi_filter` + +====Associated Analytic Story==== + +* Suspicious WMI Use + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, administrators may use wmi to execute commands for legitimate purposes. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Processes launching netsh|processes-launching-netsh}}=== +This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.004/ T1562.004] +* '''Last Updated''': 2020-07-10 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest +|`drop_dm_object_name("Processes")` +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`processes_launching_netsh_filter` + +====Associated Analytic Story==== + +* Netsh Abuse + +* Disabling Security Tools + +* DHS Report TA18-074A + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.004 +| Disable or Modify System Firewall +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/atomic_red_team/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Rare Parent-Child Process Relationship|rare-parent-child-process-relationship}}=== +An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203], [https://attack.mitre.org/techniques/T1059/ T1059], [https://attack.mitre.org/techniques/T1053/ T1053], [https://attack.mitre.org/techniques/T1072/ T1072] +* '''Last Updated''': 2020-08-13 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) +| where parent_process_name!=null +| select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id +| conditional_anomaly conditional="parent_process_name" target="process_name" +| rename output as input +| where input < 1 +| adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L +| where label AND quantile<0.1 AND (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe") + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +Collect endpoint data such as sysmon or 4688 events. + +====Required field==== + +* process_name + +* parent_process_name + +* _time + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1203 +| Exploitation for Client Execution +| Execution +|- +| T1059 +| Command and Scripting Interpreter +| Execution +|- +| T1053 +| Scheduled Task/Job +| Execution, Persistence, Privilege Escalation +|- +| T1072 +| Software Deployment Tools +| Execution, Lateral Movement +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline. + + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules|reconnaissance-and-access-to-accounts-groups-and-policies-via-powersploit-modules}}=== +This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1087/ T1087], [https://attack.mitre.org/techniques/T1484/ T1484] +* '''Last Updated''': 2020-11-05 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Invoke-EnumerateLocalAdmin/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Invoke-EventHunter/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserLocation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserHunter/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignGroupMember/)=true OR match_regex(cmd_line, /(?i)Find-ForeignGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignUser/)=true OR match_regex(cmd_line, /(?i)Find-ForeignUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPO/)=true OR match_regex(cmd_line, /(?i)Get-NetGPO/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOComputerLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOComputerAdmin/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGPOGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOUserLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOLocation/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-DomainManagedSecurityGroup/)=true OR match_regex(cmd_line, /(?i)Find-ManagedSecurityGroups/)=true OR match_regex(cmd_line, /(?i)Get-DomainOU/)=true OR match_regex(cmd_line, /(?i)Get-NetOU/)=true OR match_regex(cmd_line, /(?i)Get-DomainUser/)=true OR match_regex(cmd_line, /(?i)Get-NetUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Get-UserEvent/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetLoggedon/)=true OR match_regex(cmd_line, /(?i)Get-RegLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegLastLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-LastLoggedOn/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1087 +| Account Discovery +| Discovery +|- +| T1484 +| Domain Policy Modification +| Defense Evasion, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance and Access to Accounts and Groups via Mimikatz modules|reconnaissance-and-access-to-accounts-and-groups-via-mimikatz-modules}}=== +This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1087/ T1087], [https://attack.mitre.org/techniques/T1484/ T1484] +* '''Last Updated''': 2020-11-05 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::user/)=true OR match_regex(cmd_line, /(?i)net::group/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1087 +| Account Discovery +| Discovery +|- +| T1484 +| Domain Policy Modification +| Defense Evasion, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules|reconnaissance-and-access-to-active-directoty-infrastructure-via-powersploit-modules}}=== +This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1199/ T1199], [https://attack.mitre.org/techniques/T1482/ T1482], [https://attack.mitre.org/techniques/T1590/ T1590], [https://attack.mitre.org/techniques/T1591/ T1591], [https://attack.mitre.org/techniques/T1595/ T1595] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainSID/)=true OR match_regex(cmd_line, /(?i)Get-DomainSite/)=true OR match_regex(cmd_line, /(?i)Get-NetSite/)=true OR match_regex(cmd_line, /(?i)Get-DomainSubnet/)=true OR match_regex(cmd_line, /(?i)Get-NetSubnet/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrustMapping/)=true OR match_regex(cmd_line, /(?i)Invoke-MapDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-Forest/)=true OR match_regex(cmd_line, /(?i)Get-NetForest/)=true OR match_regex(cmd_line, /(?i)Get-ForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-NetForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-ForestGlobalCatalog/)=true OR match_regex(cmd_line, /(?i)Get-NetForestCatalog/)=true OR match_regex(cmd_line, /(?i)Get-ForestTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetForestTrust/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1199 +| Trusted Relationship +| Initial Access +|- +| T1482 +| Domain Trust Discovery +| Discovery +|- +| T1590 +| Gather Victim Network Information +| Reconnaissance +|- +| T1591 +| Gather Victim Org Information +| Reconnaissance +|- +| T1595 +| Active Scanning +| Reconnaissance +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance and Access to Computers and Domains via PowerSploit modules|reconnaissance-and-access-to-computers-and-domains-via-powersploit-modules}}=== +This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1592/ T1592], [https://attack.mitre.org/techniques/T1590/ T1590], [https://attack.mitre.org/techniques/T1087/ T1087] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ComputerDetail/)=true OR match_regex(cmd_line, /(?i)Get-Domain/)=true OR match_regex(cmd_line, /(?i)Get-NetDomain/)=true OR match_regex(cmd_line, /(?i)Get-DomainComputer/)=true OR match_regex(cmd_line, /(?i)Get-NetComputer/)=true OR match_regex(cmd_line, /(?i)Get-DomainController/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainController/)=true OR match_regex(cmd_line, /(?i)Get-DomainFileServer/)=true OR match_regex(cmd_line, /(?i)Get-NetFileServer/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1592 +| Gather Victim Host Information +| Reconnaissance +|- +| T1590 +| Gather Victim Network Information +| Reconnaissance +|- +| T1087 +| Account Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance and Access to Computers via Mimikatz modules|reconnaissance-and-access-to-computers-via-mimikatz-modules}}=== +This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1592/ T1592] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::ServerInfo/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1592 +| Gather Victim Host Information +| Reconnaissance +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance and Access to Operating System Elements via PowerSploit modules|reconnaissance-and-access-to-operating-system-elements-via-powersploit-modules}}=== +This detection identifies access to PowerSploit modules that discover and access operating system elements, such as processes, services, registry locations, security packages and files. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1007/ T1007], [https://attack.mitre.org/techniques/T1012/ T1012], [https://attack.mitre.org/techniques/T1046/ T1046], [https://attack.mitre.org/techniques/T1047/ T1047], [https://attack.mitre.org/techniques/T1057/ T1057], [https://attack.mitre.org/techniques/T1083/ T1083], [https://attack.mitre.org/techniques/T1518/ T1518], [https://attack.mitre.org/techniques/T1592.002/ T1592.002] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainProcess/)=true OR match_regex(cmd_line, /(?i)Invoke-ProcessHunter/)=true OR match_regex(cmd_line, /(?i)Get-ServiceDetail/)=true OR match_regex(cmd_line, /(?i)Get-WMIProcess/)=true OR match_regex(cmd_line, /(?i)Get-NetProcess/)=true OR match_regex(cmd_line, /(?i)Get-SecurityPackage/)=true OR match_regex(cmd_line, /(?i)Find-DomainObjectPropertyOutlier/)=true OR match_regex(cmd_line, /(?i)Get-DomainObject/)=true OR match_regex(cmd_line, /(?i)Get-ADObject/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegMountedDrive/)=true OR match_regex(cmd_line, /(?i)Get-RegistryMountedDrive/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1007 +| System Service Discovery +| Discovery +|- +| T1012 +| Query Registry +| Discovery +|- +| T1046 +| Network Service Scanning +| Discovery +|- +| T1047 +| Windows Management Instrumentation +| Execution +|- +| T1057 +| Process Discovery +| Discovery +|- +| T1083 +| File and Directory Discovery +| Discovery +|- +| T1518 +| Software Discovery +| Discovery +|- +| T1592.002 +| Software +| Reconnaissance +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance and Access to Processes and Services via Mimikatz modules|reconnaissance-and-access-to-processes-and-services-via-mimikatz-modules}}=== +This detection identifies use of Mimikatz modules for discovery and access to services and processes. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1007/ T1007], [https://attack.mitre.org/techniques/T1046/ T1046], [https://attack.mitre.org/techniques/T1057/ T1057] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::list/)=true OR match_regex(cmd_line, /(?i)service::list/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1007 +| System Service Discovery +| Discovery +|- +| T1046 +| Network Service Scanning +| Discovery +|- +| T1057 +| Process Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance and Access to Shared Resources via Mimikatz modules|reconnaissance-and-access-to-shared-resources-via-mimikatz-modules}}=== +This detection identifies use of Mimikatz modules for discovery and access to network shares. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1135/ T1135], [https://attack.mitre.org/techniques/T1039/ T1039] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::share/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|- +| T1135 +| Network Share Discovery +| Discovery +|- +| T1039 +| Data from Network Shared Drive +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance and Access to Shared Resources via PowerSploit modules|reconnaissance-and-access-to-shared-resources-via-powersploit-modules}}=== +This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1135/ T1135], [https://attack.mitre.org/techniques/T1039/ T1039] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainShare/)=true OR match_regex(cmd_line, /(?i)Invoke-ShareFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainShareFile/)=true OR match_regex(cmd_line, /(?i)Invoke-FileFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingFile/)=true OR match_regex(cmd_line, /(?i)Get-DomainDFSShare/)=true OR match_regex(cmd_line, /(?i)Get-DFSshare/)=true OR match_regex(cmd_line, /(?i)Get-NetShare/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|- +| T1135 +| Network Share Discovery +| Discovery +|- +| T1039 +| Data from Network Shared Drive +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance of Access and Persistence Opportunities via PowerSploit modules|reconnaissance-of-access-and-persistence-opportunities-via-powersploit-modules}}=== +This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1053/ T1053], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1547/ T1547], [https://attack.mitre.org/techniques/T1574/ T1574] +* '''Last Updated''': 2020-11-05 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-LocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainAcl/)=true OR match_regex(cmd_line, /(?i)Invoke-ACLScanner/)=true OR match_regex(cmd_line, /(?i)Find-PathDLLHijack/)=true OR match_regex(cmd_line, /(?i)Find-ProcessDLLHijack/)=true OR match_regex(cmd_line, /(?i)Get-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-DomainPolicy/)=true OR match_regex(cmd_line, /(?i)Get-ModifiablePath/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableRegistryAutoRun/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableScheduledTaskFile/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableService/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableServiceFile/)=true OR match_regex(cmd_line, /(?i)Get-PathAcl/)=true OR match_regex(cmd_line, /(?i)Get-UnattendedInstallFile/)=true OR match_regex(cmd_line, /(?i)Get-UnquotedService/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1053 +| Scheduled Task/Job +| Execution, Persistence, Privilege Escalation +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1543 +| Create or Modify System Process +| Persistence, Privilege Escalation +|- +| T1547 +| Boot or Logon Autostart Execution +| Persistence, Privilege Escalation +|- +| T1574 +| Hijack Execution Flow +| Defense Evasion, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance of Connectivity via PowerSploit modules|reconnaissance-of-connectivity-via-powersploit-modules}}=== +This detection identifies access to PowerSploit modules for reconnaissance of connectivity. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1135/ T1135], [https://attack.mitre.org/techniques/T1039/ T1039] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainDNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DomainDNSZone/)=true OR match_regex(cmd_line, /(?i)Get-DNSZone/)=true OR match_regex(cmd_line, /(?i)Invoke-ReverseDnsLookup/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegCachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-CachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegProxy/)=true OR match_regex(cmd_line, /(?i)Get-Proxy/)=true OR match_regex(cmd_line, /(?i)Invoke-Portscan/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|- +| T1135 +| Network Share Discovery +| Discovery +|- +| T1039 +| Data from Network Shared Drive +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance of Credential Stores and Services via Mimikatz modules|reconnaissance-of-credential-stores-and-services-via-mimikatz-modules}}=== +This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1589.001/ T1589.001], [https://attack.mitre.org/techniques/T1590.001/ T1590.001], [https://attack.mitre.org/techniques/T1590.003/ T1590.003], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] +* '''Last Updated''': 2020-11-03 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)crypto::capi/)=true OR match_regex(cmd_line, /(?i)crypto::cng/)=true OR match_regex(cmd_line, /(?i)crypto::providers/)=true OR match_regex(cmd_line, /(?i)crypto::stores/)=true OR match_regex(cmd_line, /(?i)crypto::sc/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1589.001 +| Credentials +| Reconnaissance +|- +| T1590.001 +| Domain Properties +| Reconnaissance +|- +| T1590.003 +| Network Trust Dependencies +| Reconnaissance +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1098 +| Account Manipulation +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance of Defensive Tools via PowerSploit modules|reconnaissance-of-defensive-tools-via-powersploit-modules}}=== +This detection identifies use of PowerSploit modules for assessment of presence of defensive tools. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1595.002/ T1595.002], [https://attack.mitre.org/techniques/T1592.002/ T1592.002] +* '''Last Updated''': 2020-11-05 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-AVSignature/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1595.002 +| Vulnerability Scanning +| Reconnaissance +|- +| T1592.002 +| Software +| Reconnaissance +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules|reconnaissance-of-privilege-escalation-opportunities-via-powersploit-modules}}=== +This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] +* '''Last Updated''': 2020-11-05 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-PrivescAudit/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1098 +| Account Manipulation +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules|reconnaissance-of-process-or-service-hijacking-opportunities-via-mimikatz-modules}}=== +This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1574/ T1574] +* '''Last Updated''': 2020-11-05 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::detours/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* _time + +* process + +* dest_device_id + +* dest_user_id + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1543 +| Create or Modify System Process +| Persistence, Privilege Escalation +|- +| T1055 +| Process Injection +| Defense Evasion, Privilege Escalation +|- +| T1574 +| Hijack Execution Flow +| Defense Evasion, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + +* https://en.wikipedia.org/wiki/Microsoft_Detours + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Reg exe Manipulating Windows Services Registry Keys|reg-exe-manipulating-windows-services-registry-keys}}=== +The search looks for reg.exe modifying registry keys that define Windows services and their configurations. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1574.011/ T1574.011] +* '''Last Updated''': 2020-11-26 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `reg_exe_manipulating_windows_services_registry_keys_filter` + +====Associated Analytic Story==== + +* Windows Service Abuse + +* Windows Persistence Techniques + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1574.011 +| Services Registry Permissions Weakness +| Defense Evasion, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Installation + + +====Known False Positives==== +It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|Registry Keys Used For Persistence|registry-keys-used-for-persistence}}=== +The search looks for modifications to registry keys that can be used to launch an application or service at system startup. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1547.001/ T1547.001] +* '''Last Updated''': 2020-11-27 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `registry_keys_used_for_persistence_filter` + +====Associated Analytic Story==== + +* Suspicious Windows Registry Activities + +* Suspicious MSHTA Activity + +* DHS Report TA18-074A + +* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + +* Ransomware + +* Windows Persistence Techniques + +* Emotet Malware DHS Report TA18-201A + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1547.001 +| Registry Run Keys / Startup Folder +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/windows-sysmon.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|Registry Keys Used For Privilege Escalation|registry-keys-used-for-privilege-escalation}}=== +This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.012/ T1546.012] +* '''Last Updated''': 2020-11-27 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `registry_keys_used_for_privilege_escalation_filter` + +====Associated Analytic Story==== + +* Windows Privilege Escalation + +* Suspicious Windows Registry Activities + +* Cloud Federated Credential Abuse + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.012 +| Image File Execution Options Injection +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. + +====Reference==== + +* https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Registry Keys for Creating SHIM Databases|registry-keys-for-creating-shim-databases}}=== +This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.011/ T1546.011] +* '''Last Updated''': 2020-11-26 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `registry_keys_for_creating_shim_databases_filter` + +====Associated Analytic Story==== + +* Suspicious Windows Registry Activities + +* Windows Persistence Techniques + + +====How To Implement==== +To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response product, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.011 +| Application Shimming +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Remote Desktop Network Bruteforce|remote-desktop-network-bruteforce}}=== +This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 +| where count>(avg + stdev*2) +| rename All_Traffic.src AS src All_Traffic.dest AS dest +| table firstTime lastTime src dest count avg p50 stdev +| `remote_desktop_network_bruteforce_filter` + +====Associated Analytic Story==== + +* SamSam Ransomware + +* Ryuk Ransomware + + +====How To Implement==== +You must ensure that your network traffic data is populating the Network_Traffic data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Delivery + + +====Known False Positives==== +RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Remote Desktop Network Traffic|remote-desktop-network-traffic}}=== +This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001] +* '''Last Updated''': 2020-07-07 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| `drop_dm_object_name("All_Traffic")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `remote_desktop_network_traffic_filter` + +====Associated Analytic Story==== + +* SamSam Ransomware + +* Ryuk Ransomware + +* Hidden Cobra Malware + +* Lateral Movement + + +====How To Implement==== +To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Remote Desktop may be used legitimately by users on the network. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Remote Process Instantiation via WMI|remote-process-instantiation-via-wmi}}=== +This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1047/ T1047] +* '''Last Updated''': 2020-11-30 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `remote_process_instantiation_via_wmi_filter` + +====Associated Analytic Story==== + +* Ransomware + +* Suspicious WMI Use + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|RunDLL Loading DLL By Ordinal|rundll-loading-dll-by-ordinal}}=== +This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''Last Updated''': 2020-11-30 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `rundll_loading_dll_by_ordinal_filter` + +====Associated Analytic Story==== + +* Unusual Processes + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Installation + + +====Known False Positives==== +While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Ryuk Test Files Detected|ryuk-test-files-detected}}=== +The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1486/ T1486] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `ryuk_test_files_detected_filter` + +====Associated Analytic Story==== + +* Ryuk Ransomware + + +====How To Implement==== +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1486 +| Data Encrypted for Impact +| Impact +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|SMB Traffic Spike|smb-traffic-spike}}=== +This search looks for spikes in the number of Server Message Block (SMB) traffic connections. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src +| `drop_dm_object_name("All_Traffic")` +| eventstats max(_time) as maxtime +| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) +| where isOutlier=1 +| table src count +| `smb_traffic_spike_filter` + +====Associated Analytic Story==== + +* Emotet Malware DHS Report TA18-201A + +* Hidden Cobra Malware + +* Ransomware + +* DHS Report TA18-074A + + +====How To Implement==== +This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +A file server may experience high-demand loads that could cause this analytic to trigger. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|SMB Traffic Spike - MLTK|smb-traffic-spike---mltk}}=== +This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src +| eval HourOfDay=strftime(_time, "%H") +| eval DayOfWeek=strftime(_time, "%A") +| `drop_dm_object_name(All_Traffic)` +| apply smb_pdfmodel threshold=0.001 +| rename "IsOutlier(count)" as isOutlier +| search isOutlier > 0 +| sort -count +| table _time src dest port count +| `smb_traffic_spike___mltk_filter` + +====Associated Analytic Story==== + +* Emotet Malware DHS Report TA18-201A + +* Hidden Cobra Malware + +* Ransomware + +* DHS Report TA18-074A + + +====How To Implement==== +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ +1. **Label:** Number of events, **Field:** count\ +Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Samsam Test File Write|samsam-test-file-write}}=== +The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1486/ T1486] +* '''Last Updated''': 2018-12-14 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `samsam_test_file_write_filter` + +====Associated Analytic Story==== + +* SamSam Ransomware + + +====How To Implement==== +You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1486 +| Data Encrypted for Impact +| Impact +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +No false positives have been identified. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/sam_sam_note/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Sc exe Manipulating Windows Services|sc-exe-manipulating-windows-services}}=== +This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1543.003/ T1543.003] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `sc_exe_manipulating_windows_services_filter` + +====Associated Analytic Story==== + +* Windows Service Abuse + +* DHS Report TA18-074A + +* Orangeworm Attack Group + +* Windows Persistence Techniques + +* Disabling Security Tools + +* Sunburst Malware + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1543.003 +| Windows Service +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Installation + + +====Known False Positives==== +Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Scheduled Task Deleted Or Created via CMD|scheduled-task-deleted-or-created-via-cmd}}=== +This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1053.005/ T1053.005] +* '''Last Updated''': 2020-12-17 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `scheduled_task_deleted_or_created_via_cmd_filter` + +====Associated Analytic Story==== + +* DHS Report TA18-074A + +* Sunburst Malware + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Tasks should not be manually created via CLI, this is rarely done by admins as well + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|Schtasks scheduling job on remote system|schtasks-scheduling-job-on-remote-system}}=== +This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1053.005/ T1053.005] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `schtasks_scheduling_job_on_remote_system_filter` + +====Associated Analytic Story==== + +* Lateral Movement + +* Sunburst Malware + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Schtasks used for forcing a reboot|schtasks-used-for-forcing-a-reboot}}=== +This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1053.005/ T1053.005] +* '''Last Updated''': 2020-12-07 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `schtasks_used_for_forcing_a_reboot_filter` + +====Associated Analytic Story==== + +* Windows Persistence Techniques + +* Ransomware + + +====How To Implement==== +To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_shutdown/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Script Execution via WMI|script-execution-via-wmi}}=== +This search looks for scripts launched via WMI. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1047/ T1047] +* '''Last Updated''': 2020-03-16 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `script_execution_via_wmi_filter` + +====Associated Analytic Story==== + +* Suspicious WMI Use + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/execution_scrcons/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Setting Credentials via DSInternals modules|setting-credentials-via-dsinternals-modules}}=== +This detection identifies illegal setting of credentials via DSInternals modules. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] +* '''Last Updated''': 2020-11-03 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-ADDBSidHistory/)=true OR match_regex(cmd_line, /(?i)Add-ADReplNgcKey/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPassword/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-ADDBBootKey/)=true OR match_regex(cmd_line, /(?i)Set-SamAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-AzureADUserEx/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* process_name + +* parent_process_name + +* _time + +* process_path + +* dest_user_id + +* process + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1098 +| Account Manipulation +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/MichaelGrafnetter/DSInternals + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Setting Credentials via Mimikatz modules|setting-credentials-via-mimikatz-modules}}=== +This detection identifies illegal setting of credentials via Mimikatz modules. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] +* '''Last Updated''': 2020-11-03 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::addsid/)=true OR match_regex(cmd_line, /(?i)CRYPTO::scauth/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1098 +| Account Manipulation +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/gentilkiwi/mimikatz + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Setting Credentials via PowerSploit modules|setting-credentials-via-powersploit-modules}}=== +This detection identifies illegal setting of credentials via PowerSploit modules. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] +* '''Last Updated''': 2020-11-03 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() + +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainUserPassword/)=true ) + +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging. + +====Required field==== + +* dest_device_id + +* dest_user_id + +* process + +* _time + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1098 +| Account Manipulation +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + +* https://github.com/PowerShellMafia/PowerSploit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Shim Database File Creation|shim-database-file-creation}}=== +This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.011/ T1546.011] +* '''Last Updated''': 2020-12-08 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +|`drop_dm_object_name(Filesystem)` +| `shim_database_file_creation_filter` + +====Associated Analytic Story==== + +* Windows Persistence Techniques + + +====How To Implement==== +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.011 +| Application Shimming +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Shim Database Installation With Suspicious Parameters|shim-database-installation-with-suspicious-parameters}}=== +This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.011/ T1546.011] +* '''Last Updated''': 2020-11-23 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `shim_database_installation_with_suspicious_parameters_filter` + +====Associated Analytic Story==== + +* Windows Persistence Techniques + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.011 +| Application Shimming +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Short Lived Windows Accounts|short-lived-windows-accounts}}=== +This search detects accounts that were created and deleted in a short time period. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.001/ T1136.001] +* '''Last Updated''': 2020-07-06 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name("All_Changes")` +| search result_id = 4720 result_id=4726 +| transaction user connected=false maxspan=240m +| table firstTime lastTime count user dest result_id +| `short_lived_windows_accounts_filter` + +====Associated Analytic Story==== + +* Account Monitoring and Controls + + +====How To Implement==== +This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/ + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.001 +| Local Account +| Persistence +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Single Letter Process On Endpoint|single-letter-process-on-endpoint}}=== +This search looks for process names that consist only of a single letter. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1204.002/ T1204.002] +* '''Last Updated''': 2020-12-08 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name +| `drop_dm_object_name(Processes)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) +| search process_name_length=5 AND endExe=1 +| table count, firstTime, lastTime, dest, user, process, process_name +| `single_letter_process_on_endpoint_filter` + +====Associated Analytic Story==== + +* DHS Report TA18-074A + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1204.002 +| Malicious File +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/single_letter_exe/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Suspicious MSBuild Rename|suspicious-msbuild-rename}}=== +The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1127.001/ T1127.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== +`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_msbuild_rename_filter` + +====Associated Analytic Story==== + +* Trusted Developer Utilities Proxy Execution MSBuild + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1127.001 +| MSBuild +| Defense Evasion +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive. + +====Reference==== + +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + +* https://github.com/infosecn1nja/MaliciousMacroMSBuild/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious MSBuild Spawn|suspicious-msbuild-spawn}}=== +The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1127.001/ T1127.001] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_msbuild_spawn_filter` + +====Associated Analytic Story==== + +* Trusted Developer Utilities Proxy Execution MSBuild + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1127.001 +| MSBuild +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +====Reference==== + +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious Reg exe Process|suspicious-reg-exe-process}}=== +This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1112/ T1112] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search [ +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename parent_process_id as process_id +|dedup process_id +| table process_id dest] +| `suspicious_reg_exe_process_filter` + +====Associated Analytic Story==== + +* Windows Defense Evasion Tactics + +* Disabling Security Tools + +* DHS Report TA18-074A + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1112 +| Modify Registry +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. + +====Reference==== + +* https://car.mitre.org/wiki/CAR-2013-03-001 + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Suspicious Regsvr32 Register Suspicious Path|suspicious-regsvr32-register-suspicious-path}}=== +Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.010/ T1218.010] +* '''Last Updated''': 2021-01-28 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_regsvr32_register_suspicious_path_filter` + +====Associated Analytic Story==== + +* Suspicious Regsvr32 Activity + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.010 +| Regsvr32 +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/010/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md + +* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ + +* https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 + +* https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718 + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious Rundll32 Rename|suspicious-rundll32-rename}}=== +The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] +* '''Last Updated''': 2021-02-04 + +
+
+ +====Search==== +`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_rundll32_rename_filter` + +====Associated Analytic Story==== + +* Suspicious Rundll32 Activity + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/011/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious Rundll32 StartW|suspicious-rundll32-startw}}=== +The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''Last Updated''': 2021-02-04 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_rundll32_startw_filter` + +====Associated Analytic Story==== + +* Suspicious Rundll32 Activity + +* Cobalt Strike + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/011/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + +* https://www.cobaltstrike.com/help-windows-executable + +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious Rundll32 dllregisterserver|suspicious-rundll32-dllregisterserver}}=== +The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''Last Updated''': 2021-02-09 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_rundll32_dllregisterserver_filter` + +====Associated Analytic Story==== + +* Suspicious Rundll32 Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/011/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + +* https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/seedworm-apt-iran-middle-east + +* https://github.com/pan-unit42/tweets/blob/master/2020-12-10-IOCs-from-Ursnif-infection-with-Delf-variant.txt + +* https://www.crowdstrike.com/blog/duck-hunting-with-falcon-complete-qakbot-zip-based-campaign/ + +* https://msdn.microsoft.com/en-us/library/windows/desktop/ms682162(v=vs.85).aspx + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious Rundll32 no CommandLine Arguments|suspicious-rundll32-no-commandline-arguments}}=== +The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''Last Updated''': 2021-02-09 + +
+
+ +====Search==== +`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) +| regex CommandLine="(rundll32\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_rundll32_no_commandline_arguments_filter` + +====Associated Analytic Story==== + +* Suspicious Rundll32 Activity + +* Cobalt Strike + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/011/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + +* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious microsoft workflow compiler rename|suspicious-microsoft-workflow-compiler-rename}}=== +The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1127, T1036.003/ T1127, T1036.003] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== +`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_microsoft_workflow_compiler_rename_filter` + +====Associated Analytic Story==== + +* Trusted Developer Utilities Proxy Execution + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| +| +| +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive. + +====Reference==== + +* https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious microsoft workflow compiler usage|suspicious-microsoft-workflow-compiler-usage}}=== +The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1127/ T1127] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_microsoft_workflow_compiler_usage_filter` + +====Associated Analytic Story==== + +* Trusted Developer Utilities Proxy Execution + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1127 +| Trusted Developer Utilities Proxy Execution +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM. + +====Reference==== + +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious msbuild path|suspicious-msbuild-path}}=== +The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1127.001/ T1127.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_msbuild_path_filter` + +====Associated Analytic Story==== + +* Trusted Developer Utilities Proxy Execution MSBuild + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1127.001 +| MSBuild +| Defense Evasion +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on. + +====Reference==== + +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious mshta child process|suspicious-mshta-child-process}}=== +The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.005/ T1218.005] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_mshta_child_process_filter` + +====Associated Analytic Story==== + +* Suspicious MSHTA Activity + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +====Reference==== + +* https://github.com/redcanaryco/AtomicTestHarnesses + +* https://redcanary.com/blog/introducing-atomictestharnesses/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious mshta spawn|suspicious-mshta-spawn}}=== +The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.005/ T1218.005] +* '''Last Updated''': 2021-01-20 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_mshta_spawn_filter` + +====Associated Analytic Story==== + +* Suspicious MSHTA Activity + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. + +====Reference==== + +* https://codewhitesec.blogspot.com/2018/07/lethalhta.html + +* https://github.com/redcanaryco/AtomicTestHarnesses + +* https://redcanary.com/blog/introducing-atomictestharnesses/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Suspicious wevtutil Usage|suspicious-wevtutil-usage}}=== +The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1070.001/ T1070.001] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `suspicious_wevtutil_usage_filter` + +====Associated Analytic Story==== + +* Windows Log Manipulation + +* Ransomware + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070.001 +| Clear Windows Event Logs +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Suspicious writes to windows Recycle Bin|suspicious-writes-to-windows-recycle-bin}}=== +This search detects writes to the recycle bin by a process other than explorer.exe. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036/ T1036] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest +| `drop_dm_object_name("Filesystem")` +| search [ +| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest +| `drop_dm_object_name("Processes")` +| table process_id dest] +| `suspicious_writes_to_windows_recycle_bin_filter` + +====Associated Analytic Story==== + +* Collection and Staging + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036 +| Masquerading +| Defense Evasion +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/write_to_recycle_bin/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|System Information Discovery Detection|system-information-discovery-detection}}=== +Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1082/ T1082] +* '''Last Updated''': 2020-10-12 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| eventstats dc(process) as dc_processes_by_dest by dest +| where dc_processes_by_dest > 2 +| stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `system_information_discovery_detection_filter` + +====Associated Analytic Story==== + +* Discovery Techniques + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1082 +| System Information Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators debugging servers + +====Reference==== + +* https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|System Process Running from Unexpected Location|system-process-running-from-unexpected-location}}=== +An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036/ T1036] +* '''Last Updated''': 2020-08-25 + +
+
+ +====Search==== + $ssa_input = +| from read_ssa_enriched_events() +| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), user=ucast(map_get(input_event, "dest_user_id"), "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=lower(ucast(map_get(input_event, "process_path"), "string", null)); +$cond_1 = +| from $ssa_input +| where process_name="arp.exe" OR process_name="adaptertroubleshooter.exe" OR process_name="applicationframehost.exe" OR process_name="atbroker.exe" OR process_name="authhost.exe" OR process_name="autoworkplace.exe" OR process_name="axinstui.exe" OR process_name="backgroundtransferhost.exe" OR process_name="bdehdcfg.exe" OR process_name="bdeuisrv.exe" OR process_name="bdeunlockwizard.exe" OR process_name="bitlockerdeviceencryption.exe" OR process_name="bitlockerwizard.exe" OR process_name="bitlockerwizardelev.exe" OR process_name="bytecodegenerator.exe" OR process_name="camerasettingsuihost.exe" OR process_name="castsrv.exe" OR process_name="certenrollctrl.exe" OR process_name="checknetisolation.exe" OR process_name="clipup.exe" OR process_name="cloudexperiencehostbroker.exe" OR process_name="cloudnotifications.exe" OR process_name="cloudstoragewizard.exe" OR process_name="compmgmtlauncher.exe" OR process_name="compattelrunner.exe" OR process_name="computerdefaults.exe" OR process_name="credentialuibroker.exe" OR process_name="dfdwiz.exe" OR process_name="dwwin.exe" OR process_name="dataexchangehost.exe" OR process_name="defrag.exe" OR process_name="devicedisplayobjectprovider.exe" OR process_name="deviceeject.exe" OR process_name="deviceenroller.exe" OR process_name="devicepairingwizard.exe" OR process_name="deviceproperties.exe" OR process_name="disksnapshot.exe" OR process_name="dism.exe" OR process_name="displayswitch.exe" OR process_name="dmnotificationbroker.exe" OR process_name="dmomacpmo.exe" OR process_name="dpiscaling.exe" OR process_name="dsmusertask.exe" OR process_name="dxpserver.exe" OR process_name="edpcleanup.exe" OR process_name="eosnotify.exe" OR process_name="eap3host.exe" OR process_name="easpoliciesbrokerhost.exe" OR process_name="easeofaccessdialog.exe" OR process_name="ehstorauthn.exe" OR process_name="fxscover.exe" OR process_name="fxssvc.exe" OR process_name="fxsunatd.exe" OR process_name="filehistory.exe" OR process_name="fondue.exe" OR process_name="gamepanel.exe" OR process_name="genvalobj.exe" OR process_name="gettingstarted.exe" OR process_name="hostname.exe" OR process_name="icsentitlementhost.exe" OR process_name="infdefaultinstall.exe" OR process_name="installagent.exe" OR process_name="languagecomponentsinstallercomhandler.exe" OR process_name="launchtm.exe" OR process_name="launchwinapp.exe" OR process_name="legacynetuxhost.exe" OR process_name="licensemanagershellext.exe" OR process_name="licensingui.exe" OR process_name="locationnotificationwindows.exe" OR process_name="locationnotifications.exe" OR process_name="locator.exe" OR process_name="lockapphost.exe" OR process_name="lockscreencontentserver.exe" OR process_name="logonui.exe" OR process_name="lsaiso.exe" OR process_name="mdeserver.exe" OR process_name="mdmagent.exe" OR process_name="mdmappinstaller.exe" OR process_name="mrinfo.exe" OR process_name="mrt.exe" OR process_name="mschedexe.exe" OR process_name="magnify.exe" OR process_name="mbaeparsertask.exe" OR process_name="mdres.exe" OR process_name="mdsched.exe" OR process_name="migautoplay.exe" OR process_name="mpsigstub.exe" OR process_name="msspellcheckinghost.exe" OR process_name="muiunattend.exe" OR process_name="multidigimon.exe" OR process_name="musnotification.exe" OR process_name="musnotificationux.exe" OR process_name="napstat.exe" OR process_name="netstat.exe" OR process_name="narrator.exe" OR process_name="netcfgnotifyobjecthost.exe" OR process_name="netevtfwdr.exe" OR process_name="netproj.exe" OR process_name="netplwiz.exe" OR process_name="networkuxbroker.exe"; +$cond_2 = +| from $ssa_input +| where process_name="openwith.exe" OR process_name="optionalfeatures.exe" OR process_name="pathping.exe" OR process_name="ping.exe" OR process_name="passwordonwakesettingflyout.exe" OR process_name="pickerhost.exe" OR process_name="pkgmgr.exe" OR process_name="pnpunattend.exe" OR process_name="pnputil.exe" OR process_name="presentationhost.exe" OR process_name="presentationsettings.exe" OR process_name="printbrmui.exe" OR process_name="printdialoghost.exe" OR process_name="printdialoghost3d.exe" OR process_name="printisolationhost.exe" OR process_name="proximityuxhost.exe" OR process_name="rdspnf.exe" OR process_name="rmactivate.exe" OR process_name="rmactivate_isv.exe" OR process_name="rmactivate_ssp.exe" OR process_name="rmactivate_ssp_isv.exe" OR process_name="route.exe" OR process_name="rdpsa.exe" OR process_name="rdpsaproxy.exe" OR process_name="rdpsauachelper.exe" OR process_name="reagentc.exe" OR process_name="recoverydrive.exe" OR process_name="register-cimprovider.exe" OR process_name="registeriepkeys.exe" OR process_name="relpost.exe" OR process_name="remoteposworker.exe" OR process_name="rmclient.exe" OR process_name="robocopy.exe" OR process_name="rpcping.exe" OR process_name="runlegacycplelevated.exe" OR process_name="runtimebroker.exe" OR process_name="sihclient.exe" OR process_name="searchfilterhost.exe" OR process_name="searchindexer.exe" OR process_name="searchprotocolhost.exe" OR process_name="secedit.exe" OR process_name="sensordataservice.exe" OR process_name="setieinstalleddate.exe" OR process_name="settingsynchost.exe" OR process_name="slidetoshutdown.exe" OR process_name="smartscreensettings.exe" OR process_name="sndvol.exe" OR process_name="snippingtool.exe" OR process_name="soundrecorder.exe" OR process_name="spaceagent.exe" OR process_name="sppextcomobj.exe" OR process_name="srtasks.exe" OR process_name="stikynot.exe" OR process_name="synchost.exe" OR process_name="sysreseterr.exe" OR process_name="systempropertiesadvanced.exe" OR process_name="systempropertiescomputername.exe" OR process_name="systempropertiesdataexecutionprevention.exe" OR process_name="systempropertieshardware.exe" OR process_name="systempropertiesperformance.exe" OR process_name="systempropertiesprotection.exe" OR process_name="systempropertiesremote.exe" OR process_name="systemsettingsadminflows.exe" OR process_name="systemsettingsbroker.exe" OR process_name="systemsettingsremovedevice.exe" OR process_name="tcpsvcs.exe" OR process_name="tracert.exe" OR process_name="tstheme.exe" OR process_name="tswbprxy.exe" OR process_name="tapiunattend.exe" OR process_name="taskmgr.exe" OR process_name="thumbnailextractionhost.exe" OR process_name="tokenbrokercookies.exe" OR process_name="tpminit.exe" OR process_name="tswpfwrp.exe" OR process_name="ui0detect.exe" OR process_name="upgraderesultsui.exe" OR process_name="useraccountbroker.exe" OR process_name="useraccountcontrolsettings.exe" OR process_name="usoclient.exe" OR process_name="utilman.exe" OR process_name="vssvc.exe" OR process_name="vaultcmd.exe" OR process_name="vaultsysui.exe" OR process_name="wfs.exe" OR process_name="wmpdmc.exe" OR process_name="wpdshextautoplay.exe" OR process_name="wscollect.exe" OR process_name="wsmanhttpconfig.exe" OR process_name="wsreset.exe" OR process_name="wudfhost.exe" OR process_name="wwahost.exe" OR process_name="wallpaperhost.exe" OR process_name="webcache.exe" OR process_name="werfault.exe" OR process_name="werfaultsecure.exe" OR process_name="winsat.exe" OR process_name="windows.media.backgroundplayback.exe" OR process_name="windowsactiondialog.exe" OR process_name="windowsanytimeupgrade.exe" OR process_name="windowsanytimeupgraderesults.exe"; +$cond_3 = +| from $ssa_input +| where process_name="windowsanytimeupgradeui.exe" OR process_name="windowsupdateelevatedinstaller.exe" OR process_name="workfolders.exe" OR process_name="wpcmon.exe" OR process_name="acu.exe" OR process_name="aitagent.exe" OR process_name="aitstatic.exe" OR process_name="alg.exe" OR process_name="appidcertstorecheck.exe" OR process_name="appidpolicyconverter.exe" OR process_name="at.exe" OR process_name="attrib.exe" OR process_name="audiodg.exe" OR process_name="auditpol.exe" OR process_name="autochk.exe" OR process_name="autoconv.exe" OR process_name="autofmt.exe" OR process_name="baaupdate.exe" OR process_name="backgroundtaskhost.exe" OR process_name="bcastdvr.exe" OR process_name="bcdboot.exe" OR process_name="bcdedit.exe" OR process_name="bdechangepin.exe" OR process_name="bdeunlock.exe" OR process_name="bitsadmin.exe" OR process_name="bootcfg.exe" OR process_name="bootim.exe" OR process_name="bootsect.exe" OR process_name="bridgeunattend.exe" OR process_name="browser_broker.exe" OR process_name="bthudtask.exe" OR process_name="cacls.exe" OR process_name="calc.exe" OR process_name="cdpreference.exe" OR process_name="certreq.exe" OR process_name="certutil.exe" OR process_name="change.exe" OR process_name="changepk.exe" OR process_name="charmap.exe" OR process_name="chglogon.exe" OR process_name="chgport.exe" OR process_name="chgusr.exe" OR process_name="chkdsk.exe" OR process_name="chkntfs.exe" OR process_name="choice.exe" OR process_name="cipher.exe" OR process_name="cleanmgr.exe" OR process_name="cliconfg.exe" OR process_name="clip.exe" OR process_name="cmd.exe" OR process_name="cmdkey.exe" OR process_name="cmdl32.exe" OR process_name="cmmon32.exe" OR process_name="cmstp.exe" OR process_name="cofire.exe" OR process_name="colorcpl.exe" OR process_name="comp.exe" OR process_name="compact.exe" OR process_name="conhost.exe" OR process_name="consent.exe" OR process_name="control.exe" OR process_name="convert.exe" OR process_name="credwiz.exe" OR process_name="cscript.exe" OR process_name="csrss.exe" OR process_name="ctfmon.exe" OR process_name="cttune.exe" OR process_name="cttunesvr.exe" OR process_name="dashost.exe" OR process_name="dccw.exe" OR process_name="dcomcnfg.exe" OR process_name="ddodiag.exe" OR process_name="dfrgui.exe" OR process_name="dialer.exe" OR process_name="diantz.exe" OR process_name="dinotify.exe" OR process_name="diskpart.exe" OR process_name="diskperf.exe" OR process_name="diskraid.exe" OR process_name="dispdiag.exe" OR process_name="djoin.exe" OR process_name="dllhost.exe" OR process_name="dllhst3g.exe" OR process_name="dmcertinst.exe" OR process_name="dmcfghost.exe" OR process_name="dmclient.exe" OR process_name="dnscacheugc.exe" OR process_name="doskey.exe" OR process_name="dpapimig.exe" OR process_name="dpnsvr.exe" OR process_name="driverquery.exe" OR process_name="drvcfg.exe" OR process_name="drvinst.exe" OR process_name="dsregcmd.exe" OR process_name="dstokenclean.exe" OR process_name="dvdplay.exe" OR process_name="dvdupgrd.exe" OR process_name="dwm.exe" OR process_name="dxdiag.exe" OR process_name="easinvoker.exe" OR process_name="efsui.exe"; +$cond_4 = +| from $ssa_input +| where process_name="embeddedapplauncher.exe" OR process_name="esentutl.exe" OR process_name="eudcedit.exe" OR process_name="eventcreate.exe" OR process_name="eventvwr.exe" OR process_name="expand.exe" OR process_name="extrac32.exe" OR process_name="fc.exe" OR process_name="fhmanagew.exe" OR process_name="find.exe" OR process_name="findstr.exe" OR process_name="finger.exe" OR process_name="fixmapi.exe" OR process_name="fltmc.exe" OR process_name="fodhelper.exe" OR process_name="fontdrvhost.exe" OR process_name="fontview.exe" OR process_name="forfiles.exe" OR process_name="fsavailux.exe" OR process_name="fsquirt.exe" OR process_name="fsutil.exe" OR process_name="ftp.exe" OR process_name="fvenotify.exe" OR process_name="fveprompt.exe" OR process_name="getmac.exe" OR process_name="gpresult.exe" OR process_name="gpscript.exe" OR process_name="gpupdate.exe" OR process_name="grpconv.exe" OR process_name="hdwwiz.exe" OR process_name="help.exe" OR process_name="hwrcomp.exe" OR process_name="hwrreg.exe" OR process_name="icacls.exe" OR process_name="icardagt.exe" OR process_name="icsunattend.exe" OR process_name="ie4uinit.exe" OR process_name="ieunatt.exe" OR process_name="ieetwcollector.exe" OR process_name="iexpress.exe" OR process_name="immersivetpmvscmgrsvr.exe" OR process_name="ipconfig.exe" OR process_name="irftp.exe" OR process_name="iscsicli.exe" OR process_name="iscsicpl.exe" OR process_name="isoburn.exe" OR process_name="klist.exe" OR process_name="ksetup.exe" OR process_name="ktmutil.exe" OR process_name="label.exe" OR process_name="licensingdiag.exe" OR process_name="lodctr.exe" OR process_name="logagent.exe" OR process_name="logman.exe" OR process_name="logoff.exe" OR process_name="lpkinstall.exe" OR process_name="lpksetup.exe" OR process_name="lpremove.exe" OR process_name="lsass.exe" OR process_name="lsm.exe" OR process_name="makecab.exe" OR process_name="manage-bde.exe" OR process_name="mblctr.exe" OR process_name="mcbuilder.exe" OR process_name="mctadmin.exe" OR process_name="mfpmp.exe" OR process_name="mmc.exe" OR process_name="mobsync.exe" OR process_name="mountvol.exe" OR process_name="mpnotify.exe" OR process_name="msconfig.exe" OR process_name="msdt.exe" OR process_name="msdtc.exe" OR process_name="msfeedssync.exe" OR process_name="msg.exe" OR process_name="mshta.exe" OR process_name="msiexec.exe" OR process_name="msinfo32.exe" OR process_name="mspaint.exe" OR process_name="msra.exe" OR process_name="mstsc.exe" OR process_name="mtstocom.exe" OR process_name="nbtstat.exe" OR process_name="ndadmin.exe" OR process_name="net.exe" OR process_name="net1.exe" OR process_name="netbtugc.exe" OR process_name="netcfg.exe" OR process_name="netiougc.exe" OR process_name="netsh.exe" OR process_name="newdev.exe" OR process_name="nltest.exe" OR process_name="notepad.exe" OR process_name="nslookup.exe" OR process_name="ntoskrnl.exe" OR process_name="ntprint.exe" OR process_name="ocsetup.exe" OR process_name="odbcad32.exe" OR process_name="odbcconf.exe" OR process_name="omadmclient.exe" OR process_name="omadmprc.exe"; +$cond_5 = +| from $ssa_input +| where process_name="openfiles.exe" OR process_name="osk.exe" OR process_name="p2phost.exe" OR process_name="pcalua.exe" OR process_name="pcaui.exe" OR process_name="pcawrk.exe" OR process_name="pcwrun.exe" OR process_name="perfmon.exe" OR process_name="phoneactivate.exe" OR process_name="plasrv.exe" OR process_name="poqexec.exe" OR process_name="powercfg.exe" OR process_name="prevhost.exe" OR process_name="print.exe" OR process_name="printfilterpipelinesvc.exe" OR process_name="printui.exe" OR process_name="proquota.exe" OR process_name="provtool.exe" OR process_name="psr.exe" OR process_name="pwlauncher.exe" OR process_name="qappsrv.exe" OR process_name="qprocess.exe" OR process_name="query.exe" OR process_name="quser.exe" OR process_name="qwinsta.exe" OR process_name="rasautou.exe" OR process_name="rasdial.exe" OR process_name="raserver.exe" OR process_name="rasphone.exe" OR process_name="rdpclip.exe" OR process_name="rdpinput.exe" OR process_name="rdrleakdiag.exe" OR process_name="recdisc.exe" OR process_name="recover.exe" OR process_name="reg.exe" OR process_name="regedt32.exe" OR process_name="regini.exe" OR process_name="regsvr32.exe" OR process_name="rekeywiz.exe" OR process_name="relog.exe" OR process_name="repair-bde.exe" OR process_name="replace.exe" OR process_name="reset.exe" OR process_name="resmon.exe" OR process_name="rmttpmvscmgrsvr.exe" OR process_name="rrinstaller.exe" OR process_name="rstrui.exe" OR process_name="runas.exe" OR process_name="rundll32.exe" OR process_name="runonce.exe" OR process_name="rwinsta.exe" OR process_name="sbunattend.exe" OR process_name="sc.exe" OR process_name="schtasks.exe" OR process_name="sdbinst.exe" OR process_name="sdchange.exe" OR process_name="sdclt.exe" OR process_name="sdiagnhost.exe" OR process_name="secinit.exe" OR process_name="services.exe" OR process_name="sessionmsg.exe" OR process_name="sethc.exe" OR process_name="setspn.exe" OR process_name="setupcl.exe" OR process_name="setupugc.exe" OR process_name="setx.exe" OR process_name="sfc.exe" OR process_name="shadow.exe" OR process_name="shrpubw.exe" OR process_name="shutdown.exe" OR process_name="sigverif.exe" OR process_name="sihost.exe" OR process_name="slui.exe" OR process_name="smss.exe" OR process_name="snmptrap.exe" OR process_name="sort.exe" OR process_name="spinstall.exe" OR process_name="spoolsv.exe" OR process_name="sppsvc.exe" OR process_name="spreview.exe" OR process_name="srdelayed.exe" OR process_name="subst.exe" OR process_name="svchost.exe" OR process_name="sxstrace.exe" OR process_name="syskey.exe" OR process_name="systeminfo.exe" OR process_name="systemreset.exe" OR process_name="systray.exe" OR process_name="tabcal.exe" OR process_name="takeown.exe" OR process_name="taskeng.exe" OR process_name="taskhost.exe" OR process_name="taskhostw.exe" OR process_name="taskkill.exe" OR process_name="tasklist.exe" OR process_name="taskmgr.exe" OR process_name="tcmsetup.exe" OR process_name="timeout.exe" OR process_name="tpmvscmgr.exe" OR process_name="tpmvscmgrsvr.exe"; +$cond_6 = +| from $ssa_input +| where process_name="tracerpt.exe" OR process_name="tscon.exe" OR process_name="tsdiscon.exe" OR process_name="tskill.exe" OR process_name="typeperf.exe" OR process_name="tzsync.exe" OR process_name="tzutil.exe" OR process_name="ucsvc.exe" OR process_name="unlodctr.exe" OR process_name="unregmp2.exe" OR process_name="upnpcont.exe" OR process_name="userinit.exe" OR process_name="vds.exe" OR process_name="vdsldr.exe" OR process_name="verclsid.exe" OR process_name="verifier.exe" OR process_name="verifiergui.exe" OR process_name="vmicsvc.exe" OR process_name="vssadmin.exe" OR process_name="w32tm.exe" OR process_name="waitfor.exe" OR process_name="wbadmin.exe" OR process_name="wbengine.exe" OR process_name="wecutil.exe" OR process_name="wermgr.exe" OR process_name="wevtutil.exe" OR process_name="wextract.exe" OR process_name="where.exe" OR process_name="whoami.exe" OR process_name="wiaacmgr.exe" OR process_name="wiawow64.exe" OR process_name="wifitask.exe" OR process_name="wimserv.exe" OR process_name="wininit.exe" OR process_name="winload.exe" OR process_name="winlogon.exe" OR process_name="winresume.exe" OR process_name="winrs.exe" OR process_name="winrshost.exe" OR process_name="winver.exe" OR process_name="wisptis.exe" OR process_name="wkspbroker.exe" OR process_name="wksprt.exe" OR process_name="wlanext.exe" OR process_name="wlrmdr.exe" OR process_name="wowreg32.exe" OR process_name="wpnpinst.exe" OR process_name="wpr.exe" OR process_name="write.exe" OR process_name="wscript.exe" OR process_name="wsmprovhost.exe" OR process_name="wsqmcons.exe" OR process_name="wuapihost.exe" OR process_name="wuapp.exe" OR process_name="wuauclt.exe" OR process_name="wusa.exe" OR process_name="xcopy.exe" OR process_name="xpsrchvw.exe" OR process_name="xwizard.exe"; + +| from $cond_1 +| union $cond_2 +| union $cond_3 +| union $cond_4 +| union $cond_5 +| union $cond_6 +| where process_path!="c:\\windows\\system32" AND process_path!="c:\\windows\\syswow64" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(device, user), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +Collect endpoint data such as sysmon or 4688 events. + +====Required field==== + +* dest_device_id + +* process_name + +* _time + +* dest_user_id + +* process_path + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036 +| Masquerading +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|System Processes Run From Unexpected Locations|system-processes-run-from-unexpected-locations}}=== +This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036.003/ T1036.003] +* '''Last Updated''': 2020-12-08 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name Processes.process_hash +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `is_windows_system_file` +| `system_processes_run_from_unexpected_locations_filter` + +====Associated Analytic Story==== + +* Suspicious Command-Line Executions + +* Unusual Processes + +* Ransomware + + +====How To Implement==== +To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|TOR Traffic|tor-traffic}}=== +This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.001/ T1071.001] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `tor_traffic_filter` + +====Associated Analytic Story==== + +* Prohibited Traffic Allowed or Protocol Mismatch + +* Ransomware + +* Command and Control + +* Sunburst Malware + + +====How To Implement==== +In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.001 +| Web Protocols +| Command and Control +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +None at this time + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|USN Journal Deletion|usn-journal-deletion}}=== +The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1070/ T1070] +* '''Last Updated''': 2018-12-03 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search process="*deletejournal*" AND process="*usn*" +| `usn_journal_deletion_filter` + +====Associated Analytic Story==== + +* Windows Log Manipulation + +* Ransomware + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070 +| Indicator Removal on Host +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/atomic_red_team/windows-sysmon.log + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Unload Sysmon Filter Driver|unload-sysmon-filter-driver}}=== +Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.001/ T1562.001] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`unload_sysmon_filter_driver_filter` +| table firstTime lastTime dest user count process_name process_id parent_process_name process + +====Associated Analytic Story==== + +* Disabling Security Tools + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.001 +| Disable or Modify Tools +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== + + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +==={{visible anchor|Unusually Long Command Line|unusually-long-command-line}}=== +Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-10-06 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) +| where cmd_line!=null and dest_user_id!=null +| eval cmd_line_norm=replace(cast(cmd_line, "string"), /\s(--?\w+) +|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) +| select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input +| adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 +| where label AND quantile>0.99 +| first_time_event input_columns=["dest_device_id", "cmd_line"] +| where first_time_dest_device_id_cmd_line +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +You must be ingesting sysmon endpoint data that monitors command lines. + +====Required field==== + +* process_name + +* _time + +* dest_device_id + +* dest_user_id + +* process + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +This detection may flag suspiciously long command lines when there is not sufficient evidence (samples) for a given process that this detection is tracking; or when there is high variability in the length of the command line for the tracked process. Also, some legitimate applications may use long command lines. Such is the case of Ansible, that encodes Powershell scripts using long base64. Attackers may use this technique to obfuscate their payloads. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Unusually Long Command Line|unusually-long-command-line}}=== +Command lines that are extremely long may be indicative of malicious activity on your hosts. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-12-08 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval processlen=len(process) +| eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest +| stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process +| `unusually_long_command_line_filter` +|eval threshold = 3 +| where maxlen > ((threshold*stdevperhost) + avgperhost) + +====Associated Analytic Story==== + +* Suspicious Command-Line Executions + +* Unusual Processes + +* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + +* Ransomware + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the process field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Some legitimate applications start with long command lines. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + + +''version'': 5 +
+
+ +---- + +==={{visible anchor|Unusually Long Command Line - MLTK|unusually-long-command-line---mltk}}=== +Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2019-05-08 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval processlen=len(process) +| search user!=unknown +| apply cmdline_pdfmodel threshold=0.01 +| rename "IsOutlier(processlen)" as isOutlier +| search isOutlier > 0 +| table firstTime lastTime user dest process_name process processlen count +| `unusually_long_command_line___mltk_filter` + +====Associated Analytic Story==== + +* Suspicious Command-Line Executions + +* Unusual Processes + +* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + +* Ransomware + + +====How To Implement==== +You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Unusually Long Content-Type Length|unusually-long-content-type-length}}=== +This search looks for unusually long strings in the Content-Type http header that the client sends the server. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2017-10-13 + +
+
+ +====Search==== +`stream_http` +| eval cs_content_type_length = len(cs_content_type) +| where cs_content_type_length > 100 +| table endtime src_ip dest_ip cs_content_type_length cs_content_type url +| `unusually_long_content_type_length_filter` + +====Associated Analytic Story==== + +* Apache Struts Vulnerability + + +====How To Implement==== +This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +Very few legitimate Content-Type fields will have a length greater than 100 characters. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|WBAdmin Delete System Backups|wbadmin-delete-system-backups}}=== +This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1490/ T1490] +* '''Last Updated''': 2021-01-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `wbadmin_delete_system_backups_filter` + +====Associated Analytic Story==== + +* Ryuk Ransomware + +* Ransomware + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1490 +| Inhibit System Recovery +| Impact +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators may modify the boot configuration. + +====Reference==== + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md + +* https://thedfirreport.com/2020/10/08/ryuks-return/ + +* https://attack.mitre.org/techniques/T1490/ + +* https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|WMI Permanent Event Subscription - Sysmon|wmi-permanent-event-subscription---sysmon}}=== +This search looks for the creation of WMI permanent event subscriptions. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.003/ T1546.003] +* '''Last Updated''': 2020-12-08 + +
+
+ +====Search==== +`sysmon` EventCode=21 +| rename host as dest +| table _time, dest, user, Operation, EventType, Query, Consumer, Filter +| `wmi_permanent_event_subscription___sysmon_filter` + +====Associated Analytic Story==== + +* Suspicious WMI Use + + +====How To Implement==== +To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.003 +| Windows Management Instrumentation Event Subscription +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, administrators may use event subscriptions for legitimate purposes. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log + + +''version'': 2 +
+
+ +---- + +==={{visible anchor|Web Fraud - Account Harvesting|web-fraud---account-harvesting}}=== +This search is used to identify the creation of multiple user accounts using the same email domain name. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136/ T1136] +* '''Last Updated''': 2018-10-08 + +
+
+ +====Search==== +`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" +| rex field=cookie "form_key=(?\w+)" +| rex field=form_data "login\[username\]=(?[^& +|^$]+)" +| search Username=* +| rex field=Username "@(?.*)" +| stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain +| where UniqueUsernames> 25 +| `web_fraud___account_harvesting_filter` + +====Associated Analytic Story==== + +* Web Fraud Detection + + +====How To Implement==== +We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136 +| Create Account +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. + +====Reference==== + +* https://splunkbase.splunk.com/app/2734/ + +* https://splunkbase.splunk.com/app/1809/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Web Fraud - Anomalous User Clickspeed|web-fraud---anomalous-user-clickspeed}}=== +This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2018-10-08 + +
+
+ +====Search==== +`stream_http` http_content_type=text* +| rex field=cookie "form_key=(?\w+)" +| streamstats window=2 current=1 range(_time) as TimeDelta by session_id +| where TimeDelta>0 +|stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id +| where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) +| `web_fraud___anomalous_user_clickspeed_filter` + +====Associated Analytic Story==== + +* Web Fraud Detection + + +====How To Implement==== +Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior. + +====Reference==== + +* https://en.wikipedia.org/wiki/Session_ID + +* https://en.wikipedia.org/wiki/Session_(computer_science) + +* https://en.wikipedia.org/wiki/HTTP_cookie + +* https://splunkbase.splunk.com/app/1809/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Web Fraud - Password Sharing Across Accounts|web-fraud---password-sharing-across-accounts}}=== +This search is used to identify user accounts that share a common password. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2018-10-08 + +
+
+ +====Search==== +`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* +| rex field=form_data "login\[username\]=(?[^& +|^$]+)" +| rex field=form_data "login\[password\]=(?[^& +|^$]+)" +| stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password +|where UniqueUsernames>5 +| `web_fraud___password_sharing_across_accounts_filter` + +====Associated Analytic Story==== + +* Web Fraud Detection + + +====How To Implement==== +We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. + +====Reference==== + +* https://en.wikipedia.org/wiki/Session_ID + +* https://en.wikipedia.org/wiki/Session_(computer_science) + +* https://en.wikipedia.org/wiki/HTTP_cookie + +* https://splunkbase.splunk.com/app/1809/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Web Servers Executing Suspicious Processes|web-servers-executing-suspicious-processes}}=== +This search looks for suspicious processes on all systems labeled as web servers. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1082/ T1082] +* '''Last Updated''': 2019-04-01 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `web_servers_executing_suspicious_processes_filter` + +====Associated Analytic Story==== + +* Apache Struts Vulnerability + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1082 +| System Information Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Windows AdFind Exe|windows-adfind-exe}}=== +This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1018/ T1018] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `windows_adfind_exe_filter` + +====Associated Analytic Story==== + +* Sunburst Malware + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1018 +| Remote System Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +administrators rarely use adfind, usually not used for legitimate reasons + +====Reference==== + +* https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/ + +* https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +==={{visible anchor|Windows Event Log Cleared|windows-event-log-cleared}}=== +This search looks for Windows events that indicate one of the Windows event logs has been purged. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1070.001/ T1070.001] +* '''Last Updated''': 2020-07-06 + +
+
+ +====Search==== +(`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) +| stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `windows_event_log_cleared_filter` + +====Associated Analytic Story==== + +* Windows Log Manipulation + +* Ransomware + + +====How To Implement==== +To successfully implement this search, you need to be ingesting Windows event logs from your hosts. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070.001 +| Clear Windows Event Logs +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It is possible that these logs may be legitimately cleared by Administrators. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-security.log + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-system.log + + +''version'': 4 +
+
+ +---- + +==={{visible anchor|Windows Security Account Manager Stopped|windows-security-account-manager-stopped}}=== +The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1489/ T1489] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" +| `drop_dm_object_name(Processes)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `windows_security_account_manager_stopped_filter` + +====Associated Analytic Story==== + +* Ryuk Ransomware + + +====How To Implement==== +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1489 +| Service Stop +| Impact +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + + + +[[Category:V:ESSOC:3.15.0]] \ No newline at end of file From 239745b2d6bde3e7e5f409177556e34063f419e2 Mon Sep 17 00:00:00 2001 From: divious1 Date: Mon, 1 Mar 2021 21:36:26 -0500 Subject: [PATCH 08/62] added organized by kind for detections --- bin/doc_gen.py | 21 +- bin/jinja2_templates/doc_detections_wiki.j2 | 18 +- docs/detections.wiki | 12194 +++++++++--------- 3 files changed, 5847 insertions(+), 6386 deletions(-) diff --git a/bin/doc_gen.py b/bin/doc_gen.py index 4c1b2983a6..baec9d7542 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -75,7 +75,7 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): detection_yaml['kind'] = manifest_file.split('/')[-2] detections.append(detection_yaml) - sorted_detections= sorted(detections, key=lambda i: i['name']) + sorted_detections = sorted(detections, key=lambda i: i['name']) j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), trim_blocks=False) @@ -88,10 +88,27 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): f.write(output) print("doc_gen.py wrote {0} detections documentation in markdown to: {1}".format(len(detections),output_path)) + #sort detections by kind into categories + kinds = [] + kind_names = set() + for detection in sorted_detections: + kind_names.add(detection['kind']) + + for kind_name in sorted(kind_names): + new_kind = {} + new_kind['name'] = kind_name + new_kind['detections'] = [] + kinds.append(new_kind) + + for detection in sorted_detections: + for kind in kinds: + if kind['name'] == detection['kind']: + kind['detections'].append(detection) + # write wikimarkup template = j2_env.get_template('doc_detections_wiki.j2') output_path = path.join(OUTPUT_DIR + '/detections.wiki') - output = template.render(detections=sorted_detections) + output = template.render(kinds=kinds) with open(output_path, 'w', encoding="utf-8") as f: f.write(output) print("doc_gen.py wrote {0} detections documentation in mediawiki to: {1}".format(len(detections),output_path)) diff --git a/bin/jinja2_templates/doc_detections_wiki.j2 b/bin/jinja2_templates/doc_detections_wiki.j2 index 72776aab69..9a0aaffc98 100644 --- a/bin/jinja2_templates/doc_detections_wiki.j2 +++ b/bin/jinja2_templates/doc_detections_wiki.j2 @@ -2,14 +2,11 @@ ---- All the detections shipped to different Splunk products. Below is a breakdown by kind. -==Cloud== -{% for detection in detections %} -{% if detection.kind == 'cloud' %} -* [[#{{ detection.name }}|{{ detection.name }}]] -{% endif %} -{% endfor %} -{% for detection in detections %} -==={% raw %}{{{% endraw %}visible anchor|{{ detection.name }}|{{ detection.name|lower|replace(" ", "-") }}{% raw %}}}{% endraw %}=== +{% for kind in kinds %} +=={{ kind.name|capitalize }}== + +{% for detection in kind.detections %} +==={{ detection.name}}=== {{ detection.description }} * '''Product''': {{ detection.tags.product|join(', ') }} @@ -21,7 +18,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by
====Search==== -{{ detection.search|replace("|", "\n|") }} +{{ detection.search|replace("|", "\n|") }} ====Associated Analytic Story==== {% for story in detection.tags.analytic_story %} @@ -74,5 +71,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by ---- {% endfor %} +{% endfor %} -[[Category:V:ESSOC:3.15.0]] +[[Category:V:ESSOC:draft]] diff --git a/docs/detections.wiki b/docs/detections.wiki index 5c0d7d2647..20b35d7ab6 100644 --- a/docs/detections.wiki +++ b/docs/detections.wiki @@ -2,609 +2,395 @@ ---- All the detections shipped to different Splunk products. Below is a breakdown by kind. + +==Application== + + +===Email files written outside of the Outlook directory=== +The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.001/ T1114.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest +| `drop_dm_object_name("Filesystem")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `email_files_written_outside_of_the_outlook_directory_filter` + +====Associated Analytic Story==== + +* Collection and Staging + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.001 +| Local Email Collection +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Multiple Okta Users With Invalid Credentials From The Same IP=== +This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`okta` outcome.reason=INVALID_CREDENTIALS +| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city +| stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search distinct_users > 5 +| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` + +====Associated Analytic Story==== + +* Suspicious Okta Activity + + +====How To Implement==== +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Okta Account Lockout Events=== +Detect Okta user lockout events + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`okta` displayMessage="Max sign in attempts exceeded" +| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city +| table _time, user, country, state, city, src_ip +| `okta_account_lockout_events_filter` + +====Associated Analytic Story==== + +* Suspicious Okta Activity + + +====How To Implement==== +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Okta Failed SSO Attempts=== +Detect failed Okta SSO events + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`okta` displayMessage="User attempted unauthorized access to app" +| stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `okta_failed_sso_attempts_filter` + +====Associated Analytic Story==== + +* Suspicious Okta Activity + + +====How To Implement==== +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +There may be a faulty config preventing legitmate users from accessing apps they should have access to. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Okta User Logins From Multiple Cities=== +This search detects logins from the same user from different cities in a 24 hour period. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null +| stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `okta_user_logins_from_multiple_cities_filter` +| search locations > 1 + +====Associated Analytic Story==== + +* Suspicious Okta Activity + + +====How To Implement==== +This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Web Servers Executing Suspicious Processes=== +This search looks for suspicious processes on all systems labeled as web servers. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1082/ T1082] +* '''Last Updated''': 2019-04-01 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `web_servers_executing_suspicious_processes_filter` + +====Associated Analytic Story==== + +* Apache Struts Vulnerability + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1082 +| System Information Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + + + ==Cloud== -* [[#AWS Cross Account Activity From Previously Unseen Account|AWS Cross Account Activity From Previously Unseen Account]] - - - -* [[#AWS Detect Users creating keys with encrypt policy without MFA|AWS Detect Users creating keys with encrypt policy without MFA]] - - - -* [[#AWS Detect Users with KMS keys performing encryption S3|AWS Detect Users with KMS keys performing encryption S3]] - - - -* [[#AWS Network Access Control List Created with All Open Ports|AWS Network Access Control List Created with All Open Ports]] - - - -* [[#AWS Network Access Control List Deleted|AWS Network Access Control List Deleted]] - - - -* [[#AWS SAML Access by Provider User and Principal|AWS SAML Access by Provider User and Principal]] - - - -* [[#AWS SAML Update identity provider|AWS SAML Update identity provider]] - - - -* [[#Abnormally High Number Of Cloud Infrastructure API Calls|Abnormally High Number Of Cloud Infrastructure API Calls]] - - - -* [[#Abnormally High Number Of Cloud Instances Destroyed|Abnormally High Number Of Cloud Instances Destroyed]] - - - -* [[#Abnormally High Number Of Cloud Instances Launched|Abnormally High Number Of Cloud Instances Launched]] - - - -* [[#Abnormally High Number Of Cloud Security Group API Calls|Abnormally High Number Of Cloud Security Group API Calls]] - - - - - - - - - - - - - - - - - - - - - - - - - - - -* [[#Cloud API Calls From Previously Unseen User Roles|Cloud API Calls From Previously Unseen User Roles]] - - - -* [[#Cloud Compute Instance Created By Previously Unseen User|Cloud Compute Instance Created By Previously Unseen User]] - - - -* [[#Cloud Compute Instance Created In Previously Unused Region|Cloud Compute Instance Created In Previously Unused Region]] - - - -* [[#Cloud Compute Instance Created With Previously Unseen Image|Cloud Compute Instance Created With Previously Unseen Image]] - - - -* [[#Cloud Compute Instance Created With Previously Unseen Instance Type|Cloud Compute Instance Created With Previously Unseen Instance Type]] - - - -* [[#Cloud Instance Modified By Previously Unseen User|Cloud Instance Modified By Previously Unseen User]] - - - -* [[#Cloud Provisioning Activity From Previously Unseen City|Cloud Provisioning Activity From Previously Unseen City]] - - - -* [[#Cloud Provisioning Activity From Previously Unseen Country|Cloud Provisioning Activity From Previously Unseen Country]] - - - -* [[#Cloud Provisioning Activity From Previously Unseen IP Address|Cloud Provisioning Activity From Previously Unseen IP Address]] - - - -* [[#Cloud Provisioning Activity From Previously Unseen Region|Cloud Provisioning Activity From Previously Unseen Region]] - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -* [[#Detect AWS Console Login by New User|Detect AWS Console Login by New User]] - - - -* [[#Detect AWS Console Login by User from New City|Detect AWS Console Login by User from New City]] - - - -* [[#Detect AWS Console Login by User from New Country|Detect AWS Console Login by User from New Country]] - - - -* [[#Detect AWS Console Login by User from New Region|Detect AWS Console Login by User from New Region]] - - - - - - - - - - - - - - - -* [[#Detect GCP Storage access from a new IP|Detect GCP Storage access from a new IP]] - - - - - - - - - - - - - - - - - - - - - -* [[#Detect New Open GCP Storage Buckets|Detect New Open GCP Storage Buckets]] - - - -* [[#Detect New Open S3 Buckets over AWS CLI|Detect New Open S3 Buckets over AWS CLI]] - - - -* [[#Detect New Open S3 buckets|Detect New Open S3 buckets]] - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -* [[#Detect S3 access from a new IP|Detect S3 access from a new IP]] - - - - - - - -* [[#Detect Spike in AWS Security Hub Alerts for EC2 Instance|Detect Spike in AWS Security Hub Alerts for EC2 Instance]] - - - -* [[#Detect Spike in AWS Security Hub Alerts for User|Detect Spike in AWS Security Hub Alerts for User]] - - - -* [[#Detect Spike in S3 Bucket deletion|Detect Spike in S3 Bucket deletion]] - - - -* [[#Detect Spike in blocked Outbound Traffic from your AWS|Detect Spike in blocked Outbound Traffic from your AWS]] - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -* [[#High Number of Login Failures from a single source|High Number of Login Failures from a single source]] - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -* [[#New container uploaded to AWS ECR|New container uploaded to AWS ECR]] - - - - - -* [[#O365 Add App Role Assignment Grant User|O365 Add App Role Assignment Grant User]] - - - -* [[#O365 Added Service Principal|O365 Added Service Principal]] - - - -* [[#O365 Bypass MFA via Trusted IP|O365 Bypass MFA via Trusted IP]] - - - -* [[#O365 Disable MFA|O365 Disable MFA]] - - - -* [[#O365 Excessive Authentication Failures Alert|O365 Excessive Authentication Failures Alert]] - - - -* [[#O365 Excessive SSO logon errors|O365 Excessive SSO logon errors]] - - - -* [[#O365 New Federated Domain Added|O365 New Federated Domain Added]] - - - -* [[#O365 PST export alert|O365 PST export alert]] - - - -* [[#O365 Suspicious Admin Email Forwarding|O365 Suspicious Admin Email Forwarding]] - - - -* [[#O365 Suspicious Rights Delegation|O365 Suspicious Rights Delegation]] - - - -* [[#O365 Suspicious User Email Forwarding|O365 Suspicious User Email Forwarding]] - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -==={{visible anchor|AWS Cross Account Activity From Previously Unseen Account|aws-cross-account-activity-from-previously-unseen-account}}=== +===AWS Cross Account Activity From Previously Unseen Account=== This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. This search is deprecated and have been translated to use the latest Authentication Datamodel. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Authentication -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2020-05-28
====Search==== - -| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role -| `drop_dm_object_name(Authentication)` -| rex field=user_role "arn:aws:sts:*:(?.*):" -| where vendor_account != dest_account -| rename vendor_account as requestingAccountId dest_account as requestedAccountId -| lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime -| eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") -| where status = "New Cross Account Activity" -| `security_content_ctime(firstTime)` + +| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role +| `drop_dm_object_name(Authentication)` +| rex field=user_role "arn:aws:sts:*:(?.*):" +| where vendor_account != dest_account +| rename vendor_account as requestingAccountId dest_account as requestedAccountId +| lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime +| eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") +| where status = "New Cross Account Activity" +| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| `aws_cross_account_activity_from_previously_unseen_account_filter` +| `aws_cross_account_activity_from_previously_unseen_account_filter` ====Associated Analytic Story==== @@ -646,11 +432,11 @@ Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicio ---- -==={{visible anchor|AWS Detect Users creating keys with encrypt policy without MFA|aws-detect-users-creating-keys-with-encrypt-policy-without-mfa}}=== +===AWS Detect Users creating keys with encrypt policy without MFA=== This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1486/ T1486] * '''Last Updated''': 2021-01-11 @@ -658,18 +444,18 @@ This search provides detection of KMS keys which action kms:Encrypt is accessibl
====Search==== -`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy -| spath input=requestParameters.policy output=key_policy_statements path=Statement{} -| mvexpand key_policy_statements -| spath input=key_policy_statements output=key_policy_action_1 path=Action -| spath input=key_policy_statements output=key_policy_action_2 path=Action{} -| eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) -| spath input=key_policy_statements output=key_policy_principal path=Principal.AWS -| search key_policy_action="kms:Encrypt" AND key_policy_principal="*" -| stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId +`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy +| spath input=requestParameters.policy output=key_policy_statements path=Statement{} +| mvexpand key_policy_statements +| spath input=key_policy_statements output=key_policy_action_1 path=Action +| spath input=key_policy_statements output=key_policy_action_2 path=Action{} +| eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) +| spath input=key_policy_statements output=key_policy_principal path=Principal.AWS +| search key_policy_action="kms:Encrypt" AND key_policy_principal="*" +| stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -|`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` +| `security_content_ctime(lastTime)` +|`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` ====Associated Analytic Story==== @@ -719,11 +505,11 @@ unknown ---- -==={{visible anchor|AWS Detect Users with KMS keys performing encryption S3|aws-detect-users-with-kms-keys-performing-encryption-s3}}=== +===AWS Detect Users with KMS keys performing encryption S3=== This search provides detection of users with KMS keys performing encryption specifically against S3 buckets. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1486/ T1486] * '''Last Updated''': 2021-01-11 @@ -731,12 +517,12 @@ This search provides detection of users with KMS keys performing encryption spec
====Search==== -`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" -| rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file -| stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user +`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" +| rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file +| stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -|`aws_detect_users_with_kms_keys_performing_encryption_s3_filter` +| `security_content_ctime(lastTime)` +|`aws_detect_users_with_kms_keys_performing_encryption_s3_filter` ====Associated Analytic Story==== @@ -786,11 +572,11 @@ bucket with S3 encryption ---- -==={{visible anchor|AWS Network Access Control List Created with All Open Ports|aws-network-access-control-list-created-with-all-open-ports}}=== +===AWS Network Access Control List Created with All Open Ports=== The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] * '''Last Updated''': 2021-01-11 @@ -798,15 +584,15 @@ The search looks for CloudTrail events to detect if any network ACLs were create
====Search==== -`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 -| append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 -| eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' -| where port_range>1024] -| fillnull -| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock +`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 +| append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 +| eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' +| where port_range>1024] +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `aws_network_access_control_list_created_with_all_open_ports_filter` +| `security_content_ctime(lastTime)` +| `aws_network_access_control_list_created_with_all_open_ports_filter` ====Associated Analytic Story==== @@ -852,11 +638,11 @@ It's possible that an admin has created this ACL with all ports open for some le ---- -==={{visible anchor|AWS Network Access Control List Deleted|aws-network-access-control-list-deleted}}=== +===AWS Network Access Control List Deleted=== Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] * '''Last Updated''': 2021-01-12 @@ -864,12 +650,12 @@ Enforcing network-access controls is one of the defensive mechanisms used by clo
====Search==== -`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false -| fillnull -| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent +`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `aws_network_access_control_list_deleted_filter` +| `security_content_ctime(lastTime)` +| `aws_network_access_control_list_deleted_filter` ====Associated Analytic Story==== @@ -915,11 +701,11 @@ It's possible that a user has legitimately deleted a network ACL. ---- -==={{visible anchor|AWS SAML Access by Provider User and Principal|aws-saml-access-by-provider-user-and-principal}}=== +===AWS SAML Access by Provider User and Principal=== This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] * '''Last Updated''': 2021-01-26 @@ -927,11 +713,11 @@ This search provides specific SAML access from specific Service Provider, user a
====Search==== -`cloudtrail` eventName=Assumerolewithsaml -| stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent +`cloudtrail` eventName=Assumerolewithsaml +| stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -|`aws_saml_access_by_provider_user_and_principal_filter` +| `security_content_ctime(lastTime)` +|`aws_saml_access_by_provider_user_and_principal_filter` ====Associated Analytic Story==== @@ -983,11 +769,11 @@ Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very diff ---- -==={{visible anchor|AWS SAML Update identity provider|aws-saml-update-identity-provider}}=== +===AWS SAML Update identity provider=== This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] * '''Last Updated''': 2021-01-26 @@ -995,11 +781,11 @@ This search provides detection of updates to SAML provider in AWS. Updates to SA
====Search==== -`cloudtrail` eventName=UpdateSAMLProvider -| stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId +`cloudtrail` eventName=UpdateSAMLProvider +| stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -|`aws_saml_update_identity_provider_filter` +| `security_content_ctime(lastTime)` +|`aws_saml_update_identity_provider_filter` ====Associated Analytic Story==== @@ -1051,7 +837,7 @@ Updating a SAML provider or creating a new one may not necessarily be malicious ---- -==={{visible anchor|Abnormally High Number Of Cloud Infrastructure API Calls|abnormally-high-number-of-cloud-infrastructure-api-calls}}=== +===Abnormally High Number Of Cloud Infrastructure API Calls=== This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1063,23 +849,23 @@ This search will detect a spike in the number of API calls made to your cloud in
====Search==== - -| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h -| `drop_dm_object_name("All_Changes")` -| eval HourOfDay=strftime(_time, "%H") -| eval HourOfDay=floor(HourOfDay/4)*4 -| eval DayOfWeek=strftime(_time, "%w") -| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) -| join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] -| where cardinality >=16 -| apply cloud_excessive_api_calls_v1 threshold=0.005 -| rename "IsOutlier(api_calls)" as isOutlier -| where isOutlier=1 -| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) -| where api_calls > expected_upper_threshold -| eval distance_from_threshold = api_calls - expected_upper_threshold -| table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold -| `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` + +| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] +| where cardinality >=16 +| apply cloud_excessive_api_calls_v1 threshold=0.005 +| rename "IsOutlier(api_calls)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| where api_calls > expected_upper_threshold +| eval distance_from_threshold = api_calls - expected_upper_threshold +| table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold +| `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` ====Associated Analytic Story==== @@ -1125,7 +911,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base ---- -==={{visible anchor|Abnormally High Number Of Cloud Instances Destroyed|abnormally-high-number-of-cloud-instances-destroyed}}=== +===Abnormally High Number Of Cloud Instances Destroyed=== This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1137,22 +923,22 @@ This search finds for the number successfully destroyed cloud instances for ever
====Search==== - -| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h -| `drop_dm_object_name("All_Changes")` -| eval HourOfDay=strftime(_time, "%H") -| eval HourOfDay=floor(HourOfDay/4)*4 -| eval DayOfWeek=strftime(_time, "%w") -| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) -| join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] -| where cardinality >=16 -| apply cloud_excessive_instances_destroyed_v1 threshold=0.005 -| rename "IsOutlier(instances_destroyed)" as isOutlier -| where isOutlier=1 -| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) -| eval distance_from_threshold = instances_destroyed - expected_upper_threshold -| table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id -| `abnormally_high_number_of_cloud_instances_destroyed_filter` + +| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] +| where cardinality >=16 +| apply cloud_excessive_instances_destroyed_v1 threshold=0.005 +| rename "IsOutlier(instances_destroyed)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| eval distance_from_threshold = instances_destroyed - expected_upper_threshold +| table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id +| `abnormally_high_number_of_cloud_instances_destroyed_filter` ====Associated Analytic Story==== @@ -1196,7 +982,7 @@ Many service accounts configured within a cloud infrastructure are known to exhi ---- -==={{visible anchor|Abnormally High Number Of Cloud Instances Launched|abnormally-high-number-of-cloud-instances-launched}}=== +===Abnormally High Number Of Cloud Instances Launched=== This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1208,22 +994,22 @@ This search finds for the number successfully created cloud instances for every
====Search==== - -| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h -| `drop_dm_object_name("All_Changes")` -| eval HourOfDay=strftime(_time, "%H") -| eval HourOfDay=floor(HourOfDay/4)*4 -| eval DayOfWeek=strftime(_time, "%w") -| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) -| join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] -| where cardinality >=16 -| apply cloud_excessive_instances_created_v1 threshold=0.005 -| rename "IsOutlier(instances_launched)" as isOutlier -| where isOutlier=1 -| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) -| eval distance_from_threshold = instances_launched - expected_upper_threshold -| table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id -| `abnormally_high_number_of_cloud_instances_launched_filter` + +| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] +| where cardinality >=16 +| apply cloud_excessive_instances_created_v1 threshold=0.005 +| rename "IsOutlier(instances_launched)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| eval distance_from_threshold = instances_launched - expected_upper_threshold +| table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id +| `abnormally_high_number_of_cloud_instances_launched_filter` ====Associated Analytic Story==== @@ -1269,7 +1055,7 @@ Many service accounts configured within an AWS infrastructure are known to exhib ---- -==={{visible anchor|Abnormally High Number Of Cloud Security Group API Calls|abnormally-high-number-of-cloud-security-group-api-calls}}=== +===Abnormally High Number Of Cloud Security Group API Calls=== This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1281,23 +1067,23 @@ This search will detect a spike in the number of API calls made to your cloud in
====Search==== - -| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h -| `drop_dm_object_name("All_Changes")` -| eval HourOfDay=strftime(_time, "%H") -| eval HourOfDay=floor(HourOfDay/4)*4 -| eval DayOfWeek=strftime(_time, "%w") -| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) -| join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] -| where cardinality >=16 -| apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 -| rename "IsOutlier(security_group_api_calls)" as isOutlier -| where isOutlier=1 -| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) -| where security_group_api_calls > expected_upper_threshold -| eval distance_from_threshold = security_group_api_calls - expected_upper_threshold -| table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold -| `abnormally_high_number_of_cloud_security_group_api_calls_filter` + +| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] +| where cardinality >=16 +| apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 +| rename "IsOutlier(security_group_api_calls)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| where security_group_api_calls > expected_upper_threshold +| eval distance_from_threshold = security_group_api_calls - expected_upper_threshold +| table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold +| `abnormally_high_number_of_cloud_security_group_api_calls_filter` ====Associated Analytic Story==== @@ -1343,11 +1129,2444 @@ You must be ingesting your cloud infrastructure logs. You also must run the base ---- -==={{visible anchor|Access LSASS Memory for Dump Creation|access-lsass-memory-for-dump-creation}}=== +===Cloud API Calls From Previously Unseen User Roles=== +This search looks for new commands from each user role. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-09-04 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUserApiCall=min(firstTimeSeen) +| where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") +| table firstTime, user, object, command +|`security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `cloud_api_calls_from_previously_unseen_user_roles_filter` + +====Associated Analytic Story==== + +* Suspicious Cloud User Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Cloud Compute Instance Created By Previously Unseen User=== +This search looks for cloud compute instances created by users who have not created them before. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-08-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUser=min(firstTimeSeen) +| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count vendor_region +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_by_previously_unseen_user_filter` + +====Associated Analytic Story==== + +* Cloud Cryptomining + + +====How To Implement==== +You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Cloud Compute Instance Created In Previously Unused Region=== +This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2020-09-02 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenRegion=min(firstTimeSeen) +| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count , vendor_region +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_in_previously_unused_region_filter` + +====Associated Analytic Story==== + +* Cloud Cryptomining + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Cloud Compute Instance Created With Previously Unseen Image=== +This search looks for cloud compute instances being created with previously unseen image IDs. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': +* '''Last Updated''': 2018-10-12 + +
+
+ +====Search==== + +| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| `drop_dm_object_name("Instance_Changes")` +| where image_id != "unknown" +| lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenImage=min(firstTimeSeen) +| where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") +| table firstTime, user, image_id, count, dest +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_with_previously_unseen_image_filter` + +====Associated Analytic Story==== + +* Cloud Cryptomining + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Cloud Compute Instance Created With Previously Unseen Instance Type=== +Find EC2 instances being created with previously unseen instance types. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': +* '''Last Updated''': 2020-09-12 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| `drop_dm_object_name("Instance_Changes")` +| where instance_type != "unknown" +| lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenInstanceType=min(firstTimeSeen) +| where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count, instance_type +| `security_content_ctime(firstTime)` +| `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` + +====Associated Analytic Story==== + +* Cloud Cryptomining + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Cloud Instance Modified By Previously Unseen User=== +This search looks for cloud instances being modified by users who have not previously modified them. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-07-29 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUser=min(firstTimeSeen) +| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") +| table firstTime user command object_id count +| `security_content_ctime(firstTime)` +| `cloud_instance_modified_by_previously_unseen_user_filter` + +====Associated Analytic Story==== + +* Suspicious Cloud Instance Activities + + +====How To Implement==== +This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Cloud Provisioning Activity From Previously Unseen City=== +This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-10-09 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(City) +| lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenCity=min(firstTimeSeen) +| where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, City, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_city_filter` +| `security_content_ctime(firstTime)` + +====Associated Analytic Story==== + +* Suspicious Cloud Provisioning Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Cloud Provisioning Activity From Previously Unseen Country=== +This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-10-09 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(Country) +| lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenCountry=min(firstTimeSeen) +| where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") +| table firstTime, src, Country, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_country_filter` +| `security_content_ctime(firstTime)` + +====Associated Analytic Story==== + +* Suspicious Cloud Provisioning Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Cloud Provisioning Activity From Previously Unseen IP Address=== +This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-08-16 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenSrc=min(firstTimeSeen) +| where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, user, object_id, command +| `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` +| `security_content_ctime(firstTime)` + +====Associated Analytic Story==== + +* Suspicious Cloud Provisioning Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Cloud Provisioning Activity From Previously Unseen Region=== +This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-08-16 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(Region) +| lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenRegion=min(firstTimeSeen) +| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, Region, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_region_filter` +| `security_content_ctime(firstTime)` + +====Associated Analytic Story==== + +* Suspicious Cloud Provisioning Activities + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Detect AWS Console Login by New User=== +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': +* '''Last Updated''': 2020-05-28 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user +| `drop_dm_object_name(Authentication)` +| inputlookup append=t previously_seen_users_console_logins +| stats min(firstTime) as firstTime max(lastTime) as lastTime by user +| eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") +|where userStatus="First Time Logging into AWS Console" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_aws_console_login_by_new_user_filter` + +====Associated Analytic Story==== + +* Suspicious Cloud Authentication Activities + + +====How To Implement==== +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Detect AWS Console Login by User from New City=== +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2020-10-07 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user City +| join user type=outer [ +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user City +| fields earliestseen user City] +| eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") +| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userCity = "New City" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user City userStatus userCity +| `detect_aws_console_login_by_user_from_new_city_filter` + +====Associated Analytic Story==== + +* Suspicious AWS Login Activities + +* Suspicious Cloud Authentication Activities + + +====How To Implement==== +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Detect AWS Console Login by User from New Country=== +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2020-10-07 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user Country +| join user type=outer [ +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user Country +| fields earliestseen user Country] +| eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") +| eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userCountry = "New Country" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user Country userStatus userCountry +| `detect_aws_console_login_by_user_from_new_country_filter` + +====Associated Analytic Story==== + +* Suspicious AWS Login Activities + +* Suspicious Cloud Authentication Activities + + +====How To Implement==== +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Detect AWS Console Login by User from New Region=== +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour + +* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2020-10-07 + +
+
+ +====Search==== + +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user Region +| join user type=outer [ +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user Region +| fields earliestseen user Region] +| eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") +| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userRegion = "New Region" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user Region userStatus userRegion +| `detect_aws_console_login_by_user_from_new_region_filter` + +====Associated Analytic Story==== + +* Suspicious AWS Login Activities + +* Suspicious Cloud Authentication Activities + + +====How To Implement==== +You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + + +''version'': 1 +
+
+ +---- + +===Detect GCP Storage access from a new IP=== +This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2020-08-10 + +
+
+ +====Search==== +`google_gcp_pubsub_message` +| multikv +| rename sc_status_ as status +| rename cs_object_ as bucket_name +| rename c_ip_ as remote_ip +| rename cs_uri_ as request_uri +| rename cs_method_ as operation +| search status="\"200\"" +| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri +| table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri +| inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri +| outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv +| eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) +| where newIP=1 +| eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") +| eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") +| table first_time last_time bucket_name remote_ip operation request_uri +| `detect_gcp_storage_access_from_a_new_ip_filter` + +====Associated Analytic Story==== + +* Suspicious GCP Storage Activities + + +====How To Implement==== +This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour. It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect New Open GCP Storage Buckets=== +This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2020-08-05 + +
+
+ +====Search==== +`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions +| spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action +| spath output=user path=data.protoPayload.authenticationInfo.principalEmail +| spath output=location path=data.protoPayload.resourceLocation.currentLocations{} +| spath output=src path=data.protoPayload.requestMetadata.callerIp +| spath output=bucketName path=data.protoPayload.resourceName +| spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role +| spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member +| search (member=allUsers AND action=ADD) +| table _time, bucketName, src, user, location, action, role, member +| search `detect_new_open_gcp_storage_buckets_filter` + +====Associated Analytic Story==== + +* Suspicious GCP Storage Activities + + +====How To Implement==== +This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect New Open S3 Buckets over AWS CLI=== +This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== +`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") +| rename requestParameters.bucketName AS bucketName +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_new_open_s3_buckets_over_aws_cli_filter` + +====Associated Analytic Story==== + +* Suspicious AWS S3 Activities + + +====How To Implement==== + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + + +''version'': 1 +
+
+ +---- + +===Detect New Open S3 buckets=== +This search looks for CloudTrail events where a user has created an open/public S3 bucket. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== +`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl +| rex field=_raw "(?{.+})" +| spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} +| search grantees=* +| mvexpand grantees +| spath input=grantees output=uri path=Grantee.URI +| spath input=grantees output=permission path=Permission +| search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") +| search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") +| rename requestParameters.bucketName AS bucketName +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission bucketName +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_new_open_s3_buckets_filter` + +====Associated Analytic Story==== + +* Suspicious AWS S3 Activities + + +====How To Implement==== +You must install the AWS App for Splunk. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + + +''version'': 2 +
+
+ +---- + +===Detect S3 access from a new IP=== +This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2018-06-28 + +
+
+ +====Search==== +`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 +| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip +| inputlookup append=t previously_seen_S3_access_from_remote_ip.csv +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip +| outputlookup previously_seen_S3_access_from_remote_ip.csv +| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newIP=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table bucket_name remote_ip] +| iplocation remote_ip +|rename remote_ip as src_ip +| table _time bucket_name src_ip City Country operation request_uri +| `detect_s3_access_from_a_new_ip_filter` + +====Associated Analytic Story==== + +* Suspicious AWS S3 Activities + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Spike in AWS Security Hub Alerts for EC2 Instance=== +This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance +| bucket span=4h _time +| stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest +| eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev +| eval threshold_value = 3 +| eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) +| search isOutlier=1 +| table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg +| `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` + +====Associated Analytic Story==== + +* AWS Security Hub Alerts + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +None + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json + + +''version'': 3 +
+
+ +---- + +===Detect Spike in AWS Security Hub Alerts for User=== +This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser +| rename findings{}.Resources{}.Id as user +| bucket span=4h _time +| stats count AS alerts by _time user +| eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev +| eval threshold_value = 2 +| eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) +| search isOutlier=1 +| table _time user alerts +|`detect_spike_in_aws_security_hub_alerts_for_user_filter` + +====Associated Analytic Story==== + +* AWS Security Hub Alerts + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +None + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Detect Spike in S3 Bucket deletion=== +This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2018-11-27 + +
+
+ +====Search==== +`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup s3_deletion_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup s3_deletion_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| spath output=bucketName path=requestParameters.bucketName +| stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_s3_bucket_deletion_filter` + +====Associated Analytic Story==== + +* Suspicious AWS S3 Activities + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Spike in blocked Outbound Traffic from your AWS=== +This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2018-05-07 + +
+
+ +====Search==== +`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) +| stats count as numberOfBlockedConnections by src_ip +| inputlookup baseline_blocked_outbound_connections append=t +| fields - latestCount +| stats values(*) as * by src_ip +| rename numberOfBlockedConnections as latestCount +| eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 +| eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) +| eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections +| outputlookup baseline_blocked_outbound_connections +| eval dataPointThreshold = 5, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| table src_ip] +| stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip +| `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` + +====Associated Analytic Story==== + +* AWS Network ACL Activity + +* Suspicious AWS Traffic + +* Command and Control + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Known False Positives==== +The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===High Number of Login Failures from a single source=== +This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1110.001/ T1110.001] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory +| stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app +| search accounts_locked >= 5 +| `high_number_of_login_failures_from_a_single_source_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1110.001 +| Password Guessing +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +unknown + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===New container uploaded to AWS ECR=== +This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1525/ T1525] +* '''Last Updated''': 2020-02-20 + +
+
+ +====Search==== + +| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type +| `drop_dm_object_name("Compute")` +| `new_container_uploaded_to_aws_ecr_filter` + +====Associated Analytic Story==== + +* Container Implantation Monitoring and Investigation + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1525 +| Implant Container Image +| Persistence +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +Uploading container is a normal behavior from developers or users with access to container registry. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===O365 Add App Role Assignment Grant User=== +This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." +| stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_add_app_role_assignment_grant_user_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + +* Cloud Federated Credential Abuse + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.003 +| Cloud Account +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. + +====Reference==== + +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json + + +''version'': 1 +
+
+ +---- + +===O365 Added Service Principal=== +This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." +| stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_added_service_principal_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + +* Cloud Federated Credential Abuse + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.003 +| Cloud Account +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. + +====Reference==== + +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + +* https://www.sygnia.co/golden-saml-advisory + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json + + +''version'': 1 +
+
+ +---- + +===O365 Bypass MFA via Trusted IP=== +This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Search==== +`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy +| rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" +| rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" +| eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") +| mvexpand ip_addresses_new_added +| where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) +|stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account status user_id action +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_bypass_mfa_via_trusted_ip_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== +You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration. + +====Reference==== + +* https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf + +* https://attack.mitre.org/techniques/T1562/007/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json + + +''version'': 1 +
+
+ +---- + +===O365 Disable MFA=== +This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1556/ T1556] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Operation="Disable Strong Authentication." +| stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `o365_disable_mfa_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1556 +| Modify Authentication Process +| Credential Access, Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +Unless it is a special case, it is uncommon to disable MFA or Strong Authentication + +====Reference==== + +* https://attack.mitre.org/techniques/T1556/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json + + +''version'': 1 +
+
+ +---- + +===O365 Excessive Authentication Failures Alert=== +This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1110/ T1110] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed +| stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user +| where count > 10 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `o365_excessive_authentication_failures_alert_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1110 +| Brute Force +| Credential Access +|} + +====Kill Chain Phase==== + +* Not Applicable + + +====Known False Positives==== +The threshold for alert is above 10 attempts and this should reduce the number of false positives. + +====Reference==== + +* https://attack.mitre.org/techniques/T1110/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json + + +''version'': 1 +
+
+ +---- + +===O365 Excessive SSO logon errors=== +This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1556/ T1556] +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired +| stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId +| where count > 5 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_excessive_sso_logon_errors_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + +* Cloud Federated Credential Abuse + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1556 +| Modify Authentication Process +| Credential Access, Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack. + +====Reference==== + +* https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json + + +''version'': 1 +
+
+ +---- + +===O365 New Federated Domain Added=== +This search detects the addition of a new Federated domain. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] +* '''Last Updated''': 2021-01-26 + +
+
+ +====Search==== +`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" +| stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `o365_new_federated_domain_added_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + +* Cloud Federated Credential Abuse + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.003 +| Cloud Account +| Persistence +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider. + +====Reference==== + +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + +* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + +* https://www.sygnia.co/golden-saml-advisory + +* https://o365blog.com/post/aadbackdoor/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json + + +''version'': 1 +
+
+ +---- + +===O365 PST export alert=== +This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114/ T1114] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" +| stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `o365_pst_export_alert_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== +You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114 +| Email Collection +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objective + + +====Known False Positives==== +PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored. + +====Reference==== + +* https://attack.mitre.org/techniques/T1114/ + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json + + +''version'': 1 +
+
+ +---- + +===O365 Suspicious Admin Email Forwarding=== +This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.003/ T1114.003] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Operation=Set-Mailbox +| spath input=Parameters +| rename Identity AS src_user +| search ForwardingAddress=* +| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress +| where count_src_user > 1 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`o365_suspicious_admin_email_forwarding_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.003 +| Email Forwarding Rule +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +unknown + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json + + +''version'': 1 +
+
+ +---- + +===O365 Suspicious Rights Delegation=== +This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.002/ T1114.002] +* '''Last Updated''': 2020-12-15 + +
+
+ +====Search==== +`o365_management_activity` Operation=Add-MailboxPermission +| spath input=Parameters +| rename User AS src_user, Identity AS dest_user +| search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf +| stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`o365_suspicious_rights_delegation_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.002 +| Remote Email Collection +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Service Accounts + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json + + +''version'': 1 +
+
+ +---- + +===O365 Suspicious User Email Forwarding=== +This search detects when multiple user configured a forwarding rule to the same destination. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.003/ T1114.003] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== +`o365_management_activity` Operation=Set-Mailbox +| spath input=Parameters +| rename Identity AS src_user +| search ForwardingSmtpAddress=* +| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress +| where count_src_user > 1 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`o365_suspicious_user_email_forwarding_filter` + +====Associated Analytic Story==== + +* Office 365 Detections + + +====How To Implement==== + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.003 +| Email Forwarding Rule +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +unknown + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json + + +''version'': 1 +
+
+ +---- + + + +==Endpoint== + + +===Access LSASS Memory for Dump Creation=== Detect memory dumping of the LSASS process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] * '''Last Updated''': 2019-12-06 @@ -1355,12 +3574,12 @@ Detect memory dumping of the LSASS process.
====Search==== -`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId -| rename Computer as dest +`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId +| rename Computer as dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `access_lsass_memory_for_dump_creation_filter` +| `security_content_ctime(lastTime)` +| `access_lsass_memory_for_dump_creation_filter` ====Associated Analytic Story==== @@ -1408,11 +3627,11 @@ Administrators can create memory dumps for debugging purposes, but memory dumps ---- -==={{visible anchor|Applying Stolen Credentials via Mimikatz modules|applying-stolen-credentials-via-mimikatz-modules}}=== +===Applying Stolen Credentials via Mimikatz modules=== This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1547/ T1547], [https://attack.mitre.org/techniques/T1548/ T1548], [https://attack.mitre.org/techniques/T1554/ T1554], [https://attack.mitre.org/techniques/T1556/ T1556], [https://attack.mitre.org/techniques/T1558/ T1558] * '''Last Updated''': 2020-11-03 @@ -1420,14 +3639,14 @@ This detection indicates use of Mimikatz modules that facilitate Pass-the-Token
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)kerberos::ptt/)=true OR match_regex(cmd_line, /(?i)kerberos::golden/)=true OR match_regex(cmd_line, /(?i)kerberos::silver/)=true OR match_regex(cmd_line, /(?i)misc::skeleton/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -1521,11 +3740,11 @@ None identified. ---- -==={{visible anchor|Applying Stolen Credentials via PowerSploit modules|applying-stolen-credentials-via-powersploit-modules}}=== +===Applying Stolen Credentials via PowerSploit modules=== Stolen credentials are applied by methods such as user impersonation, credential injection, spoofing of authentication processes or getting hold of critical accounts. This detection indicates such activities carried out by PowerSploit exploit kit APIs. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1547/ T1547], [https://attack.mitre.org/techniques/T1548/ T1548], [https://attack.mitre.org/techniques/T1554/ T1554], [https://attack.mitre.org/techniques/T1556/ T1556], [https://attack.mitre.org/techniques/T1558/ T1558] * '''Last Updated''': 2020-11-03 @@ -1533,14 +3752,14 @@ Stolen credentials are applied by methods such as user impersonation, credential
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-CredentialInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-TokenManipulation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserImpersonation/)=true OR match_regex(cmd_line, /(?i)Get-System/)=true OR match_regex(cmd_line, /(?i)Invoke-RevertToSelf/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -1632,11 +3851,11 @@ None identified. ---- -==={{visible anchor|Assessment of Credential Strength via DSInternals modules|assessment-of-credential-strength-via-dsinternals-modules}}=== +===Assessment of Credential Strength via DSInternals modules=== This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1087/ T1087], [https://attack.mitre.org/techniques/T1201/ T1201], [https://attack.mitre.org/techniques/T1552/ T1552], [https://attack.mitre.org/techniques/T1555/ T1555] * '''Last Updated''': 2020-11-03 @@ -1644,14 +3863,14 @@ This detection identifies use of DSInternals modules that verify password streng
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-PasswordQuality/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -1723,7 +3942,7 @@ None identified. ---- -==={{visible anchor|Attempt To Add Certificate To Untrusted Store|attempt-to-add-certificate-to-untrusted-store}}=== +===Attempt To Add Certificate To Untrusted Store=== Attempt to add a certificate to the certificate store * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1735,12 +3954,12 @@ Attempt to add a certificate to the certificate store
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `attempt_to_add_certificate_to_untrusted_store_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `attempt_to_add_certificate_to_untrusted_store_filter` ====Associated Analytic Story==== @@ -1788,7 +4007,7 @@ There may be legitimate reasons for administrators to add a certificate to the u ---- -==={{visible anchor|Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass|attempt-to-set-default-powershell-execution-policy-to-unrestricted-or-bypass}}=== +===Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass=== Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1800,12 +4019,12 @@ Monitor for changes of the ExecutionPolicy in the registry to the values "unrest
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest -| `drop_dm_object_name(Registry)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest +| `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter` +|`security_content_ctime(lastTime)` +| `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter` ====Associated Analytic Story==== @@ -1855,7 +4074,7 @@ Administrators may attempt to change the default execution policy on a system fo ---- -==={{visible anchor|Attempt To Stop Security Service|attempt-to-stop-security-service}}=== +===Attempt To Stop Security Service=== This search looks for attempts to stop security-related services on the endpoint. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1867,14 +4086,14 @@ This search looks for attempts to stop security-related services on the endpoint
====Search==== - -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -|lookup security_services_lookup service as process OUTPUTNEW category, description -| search category=security -| `attempt_to_stop_security_service_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|lookup security_services_lookup service as process OUTPUTNEW category, description +| search category=security +| `attempt_to_stop_security_service_filter` ====Associated Analytic Story==== @@ -1922,7 +4141,7 @@ None identified. Attempts to disable security-related services should be identif ---- -==={{visible anchor|Attempted Credential Dump From Registry via Reg exe|attempted-credential-dump-from-registry-via-reg-exe}}=== +===Attempted Credential Dump From Registry via Reg exe=== Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1934,12 +4153,12 @@ Monitor for execution of reg.exe with parameters specifying an export of keys th
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `attempted_credential_dump_from_registry_via_reg_exe_filter` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` ====Associated Analytic Story==== @@ -1985,11 +4204,11 @@ None identified. ---- -==={{visible anchor|Attempted Credential Dump From Registry via Reg exe|attempted-credential-dump-from-registry-via-reg-exe}}=== +===Attempted Credential Dump From Registry via Reg exe=== Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-6-04 @@ -1997,14 +4216,14 @@ Monitor for execution of reg.exe with parameters specifying an export of keys th
====Search==== - -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) -| eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) -| where process_name="cmd.exe" OR process_name="reg.exe" -| where cmd_line != null AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" -| into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) +| where process_name="cmd.exe" OR process_name="reg.exe" +| where cmd_line != null AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -2060,7 +4279,7 @@ None identified. ---- -==={{visible anchor|BCDEdit Failure Recovery Modification|bcdedit-failure-recovery-modification}}=== +===BCDEdit Failure Recovery Modification=== This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2072,12 +4291,12 @@ This search looks for flags passed to bcdedit.exe modifications to the built-in
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `bcdedit_failure_recovery_modification_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `bcdedit_failure_recovery_modification_filter` ====Associated Analytic Story==== @@ -2127,7 +4346,7 @@ Administrators may modify the boot configuration. ---- -==={{visible anchor|Batch File Write to System32|batch-file-write-to-system32}}=== +===Batch File Write to System32=== The search looks for a batch file (.bat) written to the Windows system directory tree. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2139,14 +4358,14 @@ The search looks for a batch file (.bat) written to the Windows system directory
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` -| rex field=file_name "(?\.[^\.]+)$" -| search file_path=*system32* AND file_extension=.bat -| `batch_file_write_to_system32_filter` +| rex field=file_name "(?\.[^\.]+)$" +| search file_path=*system32* AND file_extension=.bat +| `batch_file_write_to_system32_filter` ====Associated Analytic Story==== @@ -2192,24 +4411,24 @@ It is possible for this search to generate a notable event for a batch file writ ---- -==={{visible anchor|Certutil exe certificate extraction|certutil-exe-certificate-extraction}}=== +===Certutil exe certificate extraction=== This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2021-01-26
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `certutil_exe_certificate_extraction_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `certutil_exe_certificate_extraction_filter` ====Associated Analytic Story==== @@ -2253,676 +4472,7 @@ Unless there are specific use cases, manipulating or exporting certificates usin ---- -==={{visible anchor|Cloud API Calls From Previously Unseen User Roles|cloud-api-calls-from-previously-unseen-user-roles}}=== -This search looks for new commands from each user role. - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Change -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] -* '''Last Updated''': 2020-09-04 - -
-
- -====Search==== - -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object -| `drop_dm_object_name("All_Changes")` -| lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenUserApiCall=min(firstTimeSeen) -| where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") -| table firstTime, user, object, command -|`security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `cloud_api_calls_from_previously_unseen_user_roles_filter` - -====Associated Analytic Story==== - -* Suspicious Cloud User Activities - - -====How To Implement==== -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter` - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Cloud Compute Instance Created By Previously Unseen User|cloud-compute-instance-created-by-previously-unseen-user}}=== -This search looks for cloud compute instances created by users who have not created them before. - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Change -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] -* '''Last Updated''': 2020-08-21 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region -| `drop_dm_object_name("All_Changes")` -| lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenUser=min(firstTimeSeen) -| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") -| table firstTime, user, dest, count vendor_region -| `security_content_ctime(firstTime)` -| `cloud_compute_instance_created_by_previously_unseen_user_filter` - -====Associated Analytic Story==== - -* Cloud Cryptomining - - -====How To Implement==== -You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Cloud Compute Instance Created In Previously Unused Region|cloud-compute-instance-created-in-previously-unused-region}}=== -This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Change -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] -* '''Last Updated''': 2020-09-02 - -
-
- -====Search==== - -| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user -| `drop_dm_object_name("All_Changes")` -| lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenRegion=min(firstTimeSeen) -| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") -| table firstTime, user, dest, count , vendor_region -| `security_content_ctime(firstTime)` -| `cloud_compute_instance_created_in_previously_unused_region_filter` - -====Associated Analytic Story==== - -* Cloud Cryptomining - - -====How To Implement==== -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Cloud Compute Instance Created With Previously Unseen Image|cloud-compute-instance-created-with-previously-unseen-image}}=== -This search looks for cloud compute instances being created with previously unseen image IDs. - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Change -* '''ATT&CK''': -* '''Last Updated''': 2018-10-12 - -
-
- -====Search==== - -| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user -| `drop_dm_object_name("All_Changes")` -| `drop_dm_object_name("Instance_Changes")` -| where image_id != "unknown" -| lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenImage=min(firstTimeSeen) -| where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") -| table firstTime, user, image_id, count, dest -| `security_content_ctime(firstTime)` -| `cloud_compute_instance_created_with_previously_unseen_image_filter` - -====Associated Analytic Story==== - -* Cloud Cryptomining - - -====How To Implement==== -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Cloud Compute Instance Created With Previously Unseen Instance Type|cloud-compute-instance-created-with-previously-unseen-instance-type}}=== -Find EC2 instances being created with previously unseen instance types. - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Change -* '''ATT&CK''': -* '''Last Updated''': 2020-09-12 - -
-
- -====Search==== - -| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user -| `drop_dm_object_name("All_Changes")` -| `drop_dm_object_name("Instance_Changes")` -| where instance_type != "unknown" -| lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenInstanceType=min(firstTimeSeen) -| where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") -| table firstTime, user, dest, count, instance_type -| `security_content_ctime(firstTime)` -| `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` - -====Associated Analytic Story==== - -* Cloud Cryptomining - - -====How To Implement==== -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Cloud Instance Modified By Previously Unseen User|cloud-instance-modified-by-previously-unseen-user}}=== -This search looks for cloud instances being modified by users who have not previously modified them. - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Change -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] -* '''Last Updated''': 2020-07-29 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user -| `drop_dm_object_name("All_Changes")` -| lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenUser=min(firstTimeSeen) -| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") -| table firstTime user command object_id count -| `security_content_ctime(firstTime)` -| `cloud_instance_modified_by_previously_unseen_user_filter` - -====Associated Analytic Story==== - -* Suspicious Cloud Instance Activities - - -====How To Implement==== -This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Cloud Provisioning Activity From Previously Unseen City|cloud-provisioning-activity-from-previously-unseen-city}}=== -This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Change -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] -* '''Last Updated''': 2020-10-09 - -
-
- -====Search==== - -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command -| `drop_dm_object_name("All_Changes")` -| iplocation src -| where isnotnull(City) -| lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenCity=min(firstTimeSeen) -| where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) -| table firstTime, src, City, user, object, command -| `cloud_provisioning_activity_from_previously_unseen_city_filter` -| `security_content_ctime(firstTime)` - -====Associated Analytic Story==== - -* Suspicious Cloud Provisioning Activities - - -====How To Implement==== -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Cloud Provisioning Activity From Previously Unseen Country|cloud-provisioning-activity-from-previously-unseen-country}}=== -This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Change -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] -* '''Last Updated''': 2020-10-09 - -
-
- -====Search==== - -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command -| `drop_dm_object_name("All_Changes")` -| iplocation src -| where isnotnull(Country) -| lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenCountry=min(firstTimeSeen) -| where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") -| table firstTime, src, Country, user, object, command -| `cloud_provisioning_activity_from_previously_unseen_country_filter` -| `security_content_ctime(firstTime)` - -====Associated Analytic Story==== - -* Suspicious Cloud Provisioning Activities - - -====How To Implement==== -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Cloud Provisioning Activity From Previously Unseen IP Address|cloud-provisioning-activity-from-previously-unseen-ip-address}}=== -This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Change -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] -* '''Last Updated''': 2020-08-16 - -
-
- -====Search==== - -| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command -| `drop_dm_object_name("All_Changes")` -| lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenSrc=min(firstTimeSeen) -| where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) -| table firstTime, src, user, object_id, command -| `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` -| `security_content_ctime(firstTime)` - -====Associated Analytic Story==== - -* Suspicious Cloud Provisioning Activities - - -====How To Implement==== -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Cloud Provisioning Activity From Previously Unseen Region|cloud-provisioning-activity-from-previously-unseen-region}}=== -This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Change -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] -* '''Last Updated''': 2020-08-16 - -
-
- -====Search==== - -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command -| `drop_dm_object_name("All_Changes")` -| iplocation src -| where isnotnull(Region) -| lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenRegion=min(firstTimeSeen) -| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) -| table firstTime, src, Region, user, object, command -| `cloud_provisioning_activity_from_previously_unseen_region_filter` -| `security_content_ctime(firstTime)` - -====Associated Analytic Story==== - -* Suspicious Cloud Provisioning Activities - - -====How To Implement==== -You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Common Ransomware Extensions|common-ransomware-extensions}}=== +===Common Ransomware Extensions=== The search looks for file modifications with extensions commonly used by Ransomware * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2934,14 +4484,14 @@ The search looks for file modifications with extensions commonly used by Ransomw
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` -| rex field=file_name "(?\.[^\.]+)$" -| `ransomware_extensions` -| `common_ransomware_extensions_filter` +| rex field=file_name "(?\.[^\.]+)$" +| `ransomware_extensions` +| `common_ransomware_extensions_filter` ====Associated Analytic Story==== @@ -2995,7 +4545,7 @@ It is possible for a legitimate file with these extensions to be created. If thi ---- -==={{visible anchor|Common Ransomware Notes|common-ransomware-notes}}=== +===Common Ransomware Notes=== The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3007,13 +4557,13 @@ The search looks for files created with names matching those typically used in r
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `ransomware_notes` -| `common_ransomware_notes_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `ransomware_notes` +| `common_ransomware_notes_filter` ====Associated Analytic Story==== @@ -3063,11 +4613,11 @@ It's possible that a legitimate file could be created with the same name used by ---- -==={{visible anchor|Create Remote Thread into LSASS|create-remote-thread-into-lsass}}=== +===Create Remote Thread into LSASS=== Detect remote thread creation into LSASS consistent with credential dumping. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] * '''Last Updated''': 2019-12-06 @@ -3075,12 +4625,12 @@ Detect remote thread creation into LSASS consistent with credential dumping.
====Search==== -`sysmon` EventID=8 TargetImage=*lsass.exe -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId -| rename Computer as dest +`sysmon` EventID=8 TargetImage=*lsass.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId +| rename Computer as dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `create_remote_thread_into_lsass_filter` +| `security_content_ctime(lastTime)` +| `create_remote_thread_into_lsass_filter` ====Associated Analytic Story==== @@ -3128,7 +4678,7 @@ Other tools can access LSASS for legitimate reasons and generate an event. In th ---- -==={{visible anchor|Create local admin accounts using net exe|create-local-admin-accounts-using-net-exe}}=== +===Create local admin accounts using net exe=== This search looks for the creation of local administrator accounts using net.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3140,12 +4690,12 @@ This search looks for the creation of local administrator accounts using net.exe
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -|`create_local_admin_accounts_using_net_exe_filter` +| `security_content_ctime(lastTime)` +|`create_local_admin_accounts_using_net_exe_filter` ====Associated Analytic Story==== @@ -3195,7 +4745,7 @@ Administrators often leverage net.exe to create admin accounts. ---- -==={{visible anchor|Create or delete windows shares using net exe|create-or-delete-windows-shares-using-net-exe}}=== +===Create or delete windows shares using net exe=== This search looks for the creation or deletion of hidden shares using net.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3207,13 +4757,13 @@ This search looks for the creation or deletion of hidden shares using net.exe.
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| search process=*share* -| `create_or_delete_windows_shares_using_net_exe_filter` +| `security_content_ctime(lastTime)` +| search process=*share* +| `create_or_delete_windows_shares_using_net_exe_filter` ====Associated Analytic Story==== @@ -3261,7 +4811,7 @@ Administrators often leverage net.exe to create or delete network shares. You sh ---- -==={{visible anchor|Creation of Shadow Copy|creation-of-shadow-copy}}=== +===Creation of Shadow Copy=== Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3273,12 +4823,12 @@ Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy.
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `creation_of_shadow_copy_filter` +| `security_content_ctime(lastTime)` +| `creation_of_shadow_copy_filter` ====Associated Analytic Story==== @@ -3326,7 +4876,7 @@ Legitimate administrator usage of Vssadmin or Wmic will create false positives. ---- -==={{visible anchor|Creation of Shadow Copy with wmic and powershell|creation-of-shadow-copy-with-wmic-and-powershell}}=== +===Creation of Shadow Copy with wmic and powershell=== This search detects the use of wmic and Powershell to create a shadow copy. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3338,12 +4888,12 @@ This search detects the use of wmic and Powershell to create a shadow copy.
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `creation_of_shadow_copy_with_wmic_and_powershell_filter` +| `security_content_ctime(lastTime)` +| `creation_of_shadow_copy_with_wmic_and_powershell_filter` ====Associated Analytic Story==== @@ -3391,11 +4941,11 @@ Legtimate administrator usage of wmic to create a shadow copy. ---- -==={{visible anchor|Creation of lsass Dump with Taskmgr|creation-of-lsass-dump-with-taskmgr}}=== +===Creation of lsass Dump with Taskmgr=== Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] * '''Last Updated''': 2020-02-03 @@ -3403,12 +4953,12 @@ Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess
====Search==== -`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `creation_of_lsass_dump_with_taskmgr_filter` +`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `creation_of_lsass_dump_with_taskmgr_filter` ====Associated Analytic Story==== @@ -3460,7 +5010,7 @@ Administrators can create memory dumps for debugging purposes, but memory dumps ---- -==={{visible anchor|Credential Dumping via Copy Command from Shadow Copy|credential-dumping-via-copy-command-from-shadow-copy}}=== +===Credential Dumping via Copy Command from Shadow Copy=== This search detects credential dumping using copy command from a shadow copy. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3472,12 +5022,12 @@ This search detects credential dumping using copy command from a shadow copy.
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `credential_dumping_via_copy_command_from_shadow_copy_filter` +| `security_content_ctime(lastTime)` +| `credential_dumping_via_copy_command_from_shadow_copy_filter` ====Associated Analytic Story==== @@ -3525,7 +5075,7 @@ unknown ---- -==={{visible anchor|Credential Dumping via Symlink to Shadow Copy|credential-dumping-via-symlink-to-shadow-copy}}=== +===Credential Dumping via Symlink to Shadow Copy=== This search detects the creation of a symlink to a shadow copy. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3537,12 +5087,12 @@ This search detects the creation of a symlink to a shadow copy.
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `credential_dumping_via_symlink_to_shadow_copy_filter` +| `security_content_ctime(lastTime)` +| `credential_dumping_via_symlink_to_shadow_copy_filter` ====Associated Analytic Story==== @@ -3590,11 +5140,11 @@ unknown ---- -==={{visible anchor|Credential Extraction indicative of FGDump and CacheDump with s option|credential-extraction-indicative-of-fgdump-and-cachedump-with-s-option}}=== +===Credential Extraction indicative of FGDump and CacheDump with s option=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-18 @@ -3602,14 +5152,14 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND process_name != null AND parent_process_name != null AND match_regex(parent_process_name, /(?i)System32\\services.exe/)=true AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-s/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -3665,11 +5215,11 @@ None identified. ---- -==={{visible anchor|Credential Extraction indicative of FGDump and CacheDump with v option|credential-extraction-indicative-of-fgdump-and-cachedump-with-v-option}}=== +===Credential Extraction indicative of FGDump and CacheDump with v option=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-18 @@ -3677,14 +5227,14 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null) | where cmd_line != null AND process_name != null AND process_path != null AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-v/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -3738,11 +5288,11 @@ None identified. ---- -==={{visible anchor|Credential Extraction indicative of Lazagne command line options|credential-extraction-indicative-of-lazagne-command-line-options}}=== +===Credential Extraction indicative of Lazagne command line options=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003], [https://attack.mitre.org/techniques/T1555/ T1555] * '''Last Updated''': 2020-10-18 @@ -3750,14 +5300,14 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)all\s+\-oA\s+\-output/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -3811,11 +5361,11 @@ None identified. ---- -==={{visible anchor|Credential Extraction indicative of use of DSInternals credential conversion modules|credential-extraction-indicative-of-use-of-dsinternals-credential-conversion-modules}}=== +===Credential Extraction indicative of use of DSInternals credential conversion modules=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-21 @@ -3823,14 +5373,14 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)ConvertFrom-ADManagedPasswordBlob/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-UnicodePassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-KerberosKey/)=true OR match_regex(cmd_line, /(?i)ConvertTo-LMHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-NTHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-OrgIdHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-UnicodePassword/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -3888,11 +5438,11 @@ None identified. ---- -==={{visible anchor|Credential Extraction indicative of use of DSInternals modules|credential-extraction-indicative-of-use-of-dsinternals-modules}}=== +===Credential Extraction indicative of use of DSInternals modules=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-21 @@ -3900,14 +5450,14 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ADDBBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Get-ADDBKdsRootKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBSchemaAttribute/)=true OR match_regex(cmd_line, /(?i)Get-ADKeyCredential/)=true OR match_regex(cmd_line, /(?i)Get-ADReplAccount/)=true OR match_regex(cmd_line, /(?i)Get-ADReplBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADSIAccount/)=true OR match_regex(cmd_line, /(?i)Get-AzureADUserEx/)=true OR match_regex(cmd_line, /(?i)Get-BootKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaPolicyInformation/)=true OR match_regex(cmd_line, /(?i)Get-SamPasswordPolicy/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -3965,11 +5515,11 @@ None identified. ---- -==={{visible anchor|Credential Extraction indicative of use of Mimikatz modules|credential-extraction-indicative-of-use-of-mimikatz-modules}}=== +===Credential Extraction indicative of use of Mimikatz modules=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-21 @@ -3977,14 +5527,14 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)CRYPTO::Certificates/)=true OR match_regex(cmd_line, /(?i)CRYPTO::keys/)=true OR match_regex(cmd_line, /(?i)kerberos::list/)=true OR match_regex(cmd_line, /(?i)kerberos::tgt/)=true OR match_regex(cmd_line, /(?i)lsadump::sam/)=true OR match_regex(cmd_line, /(?i)lsadump::secrets/)=true OR match_regex(cmd_line, /(?i)lsadump::cache/)=true OR match_regex(cmd_line, /(?i)lsadump::lsa/)=true OR match_regex(cmd_line, /(?i)lsadump::trust/)=true OR match_regex(cmd_line, /(?i)lsadump::backupkeys/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -4036,11 +5586,11 @@ None identified. ---- -==={{visible anchor|Credential Extraction indicative of use of PowerSploit modules|credential-extraction-indicative-of-use-of-powersploit-modules}}=== +===Credential Extraction indicative of use of PowerSploit modules=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-21 @@ -4048,14 +5598,14 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ApplicationHost/)=true OR match_regex(cmd_line, /(?i)Get-CachedGPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-GPPAutologon/)=true OR match_regex(cmd_line, /(?i)Get-GPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-RegistryAutoLogon/)=true OR match_regex(cmd_line, /(?i)Get-SiteListPassword/)=true OR match_regex(cmd_line, /(?i)Get-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Request-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Get-VaultCredential/)=true OR match_regex(cmd_line, /(?i)Invoke-Kerberoast/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -4107,11 +5657,11 @@ None identified. ---- -==={{visible anchor|Credential Extraction native Microsoft debuggers peek into the kernel|credential-extraction-native-microsoft-debuggers-peek-into-the-kernel}}=== +===Credential Extraction native Microsoft debuggers peek into the kernel=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-18 @@ -4119,14 +5669,14 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND parent_process_name != null AND process_name != null AND ( match_regex(parent_process_name, /(?i)ntkd\.exe/)=true OR match_regex(parent_process_name, /(?i)livekd\.exe/)=true ) AND match_regex(process_name, /(?i)conhost\.exe/)=true AND match_regex(cmd_line, /(?i)0xffffffff/)=true AND match_regex(cmd_line, /(?i)\-ForceV1/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -4182,11 +5732,11 @@ Although unlikely, using debuggers this way may be indicative of developers anal ---- -==={{visible anchor|Credential Extraction native Microsoft debuggers via z command line option|credential-extraction-native-microsoft-debuggers-via-z-command-line-option}}=== +===Credential Extraction native Microsoft debuggers via z command line option=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-18 @@ -4194,14 +5744,14 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) | where cmd_line != null AND process_name != null AND ( match_regex(process_name, /^(?i)ntkd\.exe/)=true OR match_regex(process_name, /^(?i)kd\.exe/)=true ) AND match_regex(cmd_line, /(?i)\-z\s+/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -4253,11 +5803,11 @@ Although unlikely, using debuggers this way may be indicative of developers anal ---- -==={{visible anchor|Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals|credential-extraction-via-get-addbaccount-module-present-in-powersploit-and-dsinternals}}=== +===Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-18 @@ -4265,15 +5815,15 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)Get-ADDBAccount/)=true AND match_regex(cmd_line, /(?i)\-dbpath[\s;:\.\ |]+/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -4323,155 +5873,7 @@ None identified. ---- -==={{visible anchor|DNS Query Length Outliers - MLTK|dns-query-length-outliers---mltk}}=== -This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Resolution -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.004/ T1071.004] -* '''Last Updated''': 2020-01-22 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type -| search DNS.record_type=* -| `drop_dm_object_name(DNS)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| eval query_length = len(query) -| apply dns_query_pdfmodel threshold=0.01 -| rename "IsOutlier(query_length)" as isOutlier -| search isOutlier > 0 -| sort -query_length -| table start_time end_time query record_type count src dest query_length -| `dns_query_length_outliers___mltk_filter` - -====Associated Analytic Story==== - -* Hidden Cobra Malware - -* Suspicious DNS Traffic - -* Command and Control - - -====How To Implement==== -To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ -This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ -1. \ -1. **Label:** DNS Query Length, **Field:** query_length\ -1. \ -1. **Label:** Number of events, **Field:** count\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1071.004 -| DNS -| Command and Control -|} - -====Kill Chain Phase==== - -* Command and Control - - -====Known False Positives==== -If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. - -====Reference==== - - -====Test Dataset==== - - -''version'': 2 -
-
- ----- - -==={{visible anchor|DNS Query Length With High Standard Deviation|dns-query-length-with-high-standard-deviation}}=== -This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Resolution -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003] -* '''Last Updated''': 2021-01-18 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query -| `drop_dm_object_name("DNS")` -| eval query_length = len(query) -| table query query_length record_type count -| eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50 -| where query_length>(avg+stdev*2) -| eval z_score=(query_length-avg)/stdev -| `dns_query_length_with_high_standard_deviation_filter` - -====Associated Analytic Story==== - -* Hidden Cobra Malware - -* Suspicious DNS Traffic - -* Command and Control - - -====How To Implement==== -To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1048.003 -| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol -| Exfiltration -|} - -====Kill Chain Phase==== - -* Command and Control - - -====Known False Positives==== -It's possible there can be long domain names that are legitimate. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log - - -''version'': 3 -
-
- ----- - -==={{visible anchor|Deleting Shadow Copies|deleting-shadow-copies}}=== +===Deleting Shadow Copies=== The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4483,12 +5885,12 @@ The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `deleting_shadow_copies_filter` +| `security_content_ctime(lastTime)` +| `deleting_shadow_copies_filter` ====Associated Analytic Story==== @@ -4538,299 +5940,11 @@ vssadmin.exe and wmic.exe are standard applications shipped with modern versions ---- -==={{visible anchor|Detect AWS Console Login by New User|detect-aws-console-login-by-new-user}}=== -This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Authentication -* '''ATT&CK''': -* '''Last Updated''': 2020-05-28 - -
-
- -====Search==== - -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user -| `drop_dm_object_name(Authentication)` -| inputlookup append=t previously_seen_users_console_logins -| stats min(firstTime) as firstTime max(lastTime) as lastTime by user -| eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") -|where userStatus="First Time Logging into AWS Console" -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_aws_console_login_by_new_user_filter` - -====Associated Analytic Story==== - -* Suspicious Cloud Authentication Activities - - -====How To Implement==== -You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect AWS Console Login by User from New City|detect-aws-console-login-by-user-from-new-city}}=== -This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Authentication -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] -* '''Last Updated''': 2020-10-07 - -
-
- -====Search==== - -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src -| iplocation Authentication.src -| `drop_dm_object_name(Authentication)` -| table firstTime lastTime user City -| join user type=outer [ -| inputlookup previously_seen_users_console_logins -| stats earliest(firstTime) AS earliestseen by user City -| fields earliestseen user City] -| eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") -| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") -| where userCity = "New City" AND userStatus != "Old User" -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| table firstTime lastTime user City userStatus userCity -| `detect_aws_console_login_by_user_from_new_city_filter` - -====Associated Analytic Story==== - -* Suspicious AWS Login Activities - -* Suspicious Cloud Authentication Activities - - -====How To Implement==== -You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect AWS Console Login by User from New Country|detect-aws-console-login-by-user-from-new-country}}=== -This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Authentication -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] -* '''Last Updated''': 2020-10-07 - -
-
- -====Search==== - -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src -| iplocation Authentication.src -| `drop_dm_object_name(Authentication)` -| table firstTime lastTime user Country -| join user type=outer [ -| inputlookup previously_seen_users_console_logins -| stats earliest(firstTime) AS earliestseen by user Country -| fields earliestseen user Country] -| eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") -| eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") -| where userCountry = "New Country" AND userStatus != "Old User" -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| table firstTime lastTime user Country userStatus userCountry -| `detect_aws_console_login_by_user_from_new_country_filter` - -====Associated Analytic Story==== - -* Suspicious AWS Login Activities - -* Suspicious Cloud Authentication Activities - - -====How To Implement==== -You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect AWS Console Login by User from New Region|detect-aws-console-login-by-user-from-new-region}}=== -This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour - -* '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Authentication -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] -* '''Last Updated''': 2020-10-07 - -
-
- -====Search==== - -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src -| iplocation Authentication.src -| `drop_dm_object_name(Authentication)` -| table firstTime lastTime user Region -| join user type=outer [ -| inputlookup previously_seen_users_console_logins -| stats earliest(firstTime) AS earliestseen by user Region -| fields earliestseen user Region] -| eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") -| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") -| where userRegion = "New Region" AND userStatus != "Old User" -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| table firstTime lastTime user Region userStatus userRegion -| `detect_aws_console_login_by_user_from_new_region_filter` - -====Associated Analytic Story==== - -* Suspicious AWS Login Activities - -* Suspicious Cloud Authentication Activities - - -====How To Implement==== -You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Activity Related to Pass the Hash Attacks|detect-activity-related-to-pass-the-hash-attacks}}=== +===Detect Activity Related to Pass the Hash Attacks=== This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1550.002/ T1550.002] * '''Last Updated''': 2020-10-15 @@ -4838,12 +5952,12 @@ This search looks for specific authentication events from the Windows Security E
====Search==== -`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) -| fillnull -| stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest +`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_activity_related_to_pass_the_hash_attacks_filter` +| `security_content_ctime(lastTime)` +| `detect_activity_related_to_pass_the_hash_attacks_filter` ====Associated Analytic Story==== @@ -4889,11 +6003,11 @@ Legitimate logon activity by authorized NTLM systems may be detected by this sea ---- -==={{visible anchor|Detect Computer Changed with Anonymous Account|detect-computer-changed-with-anonymous-account}}=== +===Detect Computer Changed with Anonymous Account=== This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1210/ T1210] * '''Last Updated''': 2020-09-18 @@ -4901,9 +6015,9 @@ This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An ac
====Search==== -`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 -| stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user -| `detect_computer_changed_with_anonymous_account_filter` +`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 +| stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user +| `detect_computer_changed_with_anonymous_account_filter` ====Associated Analytic Story==== @@ -4949,11 +6063,11 @@ None thus far found ---- -==={{visible anchor|Detect Credential Dumping through LSASS access|detect-credential-dumping-through-lsass-access}}=== +===Detect Credential Dumping through LSASS access=== This search looks for reading lsass memory consistent with credential dumping. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] * '''Last Updated''': 2019-12-03 @@ -4961,12 +6075,12 @@ This search looks for reading lsass memory consistent with credential dumping.
====Search==== -`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess -| rename Computer as dest +`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess +| rename Computer as dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_credential_dumping_through_lsass_access_filter` +| `security_content_ctime(lastTime)` +| `detect_credential_dumping_through_lsass_access_filter` ====Associated Analytic Story==== @@ -5014,11 +6128,11 @@ The activity may be legitimate. Other tools can access lsass for legitimate reas ---- -==={{visible anchor|Detect Dump LSASS Memory using comsvcs|detect-dump-lsass-memory-using-comsvcs}}=== +===Detect Dump LSASS Memory using comsvcs=== This search detects the memory of lsass.exe being dumped for offline credential theft attack. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.003/ T1003.003] * '''Last Updated''': 2020-09-15 @@ -5026,12 +6140,12 @@ This search detects the memory of lsass.exe being dumped for offline credential
====Search==== - -| from read_ssa_enriched_events() -| eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)) -| where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body = "TBD" -| into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)) +| where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5087,7 +6201,7 @@ None identified. ---- -==={{visible anchor|Detect Excessive Account Lockouts From Endpoint|detect-excessive-account-lockouts-from-endpoint}}=== +===Detect Excessive Account Lockouts From Endpoint=== This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5099,14 +6213,14 @@ This search identifies endpoints that have caused a relatively high number of ac
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result -|`drop_dm_object_name("All_Changes")` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result +|`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| search count > 5 -| `detect_excessive_account_lockouts_from_endpoint_filter` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search count > 5 +| `detect_excessive_account_lockouts_from_endpoint_filter` ====Associated Analytic Story==== @@ -5156,7 +6270,7 @@ It's possible that a widely used system, such as a kiosk, could cause a large nu ---- -==={{visible anchor|Detect Excessive User Account Lockouts|detect-excessive-user-account-lockouts}}=== +===Detect Excessive User Account Lockouts=== This search detects user accounts that have been locked out a relatively high number of times in a short period. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5168,14 +6282,14 @@ This search detects user accounts that have been locked out a relatively high nu
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result -|`drop_dm_object_name("All_Changes")` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result +|`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| search count > 5 -| `detect_excessive_user_account_lockouts_filter` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search count > 5 +| `detect_excessive_user_account_lockouts_filter` ====Associated Analytic Story==== @@ -5221,85 +6335,11 @@ It is possible that a legitimate user is experiencing an issue causing multiple ---- -==={{visible anchor|Detect GCP Storage access from a new IP|detect-gcp-storage-access-from-a-new-ip}}=== -This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] -* '''Last Updated''': 2020-08-10 - -
-
- -====Search==== -`google_gcp_pubsub_message` -| multikv -| rename sc_status_ as status -| rename cs_object_ as bucket_name -| rename c_ip_ as remote_ip -| rename cs_uri_ as request_uri -| rename cs_method_ as operation -| search status="\"200\"" -| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri -| table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri -| inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv -| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri -| outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv -| eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) -| where newIP=1 -| eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") -| eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") -| table first_time last_time bucket_name remote_ip operation request_uri -| `detect_gcp_storage_access_from_a_new_ip_filter` - -====Associated Analytic Story==== - -* Suspicious GCP Storage Activities - - -====How To Implement==== -This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour. It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect HTML Help Renamed|detect-html-help-renamed}}=== +===Detect HTML Help Renamed=== The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.001/ T1218.001] * '''Last Updated''': 2021-02-11 @@ -5307,12 +6347,12 @@ The following analytic identifies a renamed instance of hh.exe (HTML Help) execu
====Search==== -`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_html_help_renamed_filter` +`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_renamed_filter` ====Associated Analytic Story==== @@ -5364,7 +6404,7 @@ Although unlikely a renamed instance of hh.exe will be used legitimately, filter ---- -==={{visible anchor|Detect HTML Help Spawn Child Process|detect-html-help-spawn-child-process}}=== +===Detect HTML Help Spawn Child Process=== The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5376,12 +6416,12 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_html_help_spawn_child_process_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_spawn_child_process_filter` ====Associated Analytic Story==== @@ -5437,7 +6477,7 @@ Although unlikely, some legitimate applications (ex. web browsers) may spawn a c ---- -==={{visible anchor|Detect HTML Help URL in Command Line|detect-html-help-url-in-command-line}}=== +===Detect HTML Help URL in Command Line=== The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5449,12 +6489,12 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_html_help_url_in_command_line_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_url_in_command_line_filter` ====Associated Analytic Story==== @@ -5512,7 +6552,7 @@ Although unlikely, some legitimate applications may retrieve a CHM remotely, fil ---- -==={{visible anchor|Detect HTML Help Using InfoTech Storage Handlers|detect-html-help-using-infotech-storage-handlers}}=== +===Detect HTML Help Using InfoTech Storage Handlers=== The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5524,12 +6564,12 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_html_help_using_infotech_storage_handlers_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_html_help_using_infotech_storage_handlers_filter` ====Associated Analytic Story==== @@ -5587,102 +6627,11 @@ It is rare to see instances of InfoTech Storage Handlers being used, but it does ---- -==={{visible anchor|Detect IPv6 Network Infrastructure Threats|detect-ipv6-network-infrastructure-threats}}=== -By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557.002/ T1557.002] -* '''Last Updated''': 2020-10-28 - -
-
- -====Search==== -`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") -| eval src_interface=src_int_prefix_long+src_int_suffix -| eval dest_interface=dest_int_prefix_long+dest_int_suffix -| stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface -| table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `detect_ipv6_network_infrastructure_threats_filter` - -====Associated Analytic Story==== - -* Router and Infrastructure Security - - -====How To Implement==== -This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1200 -| Hardware Additions -| Initial Access -|- -| T1498 -| Network Denial of Service -| Impact -|- -| T1557.002 -| ARP Cache Poisoning -| Collection, Credential Access -|} - -====Kill Chain Phase==== - -* Reconnaissance - -* Delivery - -* Actions on Objectives - - -====Known False Positives==== -None currently known - -====Reference==== - -* https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf - -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html - -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html - -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html - -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html - -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html - -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html - -* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Kerberoasting|detect-kerberoasting}}=== +===Detect Kerberoasting=== This search detects a potential kerberoasting attack via service principal name requests * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1558.003/ T1558.003] * '''Last Updated''': 2020-10-21 @@ -5690,15 +6639,15 @@ This search detects a potential kerberoasting attack via service principal name
====Search==== - -| from read_ssa_enriched_events() -| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id") -| where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" -| first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] -| where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID -| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" -| select start_time, end_time, entities, body -| into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id") +| where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" +| first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] +| where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID +| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" +| select start_time, end_time, entities, body +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5754,69 +6703,7 @@ Older systems that support kerberos RC4 by default NetApp may generate false pos ---- -==={{visible anchor|Detect Large Outbound ICMP Packets|detect-large-outbound-icmp-packets}}=== -This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1095/ T1095] -* '''Last Updated''': 2018-06-01 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip -| `drop_dm_object_name("All_Traffic")` -| search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `detect_large_outbound_icmp_packets_filter` - -====Associated Analytic Story==== - -* Command and Control - - -====How To Implement==== -In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1095 -| Non-Application Layer Protocol -| Command and Control -|} - -====Kill Chain Phase==== - -* Command and Control - - -====Known False Positives==== -ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list. - -====Reference==== - - -====Test Dataset==== - - -''version'': 2 -
-
- ----- - -==={{visible anchor|Detect MSHTA Url in Command Line|detect-mshta-url-in-command-line}}=== +===Detect MSHTA Url in Command Line=== This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5828,12 +6715,12 @@ This analytic identifies when Microsoft HTML Application Host (mshta.exe) utilit
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_mshta_url_in_command_line_filter` +| `security_content_ctime(lastTime)` +| `detect_mshta_url_in_command_line_filter` ====Associated Analytic Story==== @@ -5885,11 +6772,11 @@ It is possible legitimate applications may perform this behavior and will need t ---- -==={{visible anchor|Detect New Local Admin account|detect-new-local-admin-account}}=== +===Detect New Local Admin account=== This search looks for newly created accounts that have been elevated to local administrators. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.001/ T1136.001] * '''Last Updated''': 2020-07-08 @@ -5897,13 +6784,13 @@ This search looks for newly created accounts that have been elevated to local ad
====Search==== -`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) -| transaction member_id connected=false maxspan=180m -| rename member_id as user -| stats count min(_time) as firstTime max(_time) as lastTime by user dest +`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) +| transaction member_id connected=false maxspan=180m +| rename member_id as user +| stats count min(_time) as firstTime max(_time) as lastTime by user dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_new_local_admin_account_filter` +| `security_content_ctime(lastTime)` +| `detect_new_local_admin_account_filter` ====Associated Analytic Story==== @@ -5955,279 +6842,11 @@ The activity may be legitimate. For this reason, it's best to verify the account ---- -==={{visible anchor|Detect New Open GCP Storage Buckets|detect-new-open-gcp-storage-buckets}}=== -This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] -* '''Last Updated''': 2020-08-05 - -
-
- -====Search==== -`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions -| spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action -| spath output=user path=data.protoPayload.authenticationInfo.principalEmail -| spath output=location path=data.protoPayload.resourceLocation.currentLocations{} -| spath output=src path=data.protoPayload.requestMetadata.callerIp -| spath output=bucketName path=data.protoPayload.resourceName -| spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role -| spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member -| search (member=allUsers AND action=ADD) -| table _time, bucketName, src, user, location, action, role, member -| search `detect_new_open_gcp_storage_buckets_filter` - -====Associated Analytic Story==== - -* Suspicious GCP Storage Activities - - -====How To Implement==== -This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect New Open S3 Buckets over AWS CLI|detect-new-open-s3-buckets-over-aws-cli}}=== -This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] -* '''Last Updated''': 2021-01-12 - -
-
- -====Search==== -`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") -| rename requestParameters.bucketName AS bucketName -| fillnull -| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_new_open_s3_buckets_over_aws_cli_filter` - -====Associated Analytic Story==== - -* Suspicious AWS S3 Activities - - -====How To Implement==== - - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect New Open S3 buckets|detect-new-open-s3-buckets}}=== -This search looks for CloudTrail events where a user has created an open/public S3 bucket. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] -* '''Last Updated''': 2021-01-12 - -
-
- -====Search==== -`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl -| rex field=_raw "(?{.+})" -| spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} -| search grantees=* -| mvexpand grantees -| spath input=grantees output=uri path=Grantee.URI -| spath input=grantees output=permission path=Permission -| search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") -| search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") -| rename requestParameters.bucketName AS bucketName -| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission bucketName -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_new_open_s3_buckets_filter` - -====Associated Analytic Story==== - -* Suspicious AWS S3 Activities - - -====How To Implement==== -You must install the AWS App for Splunk. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json - - -''version'': 2 -
-
- ----- - -==={{visible anchor|Detect Outbound SMB Traffic|detect-outbound-smb-traffic}}=== -This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.002/ T1071.002] -* '''Last Updated''': 2020-07-21 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip -| `drop_dm_object_name("All_Traffic")` -| `security_content_ctime(start_time)` -| `security_content_ctime(end_time)` -| `detect_outbound_smb_traffic_filter` - -====Associated Analytic Story==== - -* Hidden Cobra Malware - -* DHS Report TA18-074A - -* Sunburst Malware - - -====How To Implement==== -In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1071.002 -| File Transfer Protocols -| Command and Control -|} - -====Kill Chain Phase==== - -* Actions on Objectives - -* Command and Control - - -====Known False Positives==== -It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. - -====Reference==== - - -====Test Dataset==== - - -''version'': 3 -
-
- ----- - -==={{visible anchor|Detect Pass the Hash|detect-pass-the-hash}}=== +===Detect Pass the Hash=== This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1550.002/ T1550.002] * '''Last Updated''': 2020-10-21 @@ -6235,15 +6854,15 @@ This search looks for specific authentication events from the Windows Security E
====Search==== - -| from read_ssa_enriched_events() -| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), LogonType=map_get(input_event, "logon_type"), LogonProcess=map_get(input_event, "logon_process"), ComputerName=map_get(input_event, "dest_ip_primary_artifact"), AccountName=map_get(input_event, "dest_user_primary_artifact") -| where (LogonType="3" AND LogonProcess="NtLmSsp" AND AccountName IS NOT NULL) OR (LogonType="9" AND LogonProcess="seclogo") -| first_time_event input_columns=["EventCode","LogonProcess","ComputerName"] -| where first_time_EventCode_LogonProcess_ComputerName -| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" -| select start_time, end_time, entities, body -| into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), LogonType=map_get(input_event, "logon_type"), LogonProcess=map_get(input_event, "logon_process"), ComputerName=map_get(input_event, "dest_ip_primary_artifact"), AccountName=map_get(input_event, "dest_user_primary_artifact") +| where (LogonType="3" AND LogonProcess="NtLmSsp" AND AccountName IS NOT NULL) OR (LogonType="9" AND LogonProcess="seclogo") +| first_time_event input_columns=["EventCode","LogonProcess","ComputerName"] +| where first_time_EventCode_LogonProcess_ComputerName +| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" +| select start_time, end_time, entities, body +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -6299,8 +6918,8 @@ Legitimate logon activity by authorized NTLM systems may be detected by this sea ---- -==={{visible anchor|Detect Path Interception By Creation Of program exe|detect-path-interception-by-creation-of-program-exe}}=== -The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. +===Detect Path Interception By Creation Of program exe=== +The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint @@ -6311,19 +6930,19 @@ The detection Detect Path Interception By Creation Of program exe is detecting t
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest index -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest index +| `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?[^\\\\]*\.(?:exe |bat |com -|ps1))" -| eval process_name = lower(process_name) -| eval service_process = lower(service_process) -| where process_name != service_process -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_path_interception_by_creation_of_program_exe_filter` +|ps1))" +| eval process_name = lower(process_name) +| eval service_process = lower(service_process) +| where process_name != service_process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_path_interception_by_creation_of_program_exe_filter` ====Associated Analytic Story==== @@ -6371,82 +6990,7 @@ unknown ---- -==={{visible anchor|Detect Port Security Violation|detect-port-security-violation}}=== -By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557.002/ T1557.002] -* '''Last Updated''': 2020-10-28 - -
-
- -====Search==== -`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") -| eval src_interface=src_int_prefix_long+src_int_suffix -| stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_port_security_violation_filter` - -====Associated Analytic Story==== - -* Router and Infrastructure Security - - -====How To Implement==== -This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1200 -| Hardware Additions -| Initial Access -|- -| T1498 -| Network Denial of Service -| Impact -|- -| T1557.002 -| ARP Cache Poisoning -| Collection, Credential Access -|} - -====Kill Chain Phase==== - -* Reconnaissance - -* Delivery - -* Exploitation - -* Actions on Objectives - - -====Known False Positives==== -This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Prohibited Applications Spawning cmd exe|detect-prohibited-applications-spawning-cmd-exe}}=== +===Detect Prohibited Applications Spawning cmd exe=== This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6458,13 +7002,13 @@ This search looks for executions of cmd.exe spawned by a process that is often a
====Search==== - + | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -|search [`prohibited_apps_launching_cmd`] -| `detect_prohibited_applications_spawning_cmd_exe_filter` +| `security_content_ctime(lastTime)` +|search [`prohibited_apps_launching_cmd`] +| `detect_prohibited_applications_spawning_cmd_exe_filter` ====Associated Analytic Story==== @@ -6516,11 +7060,11 @@ There are circumstances where an application may legitimately execute and intera ---- -==={{visible anchor|Detect Prohibited Applications Spawning cmd exe|detect-prohibited-applications-spawning-cmd-exe}}=== +===Detect Prohibited Applications Spawning cmd exe=== This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. This is a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe` by @bpatel. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1059/ T1059] * '''Last Updated''': 2020-7-13 @@ -6528,18 +7072,18 @@ This search looks for executions of cmd.exe spawned by a process that is often a
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) -| where process_name="cmd.exe" -| rex field=parent_process "(?[^\\\\]+)$" +| where process_name="cmd.exe" +| rex field=parent_process "(?[^\\\\]+)$" | where field0="winword.exe" OR field0="excel.exe" OR field0="outlook.exe" OR field0="powerpnt.exe" OR field0="visio.exe" OR field0="mspub.exe" OR field0="acrobat.exe" OR field0="acrord32.exe" OR field0="chrome.exe" OR field0="iexplore.exe" OR field0="opera.exe" OR field0="firefox.exe" OR field0="java.exe" OR field0="powershell.exe" -| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body="TBD" -| into write_ssa_detected_events(); +| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body="TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -6591,7 +7135,7 @@ There are circumstances where an application may legitimately execute and intera ---- -==={{visible anchor|Detect PsExec With accepteula Flag|detect-psexec-with-accepteula-flag}}=== +===Detect PsExec With accepteula Flag=== This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6603,12 +7147,12 @@ This search looks for events where `PsExec.exe` is run with the `accepteula` fla
====Search==== - -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest Processes.parent_process_name + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_psexec_with_accepteula_flag_filter` +| `security_content_ctime(lastTime)` +| `detect_psexec_with_accepteula_flag_filter` ====Associated Analytic Story==== @@ -6656,35 +7200,35 @@ Administrators can leverage PsExec for accessing remote systems and might pass ` ---- -==={{visible anchor|Detect Rare Executables|detect-rare-executables}}=== +===Detect Rare Executables=== This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2020-03-16
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name -| rename Processes.process_name as process -| rex field=user "(?.*)\\\\(?.*)" + +| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name +| rename Processes.process_name as process +| rex field=user "(?.*)\\\\(?.*)" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [ -| tstats count from datamodel=Endpoint.Processes by Processes.process_name -| rare Processes.process_name limit=30 +| tstats count from datamodel=Endpoint.Processes by Processes.process_name +| rare Processes.process_name limit=30 | rename Processes.process_name as process | `filter_rare_process_allow_list` -| table process ] -| `detect_rare_executables_filter` +| table process ] +| `detect_rare_executables_filter` ====Associated Analytic Story==== -* Emotet Malware DHS Report TA18-201A +* Emotet Malware DHS Report TA18-201A * Unusual Processes @@ -6728,7 +7272,7 @@ Some legitimate processes may be only rarely executed in your environment. As th ---- -==={{visible anchor|Detect Regasm Spawning a Process|detect-regasm-spawning-a-process}}=== +===Detect Regasm Spawning a Process=== The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6740,12 +7284,12 @@ The following analytic identifies regasm.exe spawning a process. This particular
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_regasm_spawning_a_process_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regasm_spawning_a_process_filter` ====Associated Analytic Story==== @@ -6799,11 +7343,11 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa ---- -==={{visible anchor|Detect Regasm with Network Connection|detect-regasm-with-network-connection}}=== +===Detect Regasm with Network Connection=== The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] * '''Last Updated''': 2021-02-16 @@ -6811,12 +7355,12 @@ The following analytic identifies regasm.exe with a network connection to a publ
====Search==== -`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe -| rename Computer as dest -| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_regasm_with_network_connection_filter` +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regasm_with_network_connection_filter` ====Associated Analytic Story==== @@ -6868,11 +7412,11 @@ Although unlikely, limited instances of regasm.exe with a network connection may ---- -==={{visible anchor|Detect Regasm with no Command Line Arguments|detect-regasm-with-no-command-line-arguments}}=== +===Detect Regasm with no Command Line Arguments=== The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] * '''Last Updated''': 2021-02-12 @@ -6880,13 +7424,13 @@ The following analytic identifies regasm.exe with no command line arguments. Thi
====Search==== -`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) -| regex CommandLine="(regasm\.exe.{0,4}$)" -| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_regasm_with_no_command_line_arguments_filter` +`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) +| regex CommandLine="(regasm\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regasm_with_no_command_line_arguments_filter` ====Associated Analytic Story==== @@ -6938,7 +7482,7 @@ Although unlikely, limited instances of regasm.exe or may cause a false positive ---- -==={{visible anchor|Detect Regsvcs Spawning a Process|detect-regsvcs-spawning-a-process}}=== +===Detect Regsvcs Spawning a Process=== The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6950,12 +7494,12 @@ The following analytic identifies regsvcs.exe spawning a process. This particula
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_regsvcs_spawning_a_process_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regsvcs_spawning_a_process_filter` ====Associated Analytic Story==== @@ -7007,11 +7551,11 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa ---- -==={{visible anchor|Detect Regsvcs with Network Connection|detect-regsvcs-with-network-connection}}=== +===Detect Regsvcs with Network Connection=== The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] * '''Last Updated''': 2021-02-16 @@ -7019,12 +7563,12 @@ The following analytic identifies Regsvcs.exe with a network connection to a pub
====Search==== -`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe -| rename Computer as dest -| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_regsvcs_with_network_connection_filter` +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regsvcs_with_network_connection_filter` ====Associated Analytic Story==== @@ -7076,11 +7620,11 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive. ---- -==={{visible anchor|Detect Regsvcs with No Command Line Arguments|detect-regsvcs-with-no-command-line-arguments}}=== +===Detect Regsvcs with No Command Line Arguments=== The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] * '''Last Updated''': 2021-02-12 @@ -7088,13 +7632,13 @@ The following analytic identifies regsvcs.exe with no command line arguments. Th
====Search==== -`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) -| regex CommandLine="(regsvcs\.exe.{0,4}$)" -| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_regsvcs_with_no_command_line_arguments_filter` +`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) +| regex CommandLine="(regsvcs\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_regsvcs_with_no_command_line_arguments_filter` ====Associated Analytic Story==== @@ -7146,9 +7690,9 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive. ---- -==={{visible anchor|Detect Regsvr32 Application Control Bypass|detect-regsvr32-application-control-bypass}}=== +===Detect Regsvr32 Application Control Bypass=== Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ -Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. +Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint @@ -7159,12 +7703,12 @@ Upon investigating, look for network connections to remote destinations (interna
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_regsvr32_application_control_bypass_filter` +| `security_content_ctime(lastTime)` +| `detect_regsvr32_application_control_bypass_filter` ====Associated Analytic Story==== @@ -7218,79 +7762,7 @@ Limited false positives related to third party software registering .DLL's. ---- -==={{visible anchor|Detect Rogue DHCP Server|detect-rogue-dhcp-server}}=== -By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557/ T1557] -* '''Last Updated''': 2020-08-11 - -
-
- -====Search==== -`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" -| stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `detect_rogue_dhcp_server_filter` - -====Associated Analytic Story==== - -* Router and Infrastructure Security - - -====How To Implement==== -This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1200 -| Hardware Additions -| Initial Access -|- -| T1498 -| Network Denial of Service -| Impact -|- -| T1557 -| Man-in-the-Middle -| Collection, Credential Access -|} - -====Kill Chain Phase==== - -* Reconnaissance - -* Delivery - -* Actions on Objectives - - -====Known False Positives==== -This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Rundll32 Application Control Bypass - advpack|detect-rundll32-application-control-bypass---advpack}}=== +===Detect Rundll32 Application Control Bypass - advpack=== The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7302,12 +7774,12 @@ The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_rundll32_application_control_bypass___advpack_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_rundll32_application_control_bypass___advpack_filter` ====Associated Analytic Story==== @@ -7363,7 +7835,7 @@ Although unlikely, some legitimate applications may use advpack.dll or ieadvpack ---- -==={{visible anchor|Detect Rundll32 Application Control Bypass - setupapi|detect-rundll32-application-control-bypass---setupapi}}=== +===Detect Rundll32 Application Control Bypass - setupapi=== The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7375,12 +7847,12 @@ The following analytic identifies rundll32.exe loading setupapi.dll and iesetupa
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_rundll32_application_control_bypass___setupapi_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_rundll32_application_control_bypass___setupapi_filter` ====Associated Analytic Story==== @@ -7436,7 +7908,7 @@ Although unlikely, some legitimate applications may use setupapi triggering a fa ---- -==={{visible anchor|Detect Rundll32 Application Control Bypass - syssetup|detect-rundll32-application-control-bypass---syssetup}}=== +===Detect Rundll32 Application Control Bypass - syssetup=== The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7448,12 +7920,12 @@ The following analytic identifies rundll32.exe loading syssetup.dll by calling t
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_rundll32_application_control_bypass___syssetup_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_rundll32_application_control_bypass___syssetup_filter` ====Associated Analytic Story==== @@ -7509,7 +7981,7 @@ Although unlikely, some legitimate applications may use syssetup.dll, triggering ---- -==={{visible anchor|Detect Rundll32 Inline HTA Execution|detect-rundll32-inline-hta-execution}}=== +===Detect Rundll32 Inline HTA Execution=== The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7521,12 +7993,12 @@ The following analytic identifies "rundll32.exe" execution with inline protocol
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_rundll32_inline_hta_execution_filter` +| `security_content_ctime(lastTime)` +| `detect_rundll32_inline_hta_execution_filter` ====Associated Analytic Story==== @@ -7578,618 +8050,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg ---- -==={{visible anchor|Detect S3 access from a new IP|detect-s3-access-from-a-new-ip}}=== -This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] -* '''Last Updated''': 2018-06-28 - -
-
- -====Search==== -`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 -| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip -| inputlookup append=t previously_seen_S3_access_from_remote_ip.csv -| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip -| outputlookup previously_seen_S3_access_from_remote_ip.csv -| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) -| where newIP=1 -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| table bucket_name remote_ip] -| iplocation remote_ip -|rename remote_ip as src_ip -| table _time bucket_name src_ip City Country operation request_uri -| `detect_s3_access_from_a_new_ip_filter` - -====Associated Analytic Story==== - -* Suspicious AWS S3 Activities - - -====How To Implement==== -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect SNICat SNI Exfiltration|detect-snicat-sni-exfiltration}}=== -This search looks for commands that the SNICat tool uses in the TLS SNI field. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1041/ T1041] -* '''Last Updated''': 2020-10-21 - -
-
- -====Search==== -`zeek_ssl` -| rex field=server_name "(?(LIST -|LS -|SIZE -|LD -|CB -|CD -|EX -|ALIVE -|EXIT -|WHERE -|finito)-[A-Za-z0-9]{16}\.)" -| stats count by src_ip dest_ip server_name snicat -| where count>0 -| table src_ip dest_ip server_name snicat -| `detect_snicat_sni_exfiltration_filter` - -====Associated Analytic Story==== - -* Data Exfiltration - - -====How To Implement==== -You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1041 -| Exfiltration Over C2 Channel -| Exfiltration -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -Unknown - -====Reference==== - -* https://www.mnemonic.no/blog/introducing-snicat/ - -* https://github.com/mnemonic-no/SNIcat - -* https://attack.mitre.org/techniques/T1041/ - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Software Download To Network Device|detect-software-download-to-network-device}}=== -Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1542.005/ T1542.005] -* '''Last Updated''': 2020-10-28 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port -| `drop_dm_object_name("All_Traffic")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_software_download_to_network_device_filter` - -====Associated Analytic Story==== - -* Router and Infrastructure Security - - -====How To Implement==== -This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1542.005 -| TFTP Boot -| Defense Evasion, Persistence -|} - -====Kill Chain Phase==== - -* Delivery - - -====Known False Positives==== -This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Spike in AWS Security Hub Alerts for EC2 Instance|detect-spike-in-aws-security-hub-alerts-for-ec2-instance}}=== -This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': -* '''Last Updated''': 2021-01-26 - -
-
- -====Search==== -`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance -| bucket span=4h _time -| stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest -| eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev -| eval threshold_value = 3 -| eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) -| search isOutlier=1 -| table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg -| `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` - -====Associated Analytic Story==== - -* AWS Security Hub Alerts - - -====How To Implement==== -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -None - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json - - -''version'': 3 -
-
- ----- - -==={{visible anchor|Detect Spike in AWS Security Hub Alerts for User|detect-spike-in-aws-security-hub-alerts-for-user}}=== -This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': -* '''Last Updated''': 2021-01-26 - -
-
- -====Search==== -`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser -| rename findings{}.Resources{}.Id as user -| bucket span=4h _time -| stats count AS alerts by _time user -| eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev -| eval threshold_value = 2 -| eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) -| search isOutlier=1 -| table _time user alerts -|`detect_spike_in_aws_security_hub_alerts_for_user_filter` - -====Associated Analytic Story==== - -* AWS Security Hub Alerts - - -====How To Implement==== -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -None - -====Reference==== - - -====Test Dataset==== - - -''version'': 3 -
-
- ----- - -==={{visible anchor|Detect Spike in S3 Bucket deletion|detect-spike-in-s3-bucket-deletion}}=== -This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] -* '''Last Updated''': 2018-11-27 - -
-
- -====Search==== -`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket -| spath output=arn path=userIdentity.arn -| stats count as apiCalls by arn -| inputlookup s3_deletion_baseline append=t -| fields - latestCount -| stats values(*) as * by arn -| rename apiCalls as latestCount -| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 -| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) -| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) -| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls -| outputlookup s3_deletion_baseline -| eval dataPointThreshold = 15, deviationThreshold = 3 -| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) -| where isSpike=1 -| rename arn as userIdentity.arn -| table userIdentity.arn] -| spath output=user userIdentity.arn -| spath output=bucketName path=requestParameters.bucketName -| stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user -| `detect_spike_in_s3_bucket_deletion_filter` - -====Associated Analytic Story==== - -* Suspicious AWS S3 Activities - - -====How To Implement==== -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Spike in blocked Outbound Traffic from your AWS|detect-spike-in-blocked-outbound-traffic-from-your-aws}}=== -This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': -* '''Last Updated''': 2018-05-07 - -
-
- -====Search==== -`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) -| stats count as numberOfBlockedConnections by src_ip -| inputlookup baseline_blocked_outbound_connections append=t -| fields - latestCount -| stats values(*) as * by src_ip -| rename numberOfBlockedConnections as latestCount -| eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 -| eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) -| eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) -| table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections -| outputlookup baseline_blocked_outbound_connections -| eval dataPointThreshold = 5, deviationThreshold = 3 -| eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) -| where isSpike=1 -| table src_ip] -| stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip -| `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` - -====Associated Analytic Story==== - -* AWS Network ACL Activity - -* Suspicious AWS Traffic - -* Command and Control - - -====How To Implement==== -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} - -====Kill Chain Phase==== - -* Actions on Objectives - -* Command and Control - - -====Known False Positives==== -The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Traffic Mirroring|detect-traffic-mirroring}}=== -Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1020.001/ T1020.001] -* '''Last Updated''': 2020-10-28 - -
-
- -====Search==== -`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") -| stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `detect_traffic_mirroring_filter` - -====Associated Analytic Story==== - -* Router and Infrastructure Security - - -====How To Implement==== -This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1200 -| Hardware Additions -| Initial Access -|- -| T1498 -| Network Denial of Service -| Impact -|- -| T1020.001 -| Traffic Duplication -| Exfiltration -|} - -====Kill Chain Phase==== - -* Delivery - -* Actions on Objectives - - -====Known False Positives==== -This search will return false positives for any legitimate traffic captures by network administrators. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Unauthorized Assets by MAC address|detect-unauthorized-assets-by-mac-address}}=== -By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Sessions -* '''ATT&CK''': -* '''Last Updated''': 2017-09-13 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac -| dedup All_Sessions.dest_mac -| `drop_dm_object_name("Network_Sessions")` -|`drop_dm_object_name("All_Sessions")` -| search NOT [ -| inputlookup asset_lookup_by_str -|rename mac as dest_mac -| fields + dest_mac] -| `detect_unauthorized_assets_by_mac_address_filter` - -====Associated Analytic Story==== - -* Asset Tracking - - -====How To Implement==== -This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} - -====Kill Chain Phase==== - -* Reconnaissance - -* Delivery - -* Actions on Objectives - - -====Known False Positives==== -This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Use of cmd exe to Launch Script Interpreters|detect-use-of-cmd-exe-to-launch-script-interpreters}}=== +===Detect Use of cmd exe to Launch Script Interpreters=== This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8201,16 +8062,16 @@ This search looks for the execution of the cscript.exe or wscript.exe processes,
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest -| `drop_dm_object_name("Processes")` + +| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` +|`security_content_ctime(lastTime)` +| `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` ====Associated Analytic Story==== -* Emotet Malware DHS Report TA18-201A +* Emotet Malware DHS Report TA18-201A * Suspicious Command-Line Executions @@ -8254,288 +8115,7 @@ Some legitimate applications may exhibit this behavior. ---- -==={{visible anchor|Detect Windows DNS SIGRed via Splunk Stream|detect-windows-dns-sigred-via-splunk-stream}}=== -This search detects SIGRed via Splunk Stream. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203] -* '''Last Updated''': 2020-07-28 - -
-
- -====Search==== -`stream_dns` -| spath "query_type{}" -| search "query_type{}" IN (SIG,KEY) -| spath protocol_stack -| search protocol_stack="ip:tcp:dns" -| append [search `stream_tcp` bytes_out>65000] -| `detect_windows_dns_sigred_via_splunk_stream_filter` -| stats count by flow_id -| where count>1 -| fields - count - -====Associated Analytic Story==== - -* Windows DNS SIGRed CVE-2020-1350 - - -====How To Implement==== -You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1203 -| Exploitation for Client Execution -| Execution -|} - -====Kill Chain Phase==== - -* Exploitation - - -====Known False Positives==== -unknown - -====Reference==== - -* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Windows DNS SIGRed via Zeek|detect-windows-dns-sigred-via-zeek}}=== -This search detects SIGRed via Zeek DNS and Zeek Conn data. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Resolution -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203] -* '''Last Updated''': 2020-07-28 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id -| rename DNS.flow_id as flow_id -| append [ -| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id -| rename All_Traffic.flow_id as flow_id] -| `detect_windows_dns_sigred_via_zeek_filter` -| stats count by flow_id -| where count>1 -| fields - count - -====Associated Analytic Story==== - -* Windows DNS SIGRed CVE-2020-1350 - - -====How To Implement==== -You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The Network Resolution and Network Traffic datamodels are in use for this search. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1203 -| Exploitation for Client Execution -| Execution -|} - -====Kill Chain Phase==== - -* Exploitation - - -====Known False Positives==== -unknown - -====Reference==== - -* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect Zerologon via Zeek|detect-zerologon-via-zeek}}=== -This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1190/ T1190] -* '''Last Updated''': 2020-09-15 - -
-
- -====Search==== -`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) -| bin span=5m _time -| stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip -| search opscount=3 authcount>4 passcount>0 -| search `detect_zerologon_via_zeek_filter` - -====Associated Analytic Story==== - -* Detect Zerologon Attack - - -====How To Implement==== -You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json. These three operations are then correlated on the Zeek UID field. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1190 -| Exploit Public-Facing Application -| Initial Access -|} - -====Kill Chain Phase==== - -* Exploitation - - -====Known False Positives==== -unknown - -====Reference==== - -* https://www.secura.com/blog/zero-logon - -* https://github.com/SecuraBV/CVE-2020-1472 - -* https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472 - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Detect hosts connecting to dynamic domain providers|detect-hosts-connecting-to-dynamic-domain-providers}}=== -Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Resolution -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1189/ T1189] -* '''Last Updated''': 2021-01-14 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host -| `drop_dm_object_name("DNS")` -| `security_content_ctime(firstTime)` -| `dynamic_dns_providers` -| `detect_hosts_connecting_to_dynamic_domain_providers_filter` - -====Associated Analytic Story==== - -* Data Protection - -* Prohibited Traffic Allowed or Protocol Mismatch - -* DNS Hijacking - -* Suspicious DNS Traffic - -* Dynamic DNS - -* Command and Control - - -====How To Implement==== -First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ -This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ -1. \ -1. **Label:** DNS Answer, **Field:** answer\ -1. \ -1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ -Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1189 -| Drive-by Compromise -| Initial Access -|} - -====Kill Chain Phase==== - -* Command and Control - -* Actions on Objectives - - -====Known False Positives==== -Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log - - -''version'': 3 -
-
- ----- - -==={{visible anchor|Detect mshta inline hta execution|detect-mshta-inline-hta-execution}}=== +===Detect mshta inline hta execution=== The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8547,12 +8127,12 @@ The following analytic identifies "mshta.exe" execution with inline protocol han
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `detect_mshta_inline_hta_execution_filter` +| `security_content_ctime(lastTime)` +| `detect_mshta_inline_hta_execution_filter` ====Associated Analytic Story==== @@ -8604,11 +8184,11 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg ---- -==={{visible anchor|Detect mshta renamed|detect-mshta-renamed}}=== +===Detect mshta renamed=== The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.005/ T1218.005] * '''Last Updated''': 2021-01-20 @@ -8616,12 +8196,12 @@ The following analytic identifies renamed instances of mshta.exe executing. Msht
====Search==== -`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest +`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| `detect_mshta_renamed_filter` +| `detect_mshta_renamed_filter` ====Associated Analytic Story==== @@ -8671,7 +8251,7 @@ Although unlikely, some legitimate applications may use a moved copy of mshta.ex ---- -==={{visible anchor|Detect processes used for System Network Configuration Discovery|detect-processes-used-for-system-network-configuration-discovery}}=== +===Detect processes used for System Network Configuration Discovery=== This search looks for fast execution of processes used for system network configuration discovery on the endpoint. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8683,16 +8263,16 @@ This search looks for fast execution of processes used for system network config
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name(Processes)` -| search `system_network_configuration_discovery_tools` -| transaction dest connected=false maxpause=5m -|where eventcount>=5 -| table firstTime lastTime dest user process_name process parent_process eventcount -| `detect_processes_used_for_system_network_configuration_discovery_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| search `system_network_configuration_discovery_tools` +| transaction dest connected=false maxpause=5m +|where eventcount>=5 +| table firstTime lastTime dest user process_name process parent_process eventcount +| `detect_processes_used_for_system_network_configuration_discovery_filter` ====Associated Analytic Story==== @@ -8742,11 +8322,11 @@ It is uncommon for normal users to execute a series of commands used for network ---- -==={{visible anchor|Disabling Remote User Account Control|disabling-remote-user-account-control}}=== +===Disabling Remote User Account Control=== The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1548.002/ T1548.002] * '''Last Updated''': 2020-11-18 @@ -8754,10 +8334,10 @@ The search looks for modifications to registry keys that control the enforcement
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action -| `drop_dm_object_name(Registry)` -| `disabling_remote_user_account_control_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action +| `drop_dm_object_name(Registry)` +| `disabling_remote_user_account_control_filter` ====Associated Analytic Story==== @@ -8805,7 +8385,7 @@ This registry key may be modified via administrators to implement a change in sy ---- -==={{visible anchor|Dump LSASS via comsvcs DLL|dump-lsass-via-comsvcs-dll}}=== +===Dump LSASS via comsvcs DLL=== Detect the usage of comsvcs.dll for dumping the lsass process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8817,12 +8397,12 @@ Detect the usage of comsvcs.dll for dumping the lsass process.
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `dump_lsass_via_comsvcs_dll_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `dump_lsass_via_comsvcs_dll_filter` ====Associated Analytic Story==== @@ -8874,7 +8454,7 @@ None identified. ---- -==={{visible anchor|Dump LSASS via procdump|dump-lsass-via-procdump}}=== +===Dump LSASS via procdump=== Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\ During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. @@ -8887,12 +8467,12 @@ During triage, confirm this is procdump.exe executing. If it is the first time a
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `dump_lsass_via_procdump_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `dump_lsass_via_procdump_filter` ====Associated Analytic Story==== @@ -8944,12 +8524,12 @@ None identified. ---- -==={{visible anchor|Dump LSASS via procdump Rename|dump-lsass-via-procdump-rename}}=== +===Dump LSASS via procdump Rename=== Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\ During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] * '''Last Updated''': 2021-02-01 @@ -8957,12 +8537,12 @@ During triage, confirm this is procdump.exe executing. If it is the first time a
====Search==== -`sysmon` OriginalFileName=procdump process_name!=procdump*.exe EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* -| rename Computer as dest -| stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `dump_lsass_via_procdump_rename_filter` +`sysmon` OriginalFileName=procdump process_name!=procdump*.exe EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `dump_lsass_via_procdump_rename_filter` ====Associated Analytic Story==== @@ -9014,68 +8594,7 @@ None identified. ---- -==={{visible anchor|Email files written outside of the Outlook directory|email-files-written-outside-of-the-outlook-directory}}=== -The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.001/ T1114.001] -* '''Last Updated''': 2020-07-21 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest -| `drop_dm_object_name("Filesystem")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `email_files_written_outside_of_the_outlook_directory_filter` - -====Associated Analytic Story==== - -* Collection and Staging - - -====How To Implement==== -To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114.001 -| Local Email Collection -| Collection -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. - -====Reference==== - - -====Test Dataset==== - - -''version'': 3 -
-
- ----- - -==={{visible anchor|Execution of File with Multiple Extensions|execution-of-file-with-multiple-extensions}}=== +===Execution of File with Multiple Extensions=== This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9087,12 +8606,12 @@ This search looks for processes launched from files that have double extensions
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name(Processes)` -| `execution_of_file_with_multiple_extensions_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `execution_of_file_with_multiple_extensions_filter` ====Associated Analytic Story==== @@ -9138,26 +8657,26 @@ None identified. ---- -==={{visible anchor|File with Samsam Extension|file-with-samsam-extension}}=== +===File with Samsam Extension=== The search looks for file writes with extensions consistent with a SamSam ransomware attack. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2018-12-14
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` -| rex field=file_name "(?\.[^\.]+)$" -| search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml -| `file_with_samsam_extension_filter` +| rex field=file_name "(?\.[^\.]+)$" +| search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml +| `file_with_samsam_extension_filter` ====Associated Analytic Story==== @@ -9199,7 +8718,7 @@ Because these extensions are not typically used in normal operations, you should ---- -==={{visible anchor|First Time Seen Child Process of Zoom|first-time-seen-child-process-of-zoom}}=== +===First Time Seen Child Process of Zoom=== This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9211,14 +8730,14 @@ This search looks for child processes spawned by zoom.exe or zoom.us that has no
====Search==== - -| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest -| `drop_dm_object_name(Processes)` -| lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen -| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") -| `security_content_ctime(firstTime)` -| table firstTime dest, process_id, process_name, parent_process_id, parent_process_name -|`first_time_seen_child_process_of_zoom_filter` + +| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest +| `drop_dm_object_name(Processes)` +| lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen +| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") +| `security_content_ctime(firstTime)` +| table firstTime dest, process_id, process_name, parent_process_id, parent_process_name +|`first_time_seen_child_process_of_zoom_filter` ====Associated Analytic Story==== @@ -9264,11 +8783,11 @@ A new child process of zoom isn't malicious by that fact alone. Further investig ---- -==={{visible anchor|First time seen command line argument|first-time-seen-command-line-argument}}=== +===First time seen command line argument=== This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1059/ T1059], [https://attack.mitre.org/techniques/T1117/ T1117], [https://attack.mitre.org/techniques/T1202/ T1202] * '''Last Updated''': 2021-2-1 @@ -9276,18 +8795,18 @@ This search looks for command-line arguments that use a `/c` parameter to execut
====Search==== - -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), cmd_line_norm=lower(cmd_line), cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/, "GUID"), cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s |$)+)/, "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s -|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N") -| where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true -| select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id -| first_time_event input_columns=["cmd_line_norm"] -| where first_time_cmd_line_norm -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" -| into write_ssa_detected_events(); +|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N") +| where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true +| select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id +| first_time_event input_columns=["cmd_line_norm"] +| where first_time_cmd_line_norm +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9318,9 +8837,9 @@ You must be populating the endpoint data model for SSA and specifically the proc | Command and Scripting Interpreter | Execution |- -| -| -| +| +| +| |- | T1202 | Indirect Command Execution @@ -9349,7 +8868,7 @@ Legitimate programs can also use command-line arguments to execute. Please verif ---- -==={{visible anchor|Hiding Files And Directories With Attrib exe|hiding-files-and-directories-with-attrib-exe}}=== +===Hiding Files And Directories With Attrib exe=== Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9361,12 +8880,12 @@ Attackers leverage an existing Windows binary, attrib.exe, to mark specific as h
====Search==== - -| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest -| `drop_dm_object_name("Processes")` + +| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` -| `hiding_files_and_directories_with_attrib_exe_filter` +| `hiding_files_and_directories_with_attrib_exe_filter` ====Associated Analytic Story==== @@ -9398,7 +8917,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Known False Positives==== -Some applications and users may legitimately use attrib.exe to interact with the files. +Some applications and users may legitimately use attrib.exe to interact with the files. ====Reference==== @@ -9414,70 +8933,11 @@ Some applications and users may legitimately use attrib.exe to interact with the ---- -==={{visible anchor|High Number of Login Failures from a single source|high-number-of-login-failures-from-a-single-source}}=== -This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1110.001/ T1110.001] -* '''Last Updated''': 2020-12-16 - -
-
- -====Search==== -`o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory -| stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app -| search accounts_locked >= 5 -| `high_number_of_login_failures_from_a_single_source_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - - -====How To Implement==== - - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1110.001 -| Password Guessing -| Credential Access -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -unknown - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Illegal Access To User Content via PowerSploit modules|illegal-access-to-user-content-via-powersploit-modules}}=== +===Illegal Access To User Content via PowerSploit modules=== This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1021/ T1021], [https://attack.mitre.org/techniques/T1113/ T1113], [https://attack.mitre.org/techniques/T1123/ T1123], [https://attack.mitre.org/techniques/T1563/ T1563] * '''Last Updated''': 2020-11-09 @@ -9485,14 +8945,14 @@ This detection identifies access to PowerSploit modules that enable illegaly acc
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-HttpStatus/)=true OR match_regex(cmd_line, /(?i)Get-Keystrokes/)=true OR match_regex(cmd_line, /(?i)Get-MicrophoneAudio/)=true OR match_regex(cmd_line, /(?i)Get-NetRDPSession/)=true OR match_regex(cmd_line, /(?i)Get-TimedScreenshot/)=true OR match_regex(cmd_line, /(?i)Get-WebConfig/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9556,11 +9016,11 @@ None identified. ---- -==={{visible anchor|Illegal Account Creation via PowerSploit modules|illegal-account-creation-via-powersploit-modules}}=== +===Illegal Account Creation via PowerSploit modules=== This detection identifies access to PowerSploit modules that create accounts illegaly. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1585/ T1585] * '''Last Updated''': 2020-11-09 @@ -9568,14 +9028,14 @@ This detection identifies access to PowerSploit modules that create accounts ill
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)New-DomainUser/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9627,11 +9087,11 @@ None identified. ---- -==={{visible anchor|Illegal Deletion of Logs via Mimikatz modules|illegal-deletion-of-logs-via-mimikatz-modules}}=== +===Illegal Deletion of Logs via Mimikatz modules=== This detection identifies access to PowerSploit modules that delete event logs. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1070/ T1070] * '''Last Updated''': 2020-11-09 @@ -9639,14 +9099,14 @@ This detection identifies access to PowerSploit modules that delete event logs.
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)event::drop/)=true OR match_regex(cmd_line, /(?i)event::clear/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9698,11 +9158,11 @@ None identified. ---- -==={{visible anchor|Illegal Enabling or Disabling of Accounts via DSInternals modules|illegal-enabling-or-disabling-of-accounts-via-dsinternals-modules}}=== +===Illegal Enabling or Disabling of Accounts via DSInternals modules=== This detection identifies use of DSInternals modules that enable or disable accounts illegaly. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-09 @@ -9710,14 +9170,14 @@ This detection identifies use of DSInternals modules that enable or disable acco
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Disable-ADDBAccount/)=true OR match_regex(cmd_line, /(?i)Enable-ADDBAccount/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9773,11 +9233,11 @@ None identified. ---- -==={{visible anchor|Illegal Management of Active Directory Elements and Policies via DSInternals modules|illegal-management-of-active-directory-elements-and-policies-via-dsinternals-modules}}=== +===Illegal Management of Active Directory Elements and Policies via DSInternals modules=== This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1207/ T1207], [https://attack.mitre.org/techniques/T1484/ T1484] * '''Last Updated''': 2020-11-09 @@ -9785,14 +9245,14 @@ This detection identifies use of DSInternals modules for illegal management of A
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Remove-ADDBObject/)=true OR match_regex(cmd_line, /(?i)Set-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Set-ADDBPrimaryGroup/)=true OR match_regex(cmd_line, /(?i)Set-LsaPolicyInformation/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9852,11 +9312,11 @@ None identified. ---- -==={{visible anchor|Illegal Management of Computers and Active Directory Elements via PowerSploit modules|illegal-management-of-computers-and-active-directory-elements-via-powersploit-modules}}=== +===Illegal Management of Computers and Active Directory Elements via PowerSploit modules=== This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1207/ T1207], [https://attack.mitre.org/techniques/T1484/ T1484] * '''Last Updated''': 2020-11-09 @@ -9864,15 +9324,15 @@ This detection identifies access to PowerSploit modules that enable illegal mana
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainObject/)=true OR match_regex(cmd_line, /(?i)Set-ADObject/)=true OR match_regex(cmd_line, /(?i)Set-DomainObjectOwner/)=true OR match_regex(cmd_line, /(?i)Set-MasterBootRecord/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9932,11 +9392,11 @@ None identified. ---- -==={{visible anchor|Illegal Privilege Elevation and Persistence via PowerSploit modules|illegal-privilege-elevation-and-persistence-via-powersploit-modules}}=== +===Illegal Privilege Elevation and Persistence via PowerSploit modules=== This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1053/ T1053], [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1548/ T1548] * '''Last Updated''': 2020-11-09 @@ -9944,14 +9404,14 @@ This detection identifies access to PowerSploit modules that illegaly elevate ge
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Add-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Enable-Privilege/)=true OR match_regex(cmd_line, /(?i)New-ElevatedPersistenceOption/)=true OR match_regex(cmd_line, /(?i)New-UserPersistenceOption/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -10011,11 +9471,11 @@ None identified. ---- -==={{visible anchor|Illegal Privilege Elevation via Mimikatz modules|illegal-privilege-elevation-via-mimikatz-modules}}=== +===Illegal Privilege Elevation via Mimikatz modules=== This detection identifies use of Mimikatz modules for illegal privilege elevation. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1548/ T1548] * '''Last Updated''': 2020-11-09 @@ -10023,14 +9483,14 @@ This detection identifies use of Mimikatz modules for illegal privilege elevatio
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)privilege::debug/)=true OR match_regex(cmd_line, /(?i)token::elevate/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -10086,11 +9546,11 @@ None identified. ---- -==={{visible anchor|Illegal Service and Process Control via Mimikatz modules|illegal-service-and-process-control-via-mimikatz-modules}}=== +===Illegal Service and Process Control via Mimikatz modules=== This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1106/ T1106], [https://attack.mitre.org/techniques/T1569/ T1569] * '''Last Updated''': 2020-11-09 @@ -10098,14 +9558,14 @@ This detection identifies use of Mimikatz modules for illegal control over servi
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::start/)=true OR match_regex(cmd_line, /(?i)service::\+/)=true OR match_regex(cmd_line, /(?i)service::\-/)=true OR match_regex(cmd_line, /(?i)service::start/)=true OR match_regex(cmd_line, /(?i)service::stop/)=true OR match_regex(cmd_line, /(?i)service::suspend/)=true OR match_regex(cmd_line, /(?i)misc::memssp/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -10165,11 +9625,11 @@ None identified. ---- -==={{visible anchor|Illegal Service and Process Control via PowerSploit modules|illegal-service-and-process-control-via-powersploit-modules}}=== +===Illegal Service and Process Control via PowerSploit modules=== This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1106/ T1106], [https://attack.mitre.org/techniques/T1569/ T1569] * '''Last Updated''': 2020-11-09 @@ -10177,15 +9637,15 @@ This detection identifies access to PowerSploit modules that enable illegal cont
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Install-SSP/)=true OR match_regex(cmd_line, /(?i)Set-CriticalProcess/)=true OR match_regex(cmd_line, /(?i)Install-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Restore-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Write-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Set-ServiceBinaryPath/)=true OR match_regex(cmd_line, /(?i)Invoke-ReflectivePEInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-DllInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-ServiceAbuse/)=true OR match_regex(cmd_line, /(?i)Invoke-Shellcode/)=true OR match_regex(cmd_line, /(?i)Invoke-WScriptUACBypass/)=true OR match_regex(cmd_line, /(?i)Invoke-WmiCommand/)=true OR match_regex(cmd_line, /(?i)Write-HijackDll/)=true OR match_regex(cmd_line, /(?i)Add-ServiceDacl/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -10245,11 +9705,11 @@ None identified. ---- -==={{visible anchor|Kerberoasting spn request with RC4 encryption|kerberoasting-spn-request-with-rc4-encryption}}=== +===Kerberoasting spn request with RC4 encryption=== This search detects a potential kerberoasting attack via service principal name requests * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1558.003/ T1558.003] * '''Last Updated''': 2020-10-16 @@ -10257,11 +9717,11 @@ This search detects a potential kerberoasting attack via service principal name
====Search==== -`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 -| stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `kerberoasting_spn_request_with_rc4_encryption_filter` +`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 +| stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `kerberoasting_spn_request_with_rc4_encryption_filter` ====Associated Analytic Story==== @@ -10311,7 +9771,7 @@ Older systems that support kerberos RC4 by default NetApp may generate false pos ---- -==={{visible anchor|Malicious PowerShell Process - Connect To Internet With Hidden Window|malicious-powershell-process---connect-to-internet-with-hidden-window}}=== +===Malicious PowerShell Process - Connect To Internet With Hidden Window=== This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10323,12 +9783,12 @@ This search looks for PowerShell processes started with parameters to modify the
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` +| `security_content_ctime(lastTime)` +| `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` ====Associated Analytic Story==== @@ -10378,7 +9838,7 @@ Legitimate process can have this combination of command-line options, but it's n ---- -==={{visible anchor|Malicious PowerShell Process - Encoded Command|malicious-powershell-process---encoded-command}}=== +===Malicious PowerShell Process - Encoded Command=== This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10390,12 +9850,12 @@ This search looks for PowerShell processes that have encoded the script within t
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `malicious_powershell_process___encoded_command_filter` +| `security_content_ctime(lastTime)` +| `malicious_powershell_process___encoded_command_filter` ====Associated Analytic Story==== @@ -10445,7 +9905,7 @@ System administrators may use this option, but it's not common. ---- -==={{visible anchor|Malicious PowerShell Process - Execution Policy Bypass|malicious-powershell-process---execution-policy-bypass}}=== +===Malicious PowerShell Process - Execution Policy Bypass=== This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10457,12 +9917,12 @@ This search looks for PowerShell processes started with parameters used to bypas
====Search==== - -| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `malicious_powershell_process___execution_policy_bypass_filter` + +| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `malicious_powershell_process___execution_policy_bypass_filter` ====Associated Analytic Story==== @@ -10510,7 +9970,7 @@ There may be legitimate reasons to bypass the PowerShell execution policy. The P ---- -==={{visible anchor|Malicious PowerShell Process With Obfuscation Techniques|malicious-powershell-process-with-obfuscation-techniques}}=== +===Malicious PowerShell Process With Obfuscation Techniques=== This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10522,14 +9982,14 @@ This search looks for PowerShell processes launched with arguments that have cha
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) -| `malicious_powershell_process_with_obfuscation_techniques_filter` -| search num_obfuscation > 10 +| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) +| `malicious_powershell_process_with_obfuscation_techniques_filter` +| search num_obfuscation > 10 ====Associated Analytic Story==== @@ -10577,11 +10037,11 @@ These characters might be legitimately on the command-line, but it is not common ---- -==={{visible anchor|Monitor Registry Keys for Print Monitors|monitor-registry-keys-for-print-monitors}}=== +===Monitor Registry Keys for Print Monitors=== This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1547.010/ T1547.010] * '''Last Updated''': 2020-11-23 @@ -10589,10 +10049,10 @@ This search looks for registry activity associated with modifications to the reg
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action -| `drop_dm_object_name(Registry)` -| `monitor_registry_keys_for_print_monitors_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action +| `drop_dm_object_name(Registry)` +| `monitor_registry_keys_for_print_monitors_filter` ====Associated Analytic Story==== @@ -10640,11 +10100,11 @@ You will encounter noise from legitimate print-monitor registry entries. ---- -==={{visible anchor|More than usual number of LOLBAS applications in short time period|more-than-usual-number-of-lolbas-applications-in-short-time-period}}=== +===More than usual number of LOLBAS applications in short time period=== Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1059/ T1059], [https://attack.mitre.org/techniques/T1053/ T1053] * '''Last Updated''': 2020-08-25 @@ -10652,17 +10112,17 @@ Attacker activity may compromise executing several LOLBAS applications in conjun
====Search==== - -| from read_ssa_enriched_events() -| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) -| where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" -| stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) -| eval lolbas_counter=lolbas_counter*1.0 -| rename window_end as timestamp -| adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L -| where label AND quantile>0.99 -| eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body = "TBD" -| into write_ssa_detected_events(); + +| from read_ssa_enriched_events() +| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" +| stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) +| eval lolbas_counter=lolbas_counter*1.0 +| rename window_end as timestamp +| adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L +| where label AND quantile>0.99 +| eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -10717,67 +10177,7 @@ Some administrative tasks may involve multiple use of LOLBAS applications in a s ---- -==={{visible anchor|Multiple Okta Users With Invalid Credentials From The Same IP|multiple-okta-users-with-invalid-credentials-from-the-same-ip}}=== -This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] -* '''Last Updated''': 2020-07-21 - -
-
- -====Search==== -`okta` outcome.reason=INVALID_CREDENTIALS -| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city -| stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| search distinct_users > 5 -| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` - -====Associated Analytic Story==== - -* Suspicious Okta Activity - - -====How To Implement==== -This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.001 -| Default Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search. - -====Reference==== - - -====Test Dataset==== - - -''version'': 2 -
-
- ----- - -==={{visible anchor|NLTest Domain Trust Discovery|nltest-domain-trust-discovery}}=== +===NLTest Domain Trust Discovery=== This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10789,12 +10189,12 @@ This search looks for the execution of `nltest.exe` with command-line arguments
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `nltest_domain_trust_discovery_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `nltest_domain_trust_discovery_filter` ====Associated Analytic Story==== @@ -10854,64 +10254,7 @@ Administrators may use nltest for troubleshooting purposes, otherwise, rarely us ---- -==={{visible anchor|New container uploaded to AWS ECR|new-container-uploaded-to-aws-ecr}}=== -This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1525/ T1525] -* '''Last Updated''': 2020-02-20 - -
-
- -====Search==== - -| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type -| `drop_dm_object_name("Compute")` -| `new_container_uploaded_to_aws_ecr_filter` - -====Associated Analytic Story==== - -* Container Implantation Monitoring and Investigation - - -====How To Implement==== -You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1525 -| Implant Container Image -| Persistence -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -Uploading container is a normal behavior from developers or users with access to container registry. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Ntdsutil export ntds|ntdsutil-export-ntds}}=== +===Ntdsutil export ntds=== Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \ ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. @@ -10925,12 +10268,12 @@ This technique uses "Install from Media" (IFM), which will extract a copy of the
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `ntdsutil_export_ntds_filter` +| `security_content_ctime(lastTime)` +| `ntdsutil_export_ntds_filter` ====Associated Analytic Story==== @@ -10984,923 +10327,7 @@ Highly possible Server Administrators will troubleshoot with ntdsutil.exe, gener ---- -==={{visible anchor|O365 Add App Role Assignment Grant User|o365-add-app-role-assignment-grant-user}}=== -This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] -* '''Last Updated''': 2021-01-26 - -
-
- -====Search==== -`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." -| stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `o365_add_app_role_assignment_grant_user_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - -* Cloud Federated Credential Abuse - - -====How To Implement==== -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1136.003 -| Cloud Account -| Persistence -|} - -====Kill Chain Phase==== - -* Actions on Objective - - -====Known False Positives==== -The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. - -====Reference==== - -* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf - -* https://us-cert.cisa.gov/ncas/alerts/aa21-008a - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|O365 Added Service Principal|o365-added-service-principal}}=== -This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] -* '''Last Updated''': 2021-01-26 - -
-
- -====Search==== -`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." -| stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `o365_added_service_principal_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - -* Cloud Federated Credential Abuse - - -====How To Implement==== -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1136.003 -| Cloud Account -| Persistence -|} - -====Kill Chain Phase==== - -* Actions on Objective - - -====Known False Positives==== -The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. - -====Reference==== - -* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf - -* https://us-cert.cisa.gov/ncas/alerts/aa21-008a - -* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html - -* https://www.sygnia.co/golden-saml-advisory - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|O365 Bypass MFA via Trusted IP|o365-bypass-mfa-via-trusted-ip}}=== -This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] -* '''Last Updated''': 2021-01-12 - -
-
- -====Search==== -`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy -| rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" -| rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" -| eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") -| mvexpand ip_addresses_new_added -| where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) -|stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account status user_id action -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `o365_bypass_mfa_via_trusted_ip_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - - -====How To Implement==== -You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.007 -| Disable or Modify Cloud Firewall -| Defense Evasion -|} - -====Kill Chain Phase==== - -* Actions on Objective - - -====Known False Positives==== -Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration. - -====Reference==== - -* https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf - -* https://attack.mitre.org/techniques/T1562/007/ - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|O365 Disable MFA|o365-disable-mfa}}=== -This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1556/ T1556] -* '''Last Updated''': 2020-12-16 - -
-
- -====Search==== -`o365_management_activity` Operation="Disable Strong Authentication." -| stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `o365_disable_mfa_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - - -====How To Implement==== -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1556 -| Modify Authentication Process -| Credential Access, Defense Evasion -|} - -====Kill Chain Phase==== - -* Actions on Objective - - -====Known False Positives==== -Unless it is a special case, it is uncommon to disable MFA or Strong Authentication - -====Reference==== - -* https://attack.mitre.org/techniques/T1556/ - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|O365 Excessive Authentication Failures Alert|o365-excessive-authentication-failures-alert}}=== -This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1110/ T1110] -* '''Last Updated''': 2020-12-16 - -
-
- -====Search==== -`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed -| stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user -| where count > 10 -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `o365_excessive_authentication_failures_alert_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - - -====How To Implement==== -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1110 -| Brute Force -| Credential Access -|} - -====Kill Chain Phase==== - -* Not Applicable - - -====Known False Positives==== -The threshold for alert is above 10 attempts and this should reduce the number of false positives. - -====Reference==== - -* https://attack.mitre.org/techniques/T1110/ - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|O365 Excessive SSO logon errors|o365-excessive-sso-logon-errors}}=== -This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1556/ T1556] -* '''Last Updated''': 2021-01-26 - -
-
- -====Search==== -`o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired -| stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId -| where count > 5 -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `o365_excessive_sso_logon_errors_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - -* Cloud Federated Credential Abuse - - -====How To Implement==== -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1556 -| Modify Authentication Process -| Credential Access, Defense Evasion -|} - -====Kill Chain Phase==== - -* Actions on Objective - - -====Known False Positives==== -Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack. - -====Reference==== - -* https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/ - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|O365 New Federated Domain Added|o365-new-federated-domain-added}}=== -This search detects the addition of a new Federated domain. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] -* '''Last Updated''': 2021-01-26 - -
-
- -====Search==== -`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" -| stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `o365_new_federated_domain_added_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - -* Cloud Federated Credential Abuse - - -====How To Implement==== -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1136.003 -| Cloud Account -| Persistence -|} - -====Kill Chain Phase==== - -* Actions on Objective - - -====Known False Positives==== -The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider. - -====Reference==== - -* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf - -* https://us-cert.cisa.gov/ncas/alerts/aa21-008a - -* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html - -* https://www.sygnia.co/golden-saml-advisory - -* https://o365blog.com/post/aadbackdoor/ - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|O365 PST export alert|o365-pst-export-alert}}=== -This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114/ T1114] -* '''Last Updated''': 2020-12-16 - -
-
- -====Search==== -`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" -| stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `o365_pst_export_alert_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - - -====How To Implement==== -You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114 -| Email Collection -| Collection -|} - -====Kill Chain Phase==== - -* Actions on Objective - - -====Known False Positives==== -PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored. - -====Reference==== - -* https://attack.mitre.org/techniques/T1114/ - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|O365 Suspicious Admin Email Forwarding|o365-suspicious-admin-email-forwarding}}=== -This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.003/ T1114.003] -* '''Last Updated''': 2020-12-16 - -
-
- -====Search==== -`o365_management_activity` Operation=Set-Mailbox -| spath input=Parameters -| rename Identity AS src_user -| search ForwardingAddress=* -| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress -| where count_src_user > 1 -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -|`o365_suspicious_admin_email_forwarding_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - - -====How To Implement==== - - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114.003 -| Email Forwarding Rule -| Collection -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -unknown - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|O365 Suspicious Rights Delegation|o365-suspicious-rights-delegation}}=== -This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.002/ T1114.002] -* '''Last Updated''': 2020-12-15 - -
-
- -====Search==== -`o365_management_activity` Operation=Add-MailboxPermission -| spath input=Parameters -| rename User AS src_user, Identity AS dest_user -| search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf -| stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -|`o365_suspicious_rights_delegation_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - - -====How To Implement==== - - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114.002 -| Remote Email Collection -| Collection -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -Service Accounts - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|O365 Suspicious User Email Forwarding|o365-suspicious-user-email-forwarding}}=== -This search detects when multiple user configured a forwarding rule to the same destination. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.003/ T1114.003] -* '''Last Updated''': 2020-12-16 - -
-
- -====Search==== -`o365_management_activity` Operation=Set-Mailbox -| spath input=Parameters -| rename Identity AS src_user -| search ForwardingSmtpAddress=* -| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress -| where count_src_user > 1 -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -|`o365_suspicious_user_email_forwarding_filter` - -====Associated Analytic Story==== - -* Office 365 Detections - - -====How To Implement==== - - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114.003 -| Email Forwarding Rule -| Collection -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -unknown - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Okta Account Lockout Events|okta-account-lockout-events}}=== -Detect Okta user lockout events - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] -* '''Last Updated''': 2020-07-21 - -
-
- -====Search==== -`okta` displayMessage="Max sign in attempts exceeded" -| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city -| table _time, user, country, state, city, src_ip -| `okta_account_lockout_events_filter` - -====Associated Analytic Story==== - -* Suspicious Okta Activity - - -====How To Implement==== -This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.001 -| Default Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. - -====Reference==== - - -====Test Dataset==== - - -''version'': 2 -
-
- ----- - -==={{visible anchor|Okta Failed SSO Attempts|okta-failed-sso-attempts}}=== -Detect failed Okta SSO events - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] -* '''Last Updated''': 2020-07-21 - -
-
- -====Search==== -`okta` displayMessage="User attempted unauthorized access to app" -| stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `okta_failed_sso_attempts_filter` - -====Associated Analytic Story==== - -* Suspicious Okta Activity - - -====How To Implement==== -This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.001 -| Default Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -There may be a faulty config preventing legitmate users from accessing apps they should have access to. - -====Reference==== - - -====Test Dataset==== - - -''version'': 2 -
-
- ----- - -==={{visible anchor|Okta User Logins From Multiple Cities|okta-user-logins-from-multiple-cities}}=== -This search detects logins from the same user from different cities in a 24 hour period. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] -* '''Last Updated''': 2020-07-21 - -
-
- -====Search==== -`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null -| stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `okta_user_logins_from_multiple_cities_filter` -| search locations > 1 - -====Associated Analytic Story==== - -* Suspicious Okta Activity - - -====How To Implement==== -This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.001 -| Default Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} - -====Kill Chain Phase==== - - -====Known False Positives==== -Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. - -====Reference==== - - -====Test Dataset==== - - -''version'': 2 -
-
- ----- - -==={{visible anchor|Overwriting Accessibility Binaries|overwriting-accessibility-binaries}}=== +===Overwriting Accessibility Binaries=== Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -11912,12 +10339,12 @@ Microsoft Windows contains accessibility features that can be launched with a ke
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `overwriting_accessibility_binaries_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `overwriting_accessibility_binaries_filter` ====Associated Analytic Story==== @@ -11963,11 +10390,11 @@ Microsoft may provide updates to these binaries. Verify that these changes do no ---- -==={{visible anchor|Probing Access with Stolen Credentials via PowerSploit modules|probing-access-with-stolen-credentials-via-powersploit-modules}}=== +===Probing Access with Stolen Credentials via PowerSploit modules=== This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-04 @@ -11975,14 +10402,14 @@ This detection identifies use of PowerSploit modules that facilitate access prob
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-AdminAccess/)=true OR match_regex(cmd_line, /(?i)Invoke-CheckLocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Test-ServiceDaclPermission/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12038,11 +10465,11 @@ None identified. ---- -==={{visible anchor|Process Creating LNK file in Suspicious Location|process-creating-lnk-file-in-suspicious-location}}=== +===Process Creating LNK file in Suspicious Location=== This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1566.002/ T1566.002] * '''Last Updated''': 2021-01-28 @@ -12050,19 +10477,19 @@ This search looks for a process launching an `*.lnk` file under `C:\User*` or `*
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user -| `drop_dm_object_name(Filesystem)` -| rename process_id as lnk_pid + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user +| `drop_dm_object_name(Filesystem)` +| rename process_id as lnk_pid | join lnk_pid, _time [ -| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process -| `drop_dm_object_name(Processes)` -| rename parent_process_id as lnk_pid -| fields _time lnk_pid process_id dest process_name process_path process] -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash -| `process_creating_lnk_file_in_suspicious_location_filter` +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process +| `drop_dm_object_name(Processes)` +| rename parent_process_id as lnk_pid +| fields _time lnk_pid process_id dest process_name process_path process] +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash +| `process_creating_lnk_file_in_suspicious_location_filter` ====Associated Analytic Story==== @@ -12114,11 +10541,11 @@ This detection should yield little or no false positive results. It is uncommon ---- -==={{visible anchor|Process Execution via WMI|process-execution-via-wmi}}=== +===Process Execution via WMI=== This search looks for processes launched via WMI. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1047/ T1047] * '''Last Updated''': 2020-03-16 @@ -12126,12 +10553,12 @@ This search looks for processes launched via WMI.
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name -| `drop_dm_object_name("Processes")` + +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| `process_execution_via_wmi_filter` +| `process_execution_via_wmi_filter` ====Associated Analytic Story==== @@ -12177,7 +10604,7 @@ Although unlikely, administrators may use wmi to execute commands for legitimate ---- -==={{visible anchor|Processes launching netsh|processes-launching-netsh}}=== +===Processes launching netsh=== This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12189,12 +10616,12 @@ This search looks for processes launching netsh.exe. Netsh is a command-line scr
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest -|`drop_dm_object_name("Processes")` -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -|`processes_launching_netsh_filter` + +| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest +|`drop_dm_object_name("Processes")` +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +|`processes_launching_netsh_filter` ====Associated Analytic Story==== @@ -12244,11 +10671,11 @@ Some VPN applications are known to launch netsh.exe. Outside of these instances, ---- -==={{visible anchor|Rare Parent-Child Process Relationship|rare-parent-child-process-relationship}}=== +===Rare Parent-Child Process Relationship=== An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203], [https://attack.mitre.org/techniques/T1059/ T1059], [https://attack.mitre.org/techniques/T1053/ T1053], [https://attack.mitre.org/techniques/T1072/ T1072] * '''Last Updated''': 2020-08-13 @@ -12256,20 +10683,20 @@ An attacker may use LOLBAS tools spawned from vulnerable applications not typica
====Search==== - -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) -| eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) -| where parent_process_name!=null -| select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id -| conditional_anomaly conditional="parent_process_name" target="process_name" -| rename output as input -| where input < 1 -| adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) +| where parent_process_name!=null +| select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id +| conditional_anomaly conditional="parent_process_name" target="process_name" +| rename output as input +| where input < 1 +| adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L | where label AND quantile<0.1 AND (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe") -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12334,11 +10761,11 @@ Some custom tools used by admins could be used rarely to launch remotely applica ---- -==={{visible anchor|Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules|reconnaissance-and-access-to-accounts-groups-and-policies-via-powersploit-modules}}=== +===Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules=== This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1087/ T1087], [https://attack.mitre.org/techniques/T1484/ T1484] * '''Last Updated''': 2020-11-05 @@ -12346,14 +10773,14 @@ This detection identifies access to PowerSploit modules that discover accounts,
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Invoke-EnumerateLocalAdmin/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Invoke-EventHunter/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserLocation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserHunter/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignGroupMember/)=true OR match_regex(cmd_line, /(?i)Find-ForeignGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignUser/)=true OR match_regex(cmd_line, /(?i)Find-ForeignUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPO/)=true OR match_regex(cmd_line, /(?i)Get-NetGPO/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOComputerLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOComputerAdmin/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGPOGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOUserLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOLocation/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-DomainManagedSecurityGroup/)=true OR match_regex(cmd_line, /(?i)Find-ManagedSecurityGroups/)=true OR match_regex(cmd_line, /(?i)Get-DomainOU/)=true OR match_regex(cmd_line, /(?i)Get-NetOU/)=true OR match_regex(cmd_line, /(?i)Get-DomainUser/)=true OR match_regex(cmd_line, /(?i)Get-NetUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Get-UserEvent/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetLoggedon/)=true OR match_regex(cmd_line, /(?i)Get-RegLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegLastLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-LastLoggedOn/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12413,11 +10840,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance and Access to Accounts and Groups via Mimikatz modules|reconnaissance-and-access-to-accounts-and-groups-via-mimikatz-modules}}=== +===Reconnaissance and Access to Accounts and Groups via Mimikatz modules=== This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1087/ T1087], [https://attack.mitre.org/techniques/T1484/ T1484] * '''Last Updated''': 2020-11-05 @@ -12425,14 +10852,14 @@ This detection identifies use of Mimikatz modules for discovery of accounts and
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::user/)=true OR match_regex(cmd_line, /(?i)net::group/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12492,11 +10919,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules|reconnaissance-and-access-to-active-directoty-infrastructure-via-powersploit-modules}}=== +===Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules=== This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1199/ T1199], [https://attack.mitre.org/techniques/T1482/ T1482], [https://attack.mitre.org/techniques/T1590/ T1590], [https://attack.mitre.org/techniques/T1591/ T1591], [https://attack.mitre.org/techniques/T1595/ T1595] * '''Last Updated''': 2020-11-06 @@ -12504,14 +10931,14 @@ This detection identifies access to PowerSploit modules for reconnaissance and a
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainSID/)=true OR match_regex(cmd_line, /(?i)Get-DomainSite/)=true OR match_regex(cmd_line, /(?i)Get-NetSite/)=true OR match_regex(cmd_line, /(?i)Get-DomainSubnet/)=true OR match_regex(cmd_line, /(?i)Get-NetSubnet/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrustMapping/)=true OR match_regex(cmd_line, /(?i)Invoke-MapDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-Forest/)=true OR match_regex(cmd_line, /(?i)Get-NetForest/)=true OR match_regex(cmd_line, /(?i)Get-ForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-NetForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-ForestGlobalCatalog/)=true OR match_regex(cmd_line, /(?i)Get-NetForestCatalog/)=true OR match_regex(cmd_line, /(?i)Get-ForestTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetForestTrust/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12579,11 +11006,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance and Access to Computers and Domains via PowerSploit modules|reconnaissance-and-access-to-computers-and-domains-via-powersploit-modules}}=== +===Reconnaissance and Access to Computers and Domains via PowerSploit modules=== This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1592/ T1592], [https://attack.mitre.org/techniques/T1590/ T1590], [https://attack.mitre.org/techniques/T1087/ T1087] * '''Last Updated''': 2020-11-06 @@ -12591,14 +11018,14 @@ This detection identifies access to PowerSploit modules that discover computers,
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ComputerDetail/)=true OR match_regex(cmd_line, /(?i)Get-Domain/)=true OR match_regex(cmd_line, /(?i)Get-NetDomain/)=true OR match_regex(cmd_line, /(?i)Get-DomainComputer/)=true OR match_regex(cmd_line, /(?i)Get-NetComputer/)=true OR match_regex(cmd_line, /(?i)Get-DomainController/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainController/)=true OR match_regex(cmd_line, /(?i)Get-DomainFileServer/)=true OR match_regex(cmd_line, /(?i)Get-NetFileServer/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12658,11 +11085,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance and Access to Computers via Mimikatz modules|reconnaissance-and-access-to-computers-via-mimikatz-modules}}=== +===Reconnaissance and Access to Computers via Mimikatz modules=== This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1592/ T1592] * '''Last Updated''': 2020-11-06 @@ -12670,14 +11097,14 @@ This detection identifies use of Mimikatz modules for discovery of computers and
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::ServerInfo/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12729,11 +11156,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance and Access to Operating System Elements via PowerSploit modules|reconnaissance-and-access-to-operating-system-elements-via-powersploit-modules}}=== +===Reconnaissance and Access to Operating System Elements via PowerSploit modules=== This detection identifies access to PowerSploit modules that discover and access operating system elements, such as processes, services, registry locations, security packages and files. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1007/ T1007], [https://attack.mitre.org/techniques/T1012/ T1012], [https://attack.mitre.org/techniques/T1046/ T1046], [https://attack.mitre.org/techniques/T1047/ T1047], [https://attack.mitre.org/techniques/T1057/ T1057], [https://attack.mitre.org/techniques/T1083/ T1083], [https://attack.mitre.org/techniques/T1518/ T1518], [https://attack.mitre.org/techniques/T1592.002/ T1592.002] * '''Last Updated''': 2020-11-06 @@ -12741,14 +11168,14 @@ This detection identifies access to PowerSploit modules that discover and access
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainProcess/)=true OR match_regex(cmd_line, /(?i)Invoke-ProcessHunter/)=true OR match_regex(cmd_line, /(?i)Get-ServiceDetail/)=true OR match_regex(cmd_line, /(?i)Get-WMIProcess/)=true OR match_regex(cmd_line, /(?i)Get-NetProcess/)=true OR match_regex(cmd_line, /(?i)Get-SecurityPackage/)=true OR match_regex(cmd_line, /(?i)Find-DomainObjectPropertyOutlier/)=true OR match_regex(cmd_line, /(?i)Get-DomainObject/)=true OR match_regex(cmd_line, /(?i)Get-ADObject/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegMountedDrive/)=true OR match_regex(cmd_line, /(?i)Get-RegistryMountedDrive/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12828,11 +11255,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance and Access to Processes and Services via Mimikatz modules|reconnaissance-and-access-to-processes-and-services-via-mimikatz-modules}}=== +===Reconnaissance and Access to Processes and Services via Mimikatz modules=== This detection identifies use of Mimikatz modules for discovery and access to services and processes. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1007/ T1007], [https://attack.mitre.org/techniques/T1046/ T1046], [https://attack.mitre.org/techniques/T1057/ T1057] * '''Last Updated''': 2020-11-06 @@ -12840,14 +11267,14 @@ This detection identifies use of Mimikatz modules for discovery and access to se
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::list/)=true OR match_regex(cmd_line, /(?i)service::list/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12907,11 +11334,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance and Access to Shared Resources via Mimikatz modules|reconnaissance-and-access-to-shared-resources-via-mimikatz-modules}}=== +===Reconnaissance and Access to Shared Resources via Mimikatz modules=== This detection identifies use of Mimikatz modules for discovery and access to network shares. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1135/ T1135], [https://attack.mitre.org/techniques/T1039/ T1039] * '''Last Updated''': 2020-11-06 @@ -12919,14 +11346,14 @@ This detection identifies use of Mimikatz modules for discovery and access to ne
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::share/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12986,11 +11413,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance and Access to Shared Resources via PowerSploit modules|reconnaissance-and-access-to-shared-resources-via-powersploit-modules}}=== +===Reconnaissance and Access to Shared Resources via PowerSploit modules=== This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1135/ T1135], [https://attack.mitre.org/techniques/T1039/ T1039] * '''Last Updated''': 2020-11-06 @@ -12998,14 +11425,14 @@ This detection identifies access to PowerSploit modules that discover and access
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainShare/)=true OR match_regex(cmd_line, /(?i)Invoke-ShareFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainShareFile/)=true OR match_regex(cmd_line, /(?i)Invoke-FileFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingFile/)=true OR match_regex(cmd_line, /(?i)Get-DomainDFSShare/)=true OR match_regex(cmd_line, /(?i)Get-DFSshare/)=true OR match_regex(cmd_line, /(?i)Get-NetShare/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -13065,11 +11492,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance of Access and Persistence Opportunities via PowerSploit modules|reconnaissance-of-access-and-persistence-opportunities-via-powersploit-modules}}=== +===Reconnaissance of Access and Persistence Opportunities via PowerSploit modules=== This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1053/ T1053], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1547/ T1547], [https://attack.mitre.org/techniques/T1574/ T1574] * '''Last Updated''': 2020-11-05 @@ -13077,14 +11504,14 @@ This detection identifies use of PowerSploit modules that discover opportunities
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-LocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainAcl/)=true OR match_regex(cmd_line, /(?i)Invoke-ACLScanner/)=true OR match_regex(cmd_line, /(?i)Find-PathDLLHijack/)=true OR match_regex(cmd_line, /(?i)Find-ProcessDLLHijack/)=true OR match_regex(cmd_line, /(?i)Get-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-DomainPolicy/)=true OR match_regex(cmd_line, /(?i)Get-ModifiablePath/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableRegistryAutoRun/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableScheduledTaskFile/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableService/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableServiceFile/)=true OR match_regex(cmd_line, /(?i)Get-PathAcl/)=true OR match_regex(cmd_line, /(?i)Get-UnattendedInstallFile/)=true OR match_regex(cmd_line, /(?i)Get-UnquotedService/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -13156,11 +11583,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance of Connectivity via PowerSploit modules|reconnaissance-of-connectivity-via-powersploit-modules}}=== +===Reconnaissance of Connectivity via PowerSploit modules=== This detection identifies access to PowerSploit modules for reconnaissance of connectivity. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1135/ T1135], [https://attack.mitre.org/techniques/T1039/ T1039] * '''Last Updated''': 2020-11-06 @@ -13168,14 +11595,14 @@ This detection identifies access to PowerSploit modules for reconnaissance of co
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainDNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DomainDNSZone/)=true OR match_regex(cmd_line, /(?i)Get-DNSZone/)=true OR match_regex(cmd_line, /(?i)Invoke-ReverseDnsLookup/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegCachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-CachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegProxy/)=true OR match_regex(cmd_line, /(?i)Get-Proxy/)=true OR match_regex(cmd_line, /(?i)Invoke-Portscan/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -13235,11 +11662,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance of Credential Stores and Services via Mimikatz modules|reconnaissance-of-credential-stores-and-services-via-mimikatz-modules}}=== +===Reconnaissance of Credential Stores and Services via Mimikatz modules=== This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1589.001/ T1589.001], [https://attack.mitre.org/techniques/T1590.001/ T1590.001], [https://attack.mitre.org/techniques/T1590.003/ T1590.003], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-03 @@ -13247,14 +11674,14 @@ This detection identifies reconnaissance of credential stores and use of CryptoA
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)crypto::capi/)=true OR match_regex(cmd_line, /(?i)crypto::cng/)=true OR match_regex(cmd_line, /(?i)crypto::providers/)=true OR match_regex(cmd_line, /(?i)crypto::stores/)=true OR match_regex(cmd_line, /(?i)crypto::sc/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -13326,11 +11753,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance of Defensive Tools via PowerSploit modules|reconnaissance-of-defensive-tools-via-powersploit-modules}}=== +===Reconnaissance of Defensive Tools via PowerSploit modules=== This detection identifies use of PowerSploit modules for assessment of presence of defensive tools. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1595.002/ T1595.002], [https://attack.mitre.org/techniques/T1592.002/ T1592.002] * '''Last Updated''': 2020-11-05 @@ -13338,14 +11765,14 @@ This detection identifies use of PowerSploit modules for assessment of presence
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-AVSignature/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -13401,11 +11828,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules|reconnaissance-of-privilege-escalation-opportunities-via-powersploit-modules}}=== +===Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules=== This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-05 @@ -13413,14 +11840,14 @@ This detection identifies use of PowerSploit modules for assessment of privilege
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-PrivescAudit/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -13480,11 +11907,11 @@ None identified. ---- -==={{visible anchor|Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules|reconnaissance-of-process-or-service-hijacking-opportunities-via-mimikatz-modules}}=== +===Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules=== This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1574/ T1574] * '''Last Updated''': 2020-11-05 @@ -13492,14 +11919,14 @@ This detection identifies use of Mimikatz modules for discovery of process or se
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::detours/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -13561,7 +11988,7 @@ None identified. ---- -==={{visible anchor|Reg exe Manipulating Windows Services Registry Keys|reg-exe-manipulating-windows-services-registry-keys}}=== +===Reg exe Manipulating Windows Services Registry Keys=== The search looks for reg.exe modifying registry keys that define Windows services and their configurations. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13573,12 +12000,12 @@ The search looks for reg.exe modifying registry keys that define Windows service
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `reg_exe_manipulating_windows_services_registry_keys_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `reg_exe_manipulating_windows_services_registry_keys_filter` ====Associated Analytic Story==== @@ -13626,11 +12053,11 @@ It is unusual for a service to be created or modified by directly manipulating t ---- -==={{visible anchor|Registry Keys Used For Persistence|registry-keys-used-for-persistence}}=== +===Registry Keys Used For Persistence=== The search looks for modifications to registry keys that can be used to launch an application or service at system startup. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1547.001/ T1547.001] * '''Last Updated''': 2020-11-27 @@ -13638,12 +12065,12 @@ The search looks for modifications to registry keys that can be used to launch a
====Search==== - -| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `drop_dm_object_name(Registry)` -| `registry_keys_used_for_persistence_filter` + +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `registry_keys_used_for_persistence_filter` ====Associated Analytic Story==== @@ -13659,7 +12086,7 @@ The search looks for modifications to registry keys that can be used to launch a * Windows Persistence Techniques -* Emotet Malware DHS Report TA18-201A +* Emotet Malware DHS Report TA18-201A ====How To Implement==== @@ -13701,11 +12128,11 @@ There are many legitimate applications that must execute on system startup and w ---- -==={{visible anchor|Registry Keys Used For Privilege Escalation|registry-keys-used-for-privilege-escalation}}=== +===Registry Keys Used For Privilege Escalation=== This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.012/ T1546.012] * '''Last Updated''': 2020-11-27 @@ -13713,12 +12140,12 @@ This search looks for modifications to registry keys that can be used to elevate
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `drop_dm_object_name(Registry)` -| `registry_keys_used_for_privilege_escalation_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `registry_keys_used_for_privilege_escalation_filter` ====Associated Analytic Story==== @@ -13770,11 +12197,11 @@ There are many legitimate applications that must execute upon system startup and ---- -==={{visible anchor|Registry Keys for Creating SHIM Databases|registry-keys-for-creating-shim-databases}}=== +===Registry Keys for Creating SHIM Databases=== This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.011/ T1546.011] * '''Last Updated''': 2020-11-26 @@ -13782,12 +12209,12 @@ This search looks for registry activity associated with application compatibilit
====Search==== - -| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `drop_dm_object_name(Registry)` -| `registry_keys_for_creating_shim_databases_filter` + +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `registry_keys_for_creating_shim_databases_filter` ====Associated Analytic Story==== @@ -13835,140 +12262,7 @@ There are many legitimate applications that leverage shim databases for compatib ---- -==={{visible anchor|Remote Desktop Network Bruteforce|remote-desktop-network-bruteforce}}=== -This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001] -* '''Last Updated''': 2020-07-21 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port -| eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 -| where count>(avg + stdev*2) -| rename All_Traffic.src AS src All_Traffic.dest AS dest -| table firstTime lastTime src dest count avg p50 stdev -| `remote_desktop_network_bruteforce_filter` - -====Associated Analytic Story==== - -* SamSam Ransomware - -* Ryuk Ransomware - - -====How To Implement==== -You must ensure that your network traffic data is populating the Network_Traffic data model. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1021.001 -| Remote Desktop Protocol -| Lateral Movement -|} - -====Kill Chain Phase==== - -* Reconnaissance - -* Delivery - - -====Known False Positives==== -RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. - -====Reference==== - - -====Test Dataset==== - - -''version'': 2 -
-
- ----- - -==={{visible anchor|Remote Desktop Network Traffic|remote-desktop-network-traffic}}=== -This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001] -* '''Last Updated''': 2020-07-07 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port -| `drop_dm_object_name("All_Traffic")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `remote_desktop_network_traffic_filter` - -====Associated Analytic Story==== - -* SamSam Ransomware - -* Ryuk Ransomware - -* Hidden Cobra Malware - -* Lateral Movement - - -====How To Implement==== -To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1021.001 -| Remote Desktop Protocol -| Lateral Movement -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -Remote Desktop may be used legitimately by users on the network. - -====Reference==== - - -====Test Dataset==== - - -''version'': 3 -
-
- ----- - -==={{visible anchor|Remote Process Instantiation via WMI|remote-process-instantiation-via-wmi}}=== +===Remote Process Instantiation via WMI=== This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13980,12 +12274,12 @@ This search looks for wmic.exe being launched with parameters to spawn a process
====Search==== - -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `remote_process_instantiation_via_wmi_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `remote_process_instantiation_via_wmi_filter` ====Associated Analytic Story==== @@ -14033,7 +12327,7 @@ The wmic.exe utility is a benign Windows application. It may be used legitimatel ---- -==={{visible anchor|RunDLL Loading DLL By Ordinal|rundll-loading-dll-by-ordinal}}=== +===RunDLL Loading DLL By Ordinal=== This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14045,12 +12339,12 @@ This search looks for executing scripts with rundll32. Adversaries may abuse run
====Search==== - -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `rundll_loading_dll_by_ordinal_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `rundll_loading_dll_by_ordinal_filter` ====Associated Analytic Story==== @@ -14096,11 +12390,11 @@ While not common, loading a DLL under %AppData% and calling a function by ordina ---- -==={{visible anchor|Ryuk Test Files Detected|ryuk-test-files-detected}}=== +===Ryuk Test Files Detected=== The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1486/ T1486] * '''Last Updated''': 2020-11-06 @@ -14108,12 +12402,12 @@ The search looks for files that contain the key word *Ryuk* under any folder in
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `ryuk_test_files_detected_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `ryuk_test_files_detected_filter` ====Associated Analytic Story==== @@ -14159,152 +12453,7 @@ If there are files with this keywoord as file names it might trigger false possi ---- -==={{visible anchor|SMB Traffic Spike|smb-traffic-spike}}=== -This search looks for spikes in the number of Server Message Block (SMB) traffic connections. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002] -* '''Last Updated''': 2020-07-22 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src -| `drop_dm_object_name("All_Traffic")` -| eventstats max(_time) as maxtime -| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) -| where isOutlier=1 -| table src count -| `smb_traffic_spike_filter` - -====Associated Analytic Story==== - -* Emotet Malware DHS Report TA18-201A - -* Hidden Cobra Malware - -* Ransomware - -* DHS Report TA18-074A - - -====How To Implement==== -This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1021.002 -| SMB/Windows Admin Shares -| Lateral Movement -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -A file server may experience high-demand loads that could cause this analytic to trigger. - -====Reference==== - - -====Test Dataset==== - - -''version'': 3 -
-
- ----- - -==={{visible anchor|SMB Traffic Spike - MLTK|smb-traffic-spike---mltk}}=== -This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002] -* '''Last Updated''': 2020-07-22 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src -| eval HourOfDay=strftime(_time, "%H") -| eval DayOfWeek=strftime(_time, "%A") -| `drop_dm_object_name(All_Traffic)` -| apply smb_pdfmodel threshold=0.001 -| rename "IsOutlier(count)" as isOutlier -| search isOutlier > 0 -| sort -count -| table _time src dest port count -| `smb_traffic_spike___mltk_filter` - -====Associated Analytic Story==== - -* Emotet Malware DHS Report TA18-201A - -* Hidden Cobra Malware - -* Ransomware - -* DHS Report TA18-074A - - -====How To Implement==== -To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ -This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ -1. **Label:** Number of events, **Field:** count\ -Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1021.002 -| SMB/Windows Admin Shares -| Lateral Movement -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results - -====Reference==== - - -====Test Dataset==== - - -''version'': 3 -
-
- ----- - -==={{visible anchor|Samsam Test File Write|samsam-test-file-write}}=== +===Samsam Test File Write=== The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14316,12 +12465,12 @@ The search looks for a file named "test.txt" written to the windows system direc
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `samsam_test_file_write_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `samsam_test_file_write_filter` ====Associated Analytic Story==== @@ -14367,7 +12516,7 @@ No false positives have been identified. ---- -==={{visible anchor|Sc exe Manipulating Windows Services|sc-exe-manipulating-windows-services}}=== +===Sc exe Manipulating Windows Services=== This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14379,12 +12528,12 @@ This search looks for arguments to sc.exe indicating the creation or modificatio
====Search==== - -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `sc_exe_manipulating_windows_services_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `sc_exe_manipulating_windows_services_filter` ====Associated Analytic Story==== @@ -14440,7 +12589,7 @@ Using sc.exe to manipulate Windows services is uncommon. However, there may be l ---- -==={{visible anchor|Scheduled Task Deleted Or Created via CMD|scheduled-task-deleted-or-created-via-cmd}}=== +===Scheduled Task Deleted Or Created via CMD=== This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14452,12 +12601,12 @@ This search looks for flags passed to schtasks.exe on the command-line that indi
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `scheduled_task_deleted_or_created_via_cmd_filter` +| `security_content_ctime(lastTime)` +| `scheduled_task_deleted_or_created_via_cmd_filter` ====Associated Analytic Story==== @@ -14505,7 +12654,7 @@ Tasks should not be manually created via CLI, this is rarely done by admins as w ---- -==={{visible anchor|Schtasks scheduling job on remote system|schtasks-scheduling-job-on-remote-system}}=== +===Schtasks scheduling job on remote system=== This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14517,12 +12666,12 @@ This search looks for flags passed to schtasks.exe on the command-line that indi
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `schtasks_scheduling_job_on_remote_system_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `schtasks_scheduling_job_on_remote_system_filter` ====Associated Analytic Story==== @@ -14570,7 +12719,7 @@ Administrators may create jobs on remote systems, but this activity is usually l ---- -==={{visible anchor|Schtasks used for forcing a reboot|schtasks-used-for-forcing-a-reboot}}=== +===Schtasks used for forcing a reboot=== This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14582,12 +12731,12 @@ This search looks for flags passed to schtasks.exe on the command-line that indi
====Search==== - -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `schtasks_used_for_forcing_a_reboot_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `schtasks_used_for_forcing_a_reboot_filter` ====Associated Analytic Story==== @@ -14635,11 +12784,11 @@ Administrators may create jobs on systems forcing reboots to perform updates, ma ---- -==={{visible anchor|Script Execution via WMI|script-execution-via-wmi}}=== +===Script Execution via WMI=== This search looks for scripts launched via WMI. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1047/ T1047] * '''Last Updated''': 2020-03-16 @@ -14647,12 +12796,12 @@ This search looks for scripts launched via WMI.
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name -| `drop_dm_object_name("Processes")` + +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| `script_execution_via_wmi_filter` +| `script_execution_via_wmi_filter` ====Associated Analytic Story==== @@ -14698,11 +12847,11 @@ Although unlikely, administrators may use wmi to launch scripts for legitimate p ---- -==={{visible anchor|Setting Credentials via DSInternals modules|setting-credentials-via-dsinternals-modules}}=== +===Setting Credentials via DSInternals modules=== This detection identifies illegal setting of credentials via DSInternals modules. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-03 @@ -14710,14 +12859,14 @@ This detection identifies illegal setting of credentials via DSInternals modules
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-ADDBSidHistory/)=true OR match_regex(cmd_line, /(?i)Add-ADReplNgcKey/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPassword/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-ADDBBootKey/)=true OR match_regex(cmd_line, /(?i)Set-SamAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-AzureADUserEx/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -14783,11 +12932,11 @@ None identified. ---- -==={{visible anchor|Setting Credentials via Mimikatz modules|setting-credentials-via-mimikatz-modules}}=== +===Setting Credentials via Mimikatz modules=== This detection identifies illegal setting of credentials via Mimikatz modules. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-03 @@ -14795,14 +12944,14 @@ This detection identifies illegal setting of credentials via Mimikatz modules.
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::addsid/)=true OR match_regex(cmd_line, /(?i)CRYPTO::scauth/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -14862,11 +13011,11 @@ None identified. ---- -==={{visible anchor|Setting Credentials via PowerSploit modules|setting-credentials-via-powersploit-modules}}=== +===Setting Credentials via PowerSploit modules=== This detection identifies illegal setting of credentials via PowerSploit modules. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-03 @@ -14874,14 +13023,14 @@ This detection identifies illegal setting of credentials via PowerSploit modules
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainUserPassword/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" -| into write_ssa_detected_events(); +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -14941,11 +13090,11 @@ None identified. ---- -==={{visible anchor|Shim Database File Creation|shim-database-file-creation}}=== +===Shim Database File Creation=== This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.011/ T1546.011] * '''Last Updated''': 2020-12-08 @@ -14953,12 +13102,12 @@ This search looks for shim database files being written to default directories.
====Search==== - -| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -|`drop_dm_object_name(Filesystem)` -| `shim_database_file_creation_filter` + +| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +|`drop_dm_object_name(Filesystem)` +| `shim_database_file_creation_filter` ====Associated Analytic Story==== @@ -15004,7 +13153,7 @@ Because legitimate shim files are created and used all the time, this event, in ---- -==={{visible anchor|Shim Database Installation With Suspicious Parameters|shim-database-installation-with-suspicious-parameters}}=== +===Shim Database Installation With Suspicious Parameters=== This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15016,12 +13165,12 @@ This search detects the process execution and arguments required to silently cre
====Search==== - -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `shim_database_installation_with_suspicious_parameters_filter` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `shim_database_installation_with_suspicious_parameters_filter` ====Associated Analytic Story==== @@ -15067,7 +13216,7 @@ None identified ---- -==={{visible anchor|Short Lived Windows Accounts|short-lived-windows-accounts}}=== +===Short Lived Windows Accounts=== This search detects accounts that were created and deleted in a short time period. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15079,15 +13228,15 @@ This search detects accounts that were created and deleted in a short time perio
====Search==== - -| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `drop_dm_object_name("All_Changes")` -| search result_id = 4720 result_id=4726 -| transaction user connected=false maxspan=240m -| table firstTime lastTime count user dest result_id -| `short_lived_windows_accounts_filter` + +| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name("All_Changes")` +| search result_id = 4720 result_id=4726 +| transaction user connected=false maxspan=240m +| table firstTime lastTime count user dest result_id +| `short_lived_windows_accounts_filter` ====Associated Analytic Story==== @@ -15135,7 +13284,7 @@ It is possible that an administrator created and deleted an account in a short t ---- -==={{visible anchor|Single Letter Process On Endpoint|single-letter-process-on-endpoint}}=== +===Single Letter Process On Endpoint=== This search looks for process names that consist only of a single letter. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15147,15 +13296,15 @@ This search looks for process names that consist only of a single letter.
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name -| `drop_dm_object_name(Processes)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) -| search process_name_length=5 AND endExe=1 -| table count, firstTime, lastTime, dest, user, process, process_name -| `single_letter_process_on_endpoint_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name +| `drop_dm_object_name(Processes)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) +| search process_name_length=5 AND endExe=1 +| table count, firstTime, lastTime, dest, user, process, process_name +| `single_letter_process_on_endpoint_filter` ====Associated Analytic Story==== @@ -15201,11 +13350,11 @@ Single-letter executables are not always malicious. Investigate this activity wi ---- -==={{visible anchor|Suspicious MSBuild Rename|suspicious-msbuild-rename}}=== +===Suspicious MSBuild Rename=== The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1127.001/ T1127.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2021-01-12 @@ -15213,12 +13362,12 @@ The following analytic identifies renamed instances of msbuild.exe executing. Ms
====Search==== -`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest +`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| `suspicious_msbuild_rename_filter` +| `suspicious_msbuild_rename_filter` ====Associated Analytic Story==== @@ -15274,7 +13423,7 @@ Although unlikely, some legitimate applications may use a moved copy of msbuild, ---- -==={{visible anchor|Suspicious MSBuild Spawn|suspicious-msbuild-spawn}}=== +===Suspicious MSBuild Spawn=== The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15286,12 +13435,12 @@ The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavi
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `suspicious_msbuild_spawn_filter` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_msbuild_spawn_filter` ====Associated Analytic Story==== @@ -15341,11 +13490,11 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg ---- -==={{visible anchor|Suspicious Reg exe Process|suspicious-reg-exe-process}}=== +===Suspicious Reg exe Process=== This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1112/ T1112] * '''Last Updated''': 2020-07-22 @@ -15353,20 +13502,20 @@ This search looks for reg.exe being launched from a command prompt not started b
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | search [ -| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| rename parent_process_id as process_id +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename parent_process_id as process_id |dedup process_id -| table process_id dest] -| `suspicious_reg_exe_process_filter` +| table process_id dest] +| `suspicious_reg_exe_process_filter` ====Associated Analytic Story==== @@ -15418,7 +13567,7 @@ It's possible for system administrators to write scripts that exhibit this behav ---- -==={{visible anchor|Suspicious Regsvr32 Register Suspicious Path|suspicious-regsvr32-register-suspicious-path}}=== +===Suspicious Regsvr32 Register Suspicious Path=== Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15430,12 +13579,12 @@ Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `suspicious_regsvr32_register_suspicious_path_filter` +| `security_content_ctime(lastTime)` +| `suspicious_regsvr32_register_suspicious_path_filter` ====Associated Analytic Story==== @@ -15491,11 +13640,11 @@ Limited false positives with the query restricted to specified paths. Add more w ---- -==={{visible anchor|Suspicious Rundll32 Rename|suspicious-rundll32-rename}}=== +===Suspicious Rundll32 Rename=== The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2021-02-04 @@ -15503,12 +13652,12 @@ The following analytic identifies renamed instances of rundll32.exe executing. r
====Search==== -`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest +`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| `suspicious_rundll32_rename_filter` +| `suspicious_rundll32_rename_filter` ====Associated Analytic Story==== @@ -15564,7 +13713,7 @@ Although unlikely, some legitimate applications may use a moved copy of rundll32 ---- -==={{visible anchor|Suspicious Rundll32 StartW|suspicious-rundll32-startw}}=== +===Suspicious Rundll32 StartW=== The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15576,12 +13725,12 @@ The following analytic identifies rundll32.exe executing a DLL function name, St
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `suspicious_rundll32_startw_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_rundll32_startw_filter` ====Associated Analytic Story==== @@ -15639,7 +13788,7 @@ Although unlikely, some legitimate applications may use Start as a function and ---- -==={{visible anchor|Suspicious Rundll32 dllregisterserver|suspicious-rundll32-dllregisterserver}}=== +===Suspicious Rundll32 dllregisterserver=== The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15651,12 +13800,12 @@ The following analytic identifies rundll32.exe using dllregisterserver on the co
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `suspicious_rundll32_dllregisterserver_filter` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_rundll32_dllregisterserver_filter` ====Associated Analytic Story==== @@ -15716,11 +13865,11 @@ This is likely to produce false positives and will require some filtering. Tune ---- -==={{visible anchor|Suspicious Rundll32 no CommandLine Arguments|suspicious-rundll32-no-commandline-arguments}}=== +===Suspicious Rundll32 no CommandLine Arguments=== The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011] * '''Last Updated''': 2021-02-09 @@ -15728,13 +13877,13 @@ The following analytic identifies rundll32.exe with no command line arguments. I
====Search==== -`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) -| regex CommandLine="(rundll32\.exe.{0,4}$)" -| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `suspicious_rundll32_no_commandline_arguments_filter` +`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) +| regex CommandLine="(rundll32\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_rundll32_no_commandline_arguments_filter` ====Associated Analytic Story==== @@ -15790,11 +13939,11 @@ Although unlikely, some legitimate applications may use a moved copy of rundll32 ---- -==={{visible anchor|Suspicious microsoft workflow compiler rename|suspicious-microsoft-workflow-compiler-rename}}=== +===Suspicious microsoft workflow compiler rename=== The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1127, T1036.003/ T1127, T1036.003] * '''Last Updated''': 2021-01-12 @@ -15802,12 +13951,12 @@ The following analytic identifies a renamed instance of microsoft.workflow.compi
====Search==== -`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest +`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| `suspicious_microsoft_workflow_compiler_rename_filter` +| `suspicious_microsoft_workflow_compiler_rename_filter` ====Associated Analytic Story==== @@ -15826,9 +13975,9 @@ To successfully implement this search, you need to be ingesting logs with the pr ! Technique ! Tactic |- -| -| -| +| +| +| |} ====Kill Chain Phase==== @@ -15857,7 +14006,7 @@ Although unlikely, some legitimate applications may use a moved copy of microsof ---- -==={{visible anchor|Suspicious microsoft workflow compiler usage|suspicious-microsoft-workflow-compiler-usage}}=== +===Suspicious microsoft workflow compiler usage=== The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15869,12 +14018,12 @@ The following analytic identifies microsoft.workflow.compiler.exe usage. microso
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `suspicious_microsoft_workflow_compiler_usage_filter` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_microsoft_workflow_compiler_usage_filter` ====Associated Analytic Story==== @@ -15924,7 +14073,7 @@ Although unlikely, limited instances have been identified coming from native Mic ---- -==={{visible anchor|Suspicious msbuild path|suspicious-msbuild-path}}=== +===Suspicious msbuild path=== The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15936,12 +14085,12 @@ The following analytic identifies msbuild.exe executing from a non-standard path
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| `suspicious_msbuild_path_filter` +| `suspicious_msbuild_path_filter` ====Associated Analytic Story==== @@ -15995,7 +14144,7 @@ Some legitimate applications may use a moved copy of msbuild.exe, triggering a f ---- -==={{visible anchor|Suspicious mshta child process|suspicious-mshta-child-process}}=== +===Suspicious mshta child process=== The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16007,12 +14156,12 @@ The following analytic identifies child processes spawning from "mshta.exe". Th
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `suspicious_mshta_child_process_filter` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_mshta_child_process_filter` ====Associated Analytic Story==== @@ -16062,7 +14211,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg ---- -==={{visible anchor|Suspicious mshta spawn|suspicious-mshta-spawn}}=== +===Suspicious mshta spawn=== The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16074,12 +14223,12 @@ The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `suspicious_mshta_spawn_filter` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_mshta_spawn_filter` ====Associated Analytic Story==== @@ -16131,7 +14280,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg ---- -==={{visible anchor|Suspicious wevtutil Usage|suspicious-wevtutil-usage}}=== +===Suspicious wevtutil Usage=== The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16143,12 +14292,12 @@ The wevtutil.exe application is the windows event log utility. This searches for
====Search==== - + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` -| `suspicious_wevtutil_usage_filter` +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `suspicious_wevtutil_usage_filter` ====Associated Analytic Story==== @@ -16196,11 +14345,11 @@ The wevtutil.exe application is a legitimate Windows event log utility. Administ ---- -==={{visible anchor|Suspicious writes to windows Recycle Bin|suspicious-writes-to-windows-recycle-bin}}=== +===Suspicious writes to windows Recycle Bin=== This search detects writes to the recycle bin by a process other than explorer.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1036/ T1036] * '''Last Updated''': 2020-07-22 @@ -16208,14 +14357,14 @@ This search detects writes to the recycle bin by a process other than explorer.e
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")` | search [ | tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest -| `drop_dm_object_name("Processes")` -| table process_id dest] -| `suspicious_writes_to_windows_recycle_bin_filter` +| `drop_dm_object_name("Processes")` +| table process_id dest] +| `suspicious_writes_to_windows_recycle_bin_filter` ====Associated Analytic Story==== @@ -16259,7 +14408,7 @@ Because the Recycle Bin is a hidden folder in modern versions of Windows, it wou ---- -==={{visible anchor|System Information Discovery Detection|system-information-discovery-detection}}=== +===System Information Discovery Detection=== Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16271,15 +14420,15 @@ Detect system information discovery techniques used by attackers to understand c
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| eventstats dc(process) as dc_processes_by_dest by dest -| where dc_processes_by_dest > 2 -| stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| eventstats dc(process) as dc_processes_by_dest by dest +| where dc_processes_by_dest > 2 +| stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `system_information_discovery_detection_filter` +| `security_content_ctime(lastTime)` +| `system_information_discovery_detection_filter` ====Associated Analytic Story==== @@ -16327,11 +14476,11 @@ Administrators debugging servers ---- -==={{visible anchor|System Process Running from Unexpected Location|system-process-running-from-unexpected-location}}=== +===System Process Running from Unexpected Location=== An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1036/ T1036] * '''Last Updated''': 2020-08-25 @@ -16339,37 +14488,37 @@ An attacker tries might try to use different version of a system command without
====Search==== - $ssa_input = -| from read_ssa_enriched_events() + $ssa_input = +| from read_ssa_enriched_events() | eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), user=ucast(map_get(input_event, "dest_user_id"), "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=lower(ucast(map_get(input_event, "process_path"), "string", null)); -$cond_1 = -| from $ssa_input +$cond_1 = +| from $ssa_input | where process_name="arp.exe" OR process_name="adaptertroubleshooter.exe" OR process_name="applicationframehost.exe" OR process_name="atbroker.exe" OR process_name="authhost.exe" OR process_name="autoworkplace.exe" OR process_name="axinstui.exe" OR process_name="backgroundtransferhost.exe" OR process_name="bdehdcfg.exe" OR process_name="bdeuisrv.exe" OR process_name="bdeunlockwizard.exe" OR process_name="bitlockerdeviceencryption.exe" OR process_name="bitlockerwizard.exe" OR process_name="bitlockerwizardelev.exe" OR process_name="bytecodegenerator.exe" OR process_name="camerasettingsuihost.exe" OR process_name="castsrv.exe" OR process_name="certenrollctrl.exe" OR process_name="checknetisolation.exe" OR process_name="clipup.exe" OR process_name="cloudexperiencehostbroker.exe" OR process_name="cloudnotifications.exe" OR process_name="cloudstoragewizard.exe" OR process_name="compmgmtlauncher.exe" OR process_name="compattelrunner.exe" OR process_name="computerdefaults.exe" OR process_name="credentialuibroker.exe" OR process_name="dfdwiz.exe" OR process_name="dwwin.exe" OR process_name="dataexchangehost.exe" OR process_name="defrag.exe" OR process_name="devicedisplayobjectprovider.exe" OR process_name="deviceeject.exe" OR process_name="deviceenroller.exe" OR process_name="devicepairingwizard.exe" OR process_name="deviceproperties.exe" OR process_name="disksnapshot.exe" OR process_name="dism.exe" OR process_name="displayswitch.exe" OR process_name="dmnotificationbroker.exe" OR process_name="dmomacpmo.exe" OR process_name="dpiscaling.exe" OR process_name="dsmusertask.exe" OR process_name="dxpserver.exe" OR process_name="edpcleanup.exe" OR process_name="eosnotify.exe" OR process_name="eap3host.exe" OR process_name="easpoliciesbrokerhost.exe" OR process_name="easeofaccessdialog.exe" OR process_name="ehstorauthn.exe" OR process_name="fxscover.exe" OR process_name="fxssvc.exe" OR process_name="fxsunatd.exe" OR process_name="filehistory.exe" OR process_name="fondue.exe" OR process_name="gamepanel.exe" OR process_name="genvalobj.exe" OR process_name="gettingstarted.exe" OR process_name="hostname.exe" OR process_name="icsentitlementhost.exe" OR process_name="infdefaultinstall.exe" OR process_name="installagent.exe" OR process_name="languagecomponentsinstallercomhandler.exe" OR process_name="launchtm.exe" OR process_name="launchwinapp.exe" OR process_name="legacynetuxhost.exe" OR process_name="licensemanagershellext.exe" OR process_name="licensingui.exe" OR process_name="locationnotificationwindows.exe" OR process_name="locationnotifications.exe" OR process_name="locator.exe" OR process_name="lockapphost.exe" OR process_name="lockscreencontentserver.exe" OR process_name="logonui.exe" OR process_name="lsaiso.exe" OR process_name="mdeserver.exe" OR process_name="mdmagent.exe" OR process_name="mdmappinstaller.exe" OR process_name="mrinfo.exe" OR process_name="mrt.exe" OR process_name="mschedexe.exe" OR process_name="magnify.exe" OR process_name="mbaeparsertask.exe" OR process_name="mdres.exe" OR process_name="mdsched.exe" OR process_name="migautoplay.exe" OR process_name="mpsigstub.exe" OR process_name="msspellcheckinghost.exe" OR process_name="muiunattend.exe" OR process_name="multidigimon.exe" OR process_name="musnotification.exe" OR process_name="musnotificationux.exe" OR process_name="napstat.exe" OR process_name="netstat.exe" OR process_name="narrator.exe" OR process_name="netcfgnotifyobjecthost.exe" OR process_name="netevtfwdr.exe" OR process_name="netproj.exe" OR process_name="netplwiz.exe" OR process_name="networkuxbroker.exe"; -$cond_2 = -| from $ssa_input +$cond_2 = +| from $ssa_input | where process_name="openwith.exe" OR process_name="optionalfeatures.exe" OR process_name="pathping.exe" OR process_name="ping.exe" OR process_name="passwordonwakesettingflyout.exe" OR process_name="pickerhost.exe" OR process_name="pkgmgr.exe" OR process_name="pnpunattend.exe" OR process_name="pnputil.exe" OR process_name="presentationhost.exe" OR process_name="presentationsettings.exe" OR process_name="printbrmui.exe" OR process_name="printdialoghost.exe" OR process_name="printdialoghost3d.exe" OR process_name="printisolationhost.exe" OR process_name="proximityuxhost.exe" OR process_name="rdspnf.exe" OR process_name="rmactivate.exe" OR process_name="rmactivate_isv.exe" OR process_name="rmactivate_ssp.exe" OR process_name="rmactivate_ssp_isv.exe" OR process_name="route.exe" OR process_name="rdpsa.exe" OR process_name="rdpsaproxy.exe" OR process_name="rdpsauachelper.exe" OR process_name="reagentc.exe" OR process_name="recoverydrive.exe" OR process_name="register-cimprovider.exe" OR process_name="registeriepkeys.exe" OR process_name="relpost.exe" OR process_name="remoteposworker.exe" OR process_name="rmclient.exe" OR process_name="robocopy.exe" OR process_name="rpcping.exe" OR process_name="runlegacycplelevated.exe" OR process_name="runtimebroker.exe" OR process_name="sihclient.exe" OR process_name="searchfilterhost.exe" OR process_name="searchindexer.exe" OR process_name="searchprotocolhost.exe" OR process_name="secedit.exe" OR process_name="sensordataservice.exe" OR process_name="setieinstalleddate.exe" OR process_name="settingsynchost.exe" OR process_name="slidetoshutdown.exe" OR process_name="smartscreensettings.exe" OR process_name="sndvol.exe" OR process_name="snippingtool.exe" OR process_name="soundrecorder.exe" OR process_name="spaceagent.exe" OR process_name="sppextcomobj.exe" OR process_name="srtasks.exe" OR process_name="stikynot.exe" OR process_name="synchost.exe" OR process_name="sysreseterr.exe" OR process_name="systempropertiesadvanced.exe" OR process_name="systempropertiescomputername.exe" OR process_name="systempropertiesdataexecutionprevention.exe" OR process_name="systempropertieshardware.exe" OR process_name="systempropertiesperformance.exe" OR process_name="systempropertiesprotection.exe" OR process_name="systempropertiesremote.exe" OR process_name="systemsettingsadminflows.exe" OR process_name="systemsettingsbroker.exe" OR process_name="systemsettingsremovedevice.exe" OR process_name="tcpsvcs.exe" OR process_name="tracert.exe" OR process_name="tstheme.exe" OR process_name="tswbprxy.exe" OR process_name="tapiunattend.exe" OR process_name="taskmgr.exe" OR process_name="thumbnailextractionhost.exe" OR process_name="tokenbrokercookies.exe" OR process_name="tpminit.exe" OR process_name="tswpfwrp.exe" OR process_name="ui0detect.exe" OR process_name="upgraderesultsui.exe" OR process_name="useraccountbroker.exe" OR process_name="useraccountcontrolsettings.exe" OR process_name="usoclient.exe" OR process_name="utilman.exe" OR process_name="vssvc.exe" OR process_name="vaultcmd.exe" OR process_name="vaultsysui.exe" OR process_name="wfs.exe" OR process_name="wmpdmc.exe" OR process_name="wpdshextautoplay.exe" OR process_name="wscollect.exe" OR process_name="wsmanhttpconfig.exe" OR process_name="wsreset.exe" OR process_name="wudfhost.exe" OR process_name="wwahost.exe" OR process_name="wallpaperhost.exe" OR process_name="webcache.exe" OR process_name="werfault.exe" OR process_name="werfaultsecure.exe" OR process_name="winsat.exe" OR process_name="windows.media.backgroundplayback.exe" OR process_name="windowsactiondialog.exe" OR process_name="windowsanytimeupgrade.exe" OR process_name="windowsanytimeupgraderesults.exe"; -$cond_3 = -| from $ssa_input +$cond_3 = +| from $ssa_input | where process_name="windowsanytimeupgradeui.exe" OR process_name="windowsupdateelevatedinstaller.exe" OR process_name="workfolders.exe" OR process_name="wpcmon.exe" OR process_name="acu.exe" OR process_name="aitagent.exe" OR process_name="aitstatic.exe" OR process_name="alg.exe" OR process_name="appidcertstorecheck.exe" OR process_name="appidpolicyconverter.exe" OR process_name="at.exe" OR process_name="attrib.exe" OR process_name="audiodg.exe" OR process_name="auditpol.exe" OR process_name="autochk.exe" OR process_name="autoconv.exe" OR process_name="autofmt.exe" OR process_name="baaupdate.exe" OR process_name="backgroundtaskhost.exe" OR process_name="bcastdvr.exe" OR process_name="bcdboot.exe" OR process_name="bcdedit.exe" OR process_name="bdechangepin.exe" OR process_name="bdeunlock.exe" OR process_name="bitsadmin.exe" OR process_name="bootcfg.exe" OR process_name="bootim.exe" OR process_name="bootsect.exe" OR process_name="bridgeunattend.exe" OR process_name="browser_broker.exe" OR process_name="bthudtask.exe" OR process_name="cacls.exe" OR process_name="calc.exe" OR process_name="cdpreference.exe" OR process_name="certreq.exe" OR process_name="certutil.exe" OR process_name="change.exe" OR process_name="changepk.exe" OR process_name="charmap.exe" OR process_name="chglogon.exe" OR process_name="chgport.exe" OR process_name="chgusr.exe" OR process_name="chkdsk.exe" OR process_name="chkntfs.exe" OR process_name="choice.exe" OR process_name="cipher.exe" OR process_name="cleanmgr.exe" OR process_name="cliconfg.exe" OR process_name="clip.exe" OR process_name="cmd.exe" OR process_name="cmdkey.exe" OR process_name="cmdl32.exe" OR process_name="cmmon32.exe" OR process_name="cmstp.exe" OR process_name="cofire.exe" OR process_name="colorcpl.exe" OR process_name="comp.exe" OR process_name="compact.exe" OR process_name="conhost.exe" OR process_name="consent.exe" OR process_name="control.exe" OR process_name="convert.exe" OR process_name="credwiz.exe" OR process_name="cscript.exe" OR process_name="csrss.exe" OR process_name="ctfmon.exe" OR process_name="cttune.exe" OR process_name="cttunesvr.exe" OR process_name="dashost.exe" OR process_name="dccw.exe" OR process_name="dcomcnfg.exe" OR process_name="ddodiag.exe" OR process_name="dfrgui.exe" OR process_name="dialer.exe" OR process_name="diantz.exe" OR process_name="dinotify.exe" OR process_name="diskpart.exe" OR process_name="diskperf.exe" OR process_name="diskraid.exe" OR process_name="dispdiag.exe" OR process_name="djoin.exe" OR process_name="dllhost.exe" OR process_name="dllhst3g.exe" OR process_name="dmcertinst.exe" OR process_name="dmcfghost.exe" OR process_name="dmclient.exe" OR process_name="dnscacheugc.exe" OR process_name="doskey.exe" OR process_name="dpapimig.exe" OR process_name="dpnsvr.exe" OR process_name="driverquery.exe" OR process_name="drvcfg.exe" OR process_name="drvinst.exe" OR process_name="dsregcmd.exe" OR process_name="dstokenclean.exe" OR process_name="dvdplay.exe" OR process_name="dvdupgrd.exe" OR process_name="dwm.exe" OR process_name="dxdiag.exe" OR process_name="easinvoker.exe" OR process_name="efsui.exe"; -$cond_4 = -| from $ssa_input +$cond_4 = +| from $ssa_input | where process_name="embeddedapplauncher.exe" OR process_name="esentutl.exe" OR process_name="eudcedit.exe" OR process_name="eventcreate.exe" OR process_name="eventvwr.exe" OR process_name="expand.exe" OR process_name="extrac32.exe" OR process_name="fc.exe" OR process_name="fhmanagew.exe" OR process_name="find.exe" OR process_name="findstr.exe" OR process_name="finger.exe" OR process_name="fixmapi.exe" OR process_name="fltmc.exe" OR process_name="fodhelper.exe" OR process_name="fontdrvhost.exe" OR process_name="fontview.exe" OR process_name="forfiles.exe" OR process_name="fsavailux.exe" OR process_name="fsquirt.exe" OR process_name="fsutil.exe" OR process_name="ftp.exe" OR process_name="fvenotify.exe" OR process_name="fveprompt.exe" OR process_name="getmac.exe" OR process_name="gpresult.exe" OR process_name="gpscript.exe" OR process_name="gpupdate.exe" OR process_name="grpconv.exe" OR process_name="hdwwiz.exe" OR process_name="help.exe" OR process_name="hwrcomp.exe" OR process_name="hwrreg.exe" OR process_name="icacls.exe" OR process_name="icardagt.exe" OR process_name="icsunattend.exe" OR process_name="ie4uinit.exe" OR process_name="ieunatt.exe" OR process_name="ieetwcollector.exe" OR process_name="iexpress.exe" OR process_name="immersivetpmvscmgrsvr.exe" OR process_name="ipconfig.exe" OR process_name="irftp.exe" OR process_name="iscsicli.exe" OR process_name="iscsicpl.exe" OR process_name="isoburn.exe" OR process_name="klist.exe" OR process_name="ksetup.exe" OR process_name="ktmutil.exe" OR process_name="label.exe" OR process_name="licensingdiag.exe" OR process_name="lodctr.exe" OR process_name="logagent.exe" OR process_name="logman.exe" OR process_name="logoff.exe" OR process_name="lpkinstall.exe" OR process_name="lpksetup.exe" OR process_name="lpremove.exe" OR process_name="lsass.exe" OR process_name="lsm.exe" OR process_name="makecab.exe" OR process_name="manage-bde.exe" OR process_name="mblctr.exe" OR process_name="mcbuilder.exe" OR process_name="mctadmin.exe" OR process_name="mfpmp.exe" OR process_name="mmc.exe" OR process_name="mobsync.exe" OR process_name="mountvol.exe" OR process_name="mpnotify.exe" OR process_name="msconfig.exe" OR process_name="msdt.exe" OR process_name="msdtc.exe" OR process_name="msfeedssync.exe" OR process_name="msg.exe" OR process_name="mshta.exe" OR process_name="msiexec.exe" OR process_name="msinfo32.exe" OR process_name="mspaint.exe" OR process_name="msra.exe" OR process_name="mstsc.exe" OR process_name="mtstocom.exe" OR process_name="nbtstat.exe" OR process_name="ndadmin.exe" OR process_name="net.exe" OR process_name="net1.exe" OR process_name="netbtugc.exe" OR process_name="netcfg.exe" OR process_name="netiougc.exe" OR process_name="netsh.exe" OR process_name="newdev.exe" OR process_name="nltest.exe" OR process_name="notepad.exe" OR process_name="nslookup.exe" OR process_name="ntoskrnl.exe" OR process_name="ntprint.exe" OR process_name="ocsetup.exe" OR process_name="odbcad32.exe" OR process_name="odbcconf.exe" OR process_name="omadmclient.exe" OR process_name="omadmprc.exe"; -$cond_5 = -| from $ssa_input +$cond_5 = +| from $ssa_input | where process_name="openfiles.exe" OR process_name="osk.exe" OR process_name="p2phost.exe" OR process_name="pcalua.exe" OR process_name="pcaui.exe" OR process_name="pcawrk.exe" OR process_name="pcwrun.exe" OR process_name="perfmon.exe" OR process_name="phoneactivate.exe" OR process_name="plasrv.exe" OR process_name="poqexec.exe" OR process_name="powercfg.exe" OR process_name="prevhost.exe" OR process_name="print.exe" OR process_name="printfilterpipelinesvc.exe" OR process_name="printui.exe" OR process_name="proquota.exe" OR process_name="provtool.exe" OR process_name="psr.exe" OR process_name="pwlauncher.exe" OR process_name="qappsrv.exe" OR process_name="qprocess.exe" OR process_name="query.exe" OR process_name="quser.exe" OR process_name="qwinsta.exe" OR process_name="rasautou.exe" OR process_name="rasdial.exe" OR process_name="raserver.exe" OR process_name="rasphone.exe" OR process_name="rdpclip.exe" OR process_name="rdpinput.exe" OR process_name="rdrleakdiag.exe" OR process_name="recdisc.exe" OR process_name="recover.exe" OR process_name="reg.exe" OR process_name="regedt32.exe" OR process_name="regini.exe" OR process_name="regsvr32.exe" OR process_name="rekeywiz.exe" OR process_name="relog.exe" OR process_name="repair-bde.exe" OR process_name="replace.exe" OR process_name="reset.exe" OR process_name="resmon.exe" OR process_name="rmttpmvscmgrsvr.exe" OR process_name="rrinstaller.exe" OR process_name="rstrui.exe" OR process_name="runas.exe" OR process_name="rundll32.exe" OR process_name="runonce.exe" OR process_name="rwinsta.exe" OR process_name="sbunattend.exe" OR process_name="sc.exe" OR process_name="schtasks.exe" OR process_name="sdbinst.exe" OR process_name="sdchange.exe" OR process_name="sdclt.exe" OR process_name="sdiagnhost.exe" OR process_name="secinit.exe" OR process_name="services.exe" OR process_name="sessionmsg.exe" OR process_name="sethc.exe" OR process_name="setspn.exe" OR process_name="setupcl.exe" OR process_name="setupugc.exe" OR process_name="setx.exe" OR process_name="sfc.exe" OR process_name="shadow.exe" OR process_name="shrpubw.exe" OR process_name="shutdown.exe" OR process_name="sigverif.exe" OR process_name="sihost.exe" OR process_name="slui.exe" OR process_name="smss.exe" OR process_name="snmptrap.exe" OR process_name="sort.exe" OR process_name="spinstall.exe" OR process_name="spoolsv.exe" OR process_name="sppsvc.exe" OR process_name="spreview.exe" OR process_name="srdelayed.exe" OR process_name="subst.exe" OR process_name="svchost.exe" OR process_name="sxstrace.exe" OR process_name="syskey.exe" OR process_name="systeminfo.exe" OR process_name="systemreset.exe" OR process_name="systray.exe" OR process_name="tabcal.exe" OR process_name="takeown.exe" OR process_name="taskeng.exe" OR process_name="taskhost.exe" OR process_name="taskhostw.exe" OR process_name="taskkill.exe" OR process_name="tasklist.exe" OR process_name="taskmgr.exe" OR process_name="tcmsetup.exe" OR process_name="timeout.exe" OR process_name="tpmvscmgr.exe" OR process_name="tpmvscmgrsvr.exe"; -$cond_6 = -| from $ssa_input +$cond_6 = +| from $ssa_input | where process_name="tracerpt.exe" OR process_name="tscon.exe" OR process_name="tsdiscon.exe" OR process_name="tskill.exe" OR process_name="typeperf.exe" OR process_name="tzsync.exe" OR process_name="tzutil.exe" OR process_name="ucsvc.exe" OR process_name="unlodctr.exe" OR process_name="unregmp2.exe" OR process_name="upnpcont.exe" OR process_name="userinit.exe" OR process_name="vds.exe" OR process_name="vdsldr.exe" OR process_name="verclsid.exe" OR process_name="verifier.exe" OR process_name="verifiergui.exe" OR process_name="vmicsvc.exe" OR process_name="vssadmin.exe" OR process_name="w32tm.exe" OR process_name="waitfor.exe" OR process_name="wbadmin.exe" OR process_name="wbengine.exe" OR process_name="wecutil.exe" OR process_name="wermgr.exe" OR process_name="wevtutil.exe" OR process_name="wextract.exe" OR process_name="where.exe" OR process_name="whoami.exe" OR process_name="wiaacmgr.exe" OR process_name="wiawow64.exe" OR process_name="wifitask.exe" OR process_name="wimserv.exe" OR process_name="wininit.exe" OR process_name="winload.exe" OR process_name="winlogon.exe" OR process_name="winresume.exe" OR process_name="winrs.exe" OR process_name="winrshost.exe" OR process_name="winver.exe" OR process_name="wisptis.exe" OR process_name="wkspbroker.exe" OR process_name="wksprt.exe" OR process_name="wlanext.exe" OR process_name="wlrmdr.exe" OR process_name="wowreg32.exe" OR process_name="wpnpinst.exe" OR process_name="wpr.exe" OR process_name="write.exe" OR process_name="wscript.exe" OR process_name="wsmprovhost.exe" OR process_name="wsqmcons.exe" OR process_name="wuapihost.exe" OR process_name="wuapp.exe" OR process_name="wuauclt.exe" OR process_name="wusa.exe" OR process_name="xcopy.exe" OR process_name="xpsrchvw.exe" OR process_name="xwizard.exe"; -| from $cond_1 -| union $cond_2 -| union $cond_3 -| union $cond_4 -| union $cond_5 -| union $cond_6 -| where process_path!="c:\\windows\\system32" AND process_path!="c:\\windows\\syswow64" -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(device, user), body = "TBD" -| into write_ssa_detected_events(); +| from $cond_1 +| union $cond_2 +| union $cond_3 +| union $cond_4 +| union $cond_5 +| union $cond_6 +| where process_path!="c:\\windows\\system32" AND process_path!="c:\\windows\\syswow64" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(device, user), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -16421,11 +14570,11 @@ None ---- -==={{visible anchor|System Processes Run From Unexpected Locations|system-processes-run-from-unexpected-locations}}=== +===System Processes Run From Unexpected Locations=== This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2020-12-08 @@ -16433,13 +14582,13 @@ This search looks for system processes that normally run out of C:\Windows\Syste
====Search==== - + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name Processes.process_hash -| `drop_dm_object_name("Processes")` +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| `is_windows_system_file` -| `system_processes_run_from_unexpected_locations_filter` +| `is_windows_system_file` +| `system_processes_run_from_unexpected_locations_filter` ====Associated Analytic Story==== @@ -16489,74 +14638,7 @@ None identified ---- -==={{visible anchor|TOR Traffic|tor-traffic}}=== -This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.001/ T1071.001] -* '''Last Updated''': 2020-07-22 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name("All_Traffic")` -| `tor_traffic_filter` - -====Associated Analytic Story==== - -* Prohibited Traffic Allowed or Protocol Mismatch - -* Ransomware - -* Command and Control - -* Sunburst Malware - - -====How To Implement==== -In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1071.001 -| Web Protocols -| Command and Control -|} - -====Kill Chain Phase==== - -* Command and Control - - -====Known False Positives==== -None at this time - -====Reference==== - - -====Test Dataset==== - - -''version'': 2 -
-
- ----- - -==={{visible anchor|USN Journal Deletion|usn-journal-deletion}}=== +===USN Journal Deletion=== The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16568,13 +14650,13 @@ The fsutil.exe application is a legitimate Windows utility used to perform tasks
====Search==== - -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| search process="*deletejournal*" AND process="*usn*" -| `usn_journal_deletion_filter` +| `security_content_ctime(lastTime)` +| search process="*deletejournal*" AND process="*usn*" +| `usn_journal_deletion_filter` ====Associated Analytic Story==== @@ -16622,7 +14704,7 @@ None identified ---- -==={{visible anchor|Unload Sysmon Filter Driver|unload-sysmon-filter-driver}}=== +===Unload Sysmon Filter Driver=== Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16634,13 +14716,13 @@ Attackers often disable security tools to avoid detection. This search looks for
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user -| `drop_dm_object_name("Processes")` + +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +|`security_content_ctime(lastTime)` |`unload_sysmon_filter_driver_filter` -| table firstTime lastTime dest user count process_name process_id parent_process_name process +| table firstTime lastTime dest user count process_name process_id parent_process_name process ====Associated Analytic Story==== @@ -16686,32 +14768,32 @@ You must be ingesting data that records process activity from your hosts to popu ---- -==={{visible anchor|Unusually Long Command Line|unusually-long-command-line}}=== +===Unusually Long Command Line=== Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2020-10-06
====Search==== - -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) -| eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) -| where cmd_line!=null and dest_user_id!=null + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) +| where cmd_line!=null and dest_user_id!=null | eval cmd_line_norm=replace(cast(cmd_line, "string"), /\s(--?\w+) -|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) -| select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input -| adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 -| where label AND quantile>0.99 -| first_time_event input_columns=["dest_device_id", "cmd_line"] -| where first_time_dest_device_id_cmd_line -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" -| into write_ssa_detected_events(); +|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) +| select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input +| adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 +| where label AND quantile>0.99 +| first_time_event input_columns=["dest_device_id", "cmd_line"] +| where first_time_dest_device_id_cmd_line +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -16759,29 +14841,29 @@ This detection may flag suspiciously long command lines when there is not suffic ---- -==={{visible anchor|Unusually Long Command Line|unusually-long-command-line}}=== +===Unusually Long Command Line=== Command lines that are extremely long may be indicative of malicious activity on your hosts. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2020-12-08
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process -| `drop_dm_object_name("Processes")` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| eval processlen=len(process) -| eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest -| stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process -| `unusually_long_command_line_filter` -|eval threshold = 3 -| where maxlen > ((threshold*stdevperhost) + avgperhost) +| eval processlen=len(process) +| eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest +| stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process +| `unusually_long_command_line_filter` +|eval threshold = 3 +| where maxlen > ((threshold*stdevperhost) + avgperhost) ====Associated Analytic Story==== @@ -16829,30 +14911,30 @@ Some legitimate applications start with long command lines. ---- -==={{visible anchor|Unusually Long Command Line - MLTK|unusually-long-command-line---mltk}}=== +===Unusually Long Command Line - MLTK=== Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2019-05-08
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process -| `drop_dm_object_name(Processes)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| eval processlen=len(process) -| search user!=unknown -| apply cmdline_pdfmodel threshold=0.01 -| rename "IsOutlier(processlen)" as isOutlier -| search isOutlier > 0 -| table firstTime lastTime user dest process_name process processlen count -| `unusually_long_command_line___mltk_filter` +| eval processlen=len(process) +| search user!=unknown +| apply cmdline_pdfmodel threshold=0.01 +| rename "IsOutlier(processlen)" as isOutlier +| search isOutlier > 0 +| table firstTime lastTime user dest process_name process processlen count +| `unusually_long_command_line___mltk_filter` ====Associated Analytic Story==== @@ -16898,63 +14980,7 @@ Some legitimate applications use long command lines for installs or updates. You ---- -==={{visible anchor|Unusually Long Content-Type Length|unusually-long-content-type-length}}=== -This search looks for unusually long strings in the Content-Type http header that the client sends the server. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': -* '''Last Updated''': 2017-10-13 - -
-
- -====Search==== -`stream_http` -| eval cs_content_type_length = len(cs_content_type) -| where cs_content_type_length > 100 -| table endtime src_ip dest_ip cs_content_type_length cs_content_type url -| `unusually_long_content_type_length_filter` - -====Associated Analytic Story==== - -* Apache Struts Vulnerability - - -====How To Implement==== -This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} - -====Kill Chain Phase==== - -* Delivery - - -====Known False Positives==== -Very few legitimate Content-Type fields will have a length greater than 100 characters. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|WBAdmin Delete System Backups|wbadmin-delete-system-backups}}=== +===WBAdmin Delete System Backups=== This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16966,12 +14992,12 @@ This search looks for flags passed to wbadmin.exe (Windows Backup Administrator
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| `wbadmin_delete_system_backups_filter` +| `wbadmin_delete_system_backups_filter` ====Associated Analytic Story==== @@ -17027,11 +15053,11 @@ Administrators may modify the boot configuration. ---- -==={{visible anchor|WMI Permanent Event Subscription - Sysmon|wmi-permanent-event-subscription---sysmon}}=== +===WMI Permanent Event Subscription - Sysmon=== This search looks for the creation of WMI permanent event subscriptions. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.003/ T1546.003] * '''Last Updated''': 2020-12-08 @@ -17039,10 +15065,10 @@ This search looks for the creation of WMI permanent event subscriptions.
====Search==== -`sysmon` EventCode=21 -| rename host as dest -| table _time, dest, user, Operation, EventType, Query, Consumer, Filter -| `wmi_permanent_event_subscription___sysmon_filter` +`sysmon` EventCode=21 +| rename host as dest +| table _time, dest, user, Operation, EventType, Query, Consumer, Filter +| `wmi_permanent_event_subscription___sysmon_filter` ====Associated Analytic Story==== @@ -17088,11 +15114,1687 @@ Although unlikely, administrators may use event subscriptions for legitimate pur ---- -==={{visible anchor|Web Fraud - Account Harvesting|web-fraud---account-harvesting}}=== +===Windows AdFind Exe=== +This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1018/ T1018] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `windows_adfind_exe_filter` + +====Associated Analytic Story==== + +* Sunburst Malware + + +====How To Implement==== +To successfully implement this search, you need to be ingesting logs with the process name, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1018 +| Remote System Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +administrators rarely use adfind, usually not used for legitimate reasons + +====Reference==== + +* https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/ + +* https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + +===Windows Event Log Cleared=== +This search looks for Windows events that indicate one of the Windows event logs has been purged. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1070.001/ T1070.001] +* '''Last Updated''': 2020-07-06 + +
+
+ +====Search==== +(`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) +| stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `windows_event_log_cleared_filter` + +====Associated Analytic Story==== + +* Windows Log Manipulation + +* Ransomware + + +====How To Implement==== +To successfully implement this search, you need to be ingesting Windows event logs from your hosts. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070.001 +| Clear Windows Event Logs +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It is possible that these logs may be legitimately cleared by Administrators. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-security.log + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-system.log + + +''version'': 4 +
+
+ +---- + +===Windows Security Account Manager Stopped=== +The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1489/ T1489] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" +| `drop_dm_object_name(Processes)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `windows_security_account_manager_stopped_filter` + +====Associated Analytic Story==== + +* Ryuk Ransomware + + +====How To Implement==== +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1489 +| Service Stop +| Impact +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log + + +''version'': 1 +
+
+ +---- + + + +==Network== + + +===DNS Query Length Outliers - MLTK=== +This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.004/ T1071.004] +* '''Last Updated''': 2020-01-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type +| search DNS.record_type=* +| `drop_dm_object_name(DNS)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval query_length = len(query) +| apply dns_query_pdfmodel threshold=0.01 +| rename "IsOutlier(query_length)" as isOutlier +| search isOutlier > 0 +| sort -query_length +| table start_time end_time query record_type count src dest query_length +| `dns_query_length_outliers___mltk_filter` + +====Associated Analytic Story==== + +* Hidden Cobra Malware + +* Suspicious DNS Traffic + +* Command and Control + + +====How To Implement==== +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +1. \ +1. **Label:** DNS Query Length, **Field:** query_length\ +1. \ +1. **Label:** Number of events, **Field:** count\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.004 +| DNS +| Command and Control +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===DNS Query Length With High Standard Deviation=== +This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003] +* '''Last Updated''': 2021-01-18 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query +| `drop_dm_object_name("DNS")` +| eval query_length = len(query) +| table query query_length record_type count +| eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50 +| where query_length>(avg+stdev*2) +| eval z_score=(query_length-avg)/stdev +| `dns_query_length_with_high_standard_deviation_filter` + +====Associated Analytic Story==== + +* Hidden Cobra Malware + +* Suspicious DNS Traffic + +* Command and Control + + +====How To Implement==== +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +It's possible there can be long domain names that are legitimate. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +===Detect IPv6 Network Infrastructure Threats=== +By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557.002/ T1557.002] +* '''Last Updated''': 2020-10-28 + +
+
+ +====Search==== +`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") +| eval src_interface=src_int_prefix_long+src_int_suffix +| eval dest_interface=dest_int_prefix_long+dest_int_suffix +| stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface +| table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_ipv6_network_infrastructure_threats_filter` + +====Associated Analytic Story==== + +* Router and Infrastructure Security + + +====How To Implement==== +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1200 +| Hardware Additions +| Initial Access +|- +| T1498 +| Network Denial of Service +| Impact +|- +| T1557.002 +| ARP Cache Poisoning +| Collection, Credential Access +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Delivery + +* Actions on Objectives + + +====Known False Positives==== +None currently known + +====Reference==== + +* https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html + +* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Large Outbound ICMP Packets=== +This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1095/ T1095] +* '''Last Updated''': 2018-06-01 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip +| `drop_dm_object_name("All_Traffic")` +| search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_large_outbound_icmp_packets_filter` + +====Associated Analytic Story==== + +* Command and Control + + +====How To Implement==== +In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1095 +| Non-Application Layer Protocol +| Command and Control +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Detect Outbound SMB Traffic=== +This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.002/ T1071.002] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip +| `drop_dm_object_name("All_Traffic")` +| `security_content_ctime(start_time)` +| `security_content_ctime(end_time)` +| `detect_outbound_smb_traffic_filter` + +====Associated Analytic Story==== + +* Hidden Cobra Malware + +* DHS Report TA18-074A + +* Sunburst Malware + + +====How To Implement==== +In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.002 +| File Transfer Protocols +| Command and Control +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Known False Positives==== +It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Detect Port Security Violation=== +By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557.002/ T1557.002] +* '''Last Updated''': 2020-10-28 + +
+
+ +====Search==== +`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") +| eval src_interface=src_int_prefix_long+src_int_suffix +| stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_port_security_violation_filter` + +====Associated Analytic Story==== + +* Router and Infrastructure Security + + +====How To Implement==== +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1200 +| Hardware Additions +| Initial Access +|- +| T1498 +| Network Denial of Service +| Impact +|- +| T1557.002 +| ARP Cache Poisoning +| Collection, Credential Access +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Delivery + +* Exploitation + +* Actions on Objectives + + +====Known False Positives==== +This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Rogue DHCP Server=== +By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557/ T1557] +* '''Last Updated''': 2020-08-11 + +
+
+ +====Search==== +`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" +| stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_rogue_dhcp_server_filter` + +====Associated Analytic Story==== + +* Router and Infrastructure Security + + +====How To Implement==== +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1200 +| Hardware Additions +| Initial Access +|- +| T1498 +| Network Denial of Service +| Impact +|- +| T1557 +| Man-in-the-Middle +| Collection, Credential Access +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Delivery + +* Actions on Objectives + + +====Known False Positives==== +This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect SNICat SNI Exfiltration=== +This search looks for commands that the SNICat tool uses in the TLS SNI field. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1041/ T1041] +* '''Last Updated''': 2020-10-21 + +
+
+ +====Search==== +`zeek_ssl` +| rex field=server_name "(?(LIST +|LS +|SIZE +|LD +|CB +|CD +|EX +|ALIVE +|EXIT +|WHERE +|finito)-[A-Za-z0-9]{16}\.)" +| stats count by src_ip dest_ip server_name snicat +| where count>0 +| table src_ip dest_ip server_name snicat +| `detect_snicat_sni_exfiltration_filter` + +====Associated Analytic Story==== + +* Data Exfiltration + + +====How To Implement==== +You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1041 +| Exfiltration Over C2 Channel +| Exfiltration +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Unknown + +====Reference==== + +* https://www.mnemonic.no/blog/introducing-snicat/ + +* https://github.com/mnemonic-no/SNIcat + +* https://attack.mitre.org/techniques/T1041/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Software Download To Network Device=== +Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1542.005/ T1542.005] +* '''Last Updated''': 2020-10-28 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| `drop_dm_object_name("All_Traffic")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_software_download_to_network_device_filter` + +====Associated Analytic Story==== + +* Router and Infrastructure Security + + +====How To Implement==== +This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1542.005 +| TFTP Boot +| Defense Evasion, Persistence +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Traffic Mirroring=== +Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1020.001/ T1020.001] +* '''Last Updated''': 2020-10-28 + +
+
+ +====Search==== +`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") +| stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_traffic_mirroring_filter` + +====Associated Analytic Story==== + +* Router and Infrastructure Security + + +====How To Implement==== +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1200 +| Hardware Additions +| Initial Access +|- +| T1498 +| Network Denial of Service +| Impact +|- +| T1020.001 +| Traffic Duplication +| Exfiltration +|} + +====Kill Chain Phase==== + +* Delivery + +* Actions on Objectives + + +====Known False Positives==== +This search will return false positives for any legitimate traffic captures by network administrators. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Unauthorized Assets by MAC address=== +By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Sessions +* '''ATT&CK''': +* '''Last Updated''': 2017-09-13 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac +| dedup All_Sessions.dest_mac +| `drop_dm_object_name("Network_Sessions")` +|`drop_dm_object_name("All_Sessions")` +| search NOT [ +| inputlookup asset_lookup_by_str +|rename mac as dest_mac +| fields + dest_mac] +| `detect_unauthorized_assets_by_mac_address_filter` + +====Associated Analytic Story==== + +* Asset Tracking + + +====How To Implement==== +This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Delivery + +* Actions on Objectives + + +====Known False Positives==== +This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Windows DNS SIGRed via Splunk Stream=== +This search detects SIGRed via Splunk Stream. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203] +* '''Last Updated''': 2020-07-28 + +
+
+ +====Search==== +`stream_dns` +| spath "query_type{}" +| search "query_type{}" IN (SIG,KEY) +| spath protocol_stack +| search protocol_stack="ip:tcp:dns" +| append [search `stream_tcp` bytes_out>65000] +| `detect_windows_dns_sigred_via_splunk_stream_filter` +| stats count by flow_id +| where count>1 +| fields - count + +====Associated Analytic Story==== + +* Windows DNS SIGRed CVE-2020-1350 + + +====How To Implement==== +You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1203 +| Exploitation for Client Execution +| Execution +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +unknown + +====Reference==== + +* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Windows DNS SIGRed via Zeek=== +This search detects SIGRed via Zeek DNS and Zeek Conn data. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203] +* '''Last Updated''': 2020-07-28 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id +| rename DNS.flow_id as flow_id +| append [ +| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id +| rename All_Traffic.flow_id as flow_id] +| `detect_windows_dns_sigred_via_zeek_filter` +| stats count by flow_id +| where count>1 +| fields - count + +====Associated Analytic Story==== + +* Windows DNS SIGRed CVE-2020-1350 + + +====How To Implement==== +You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The Network Resolution and Network Traffic datamodels are in use for this search. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1203 +| Exploitation for Client Execution +| Execution +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +unknown + +====Reference==== + +* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Zerologon via Zeek=== +This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1190/ T1190] +* '''Last Updated''': 2020-09-15 + +
+
+ +====Search==== +`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) +| bin span=5m _time +| stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip +| search opscount=3 authcount>4 passcount>0 +| search `detect_zerologon_via_zeek_filter` + +====Associated Analytic Story==== + +* Detect Zerologon Attack + + +====How To Implement==== +You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json. These three operations are then correlated on the Zeek UID field. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +unknown + +====Reference==== + +* https://www.secura.com/blog/zero-logon + +* https://github.com/SecuraBV/CVE-2020-1472 + +* https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472 + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect hosts connecting to dynamic domain providers=== +Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1189/ T1189] +* '''Last Updated''': 2021-01-14 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host +| `drop_dm_object_name("DNS")` +| `security_content_ctime(firstTime)` +| `dynamic_dns_providers` +| `detect_hosts_connecting_to_dynamic_domain_providers_filter` + +====Associated Analytic Story==== + +* Data Protection + +* Prohibited Traffic Allowed or Protocol Mismatch + +* DNS Hijacking + +* Suspicious DNS Traffic + +* Dynamic DNS + +* Command and Control + + +====How To Implement==== +First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ +This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +1. \ +1. **Label:** DNS Answer, **Field:** answer\ +1. \ +1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1189 +| Drive-by Compromise +| Initial Access +|} + +====Kill Chain Phase==== + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log + + +''version'': 3 +
+
+ +---- + +===Remote Desktop Network Bruteforce=== +This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 +| where count>(avg + stdev*2) +| rename All_Traffic.src AS src All_Traffic.dest AS dest +| table firstTime lastTime src dest count avg p50 stdev +| `remote_desktop_network_bruteforce_filter` + +====Associated Analytic Story==== + +* SamSam Ransomware + +* Ryuk Ransomware + + +====How To Implement==== +You must ensure that your network traffic data is populating the Network_Traffic data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Delivery + + +====Known False Positives==== +RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Remote Desktop Network Traffic=== +This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001] +* '''Last Updated''': 2020-07-07 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| `drop_dm_object_name("All_Traffic")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `remote_desktop_network_traffic_filter` + +====Associated Analytic Story==== + +* SamSam Ransomware + +* Ryuk Ransomware + +* Hidden Cobra Malware + +* Lateral Movement + + +====How To Implement==== +To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Remote Desktop may be used legitimately by users on the network. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===SMB Traffic Spike=== +This search looks for spikes in the number of Server Message Block (SMB) traffic connections. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src +| `drop_dm_object_name("All_Traffic")` +| eventstats max(_time) as maxtime +| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) +| where isOutlier=1 +| table src count +| `smb_traffic_spike_filter` + +====Associated Analytic Story==== + +* Emotet Malware DHS Report TA18-201A + +* Hidden Cobra Malware + +* Ransomware + +* DHS Report TA18-074A + + +====How To Implement==== +This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +A file server may experience high-demand loads that could cause this analytic to trigger. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===SMB Traffic Spike - MLTK=== +This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src +| eval HourOfDay=strftime(_time, "%H") +| eval DayOfWeek=strftime(_time, "%A") +| `drop_dm_object_name(All_Traffic)` +| apply smb_pdfmodel threshold=0.001 +| rename "IsOutlier(count)" as isOutlier +| search isOutlier > 0 +| sort -count +| table _time src dest port count +| `smb_traffic_spike___mltk_filter` + +====Associated Analytic Story==== + +* Emotet Malware DHS Report TA18-201A + +* Hidden Cobra Malware + +* Ransomware + +* DHS Report TA18-074A + + +====How To Implement==== +To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ +1. **Label:** Number of events, **Field:** count\ +Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===TOR Traffic=== +This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.001/ T1071.001] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `tor_traffic_filter` + +====Associated Analytic Story==== + +* Prohibited Traffic Allowed or Protocol Mismatch + +* Ransomware + +* Command and Control + +* Sunburst Malware + + +====How To Implement==== +In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.001 +| Web Protocols +| Command and Control +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +None at this time + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Unusually Long Content-Type Length=== +This search looks for unusually long strings in the Content-Type http header that the client sends the server. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2017-10-13 + +
+
+ +====Search==== +`stream_http` +| eval cs_content_type_length = len(cs_content_type) +| where cs_content_type_length > 100 +| table endtime src_ip dest_ip cs_content_type_length cs_content_type url +| `unusually_long_content_type_length_filter` + +====Associated Analytic Story==== + +* Apache Struts Vulnerability + + +====How To Implement==== +This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +Very few legitimate Content-Type fields will have a length greater than 100 characters. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + + + +==Web== + + +===Web Fraud - Account Harvesting=== This search is used to identify the creation of multiple user accounts using the same email domain name. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1136/ T1136] * '''Last Updated''': 2018-10-08 @@ -17100,15 +16802,15 @@ This search is used to identify the creation of multiple user accounts using the
====Search==== -`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" -| rex field=cookie "form_key=(?\w+)" +`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" +| rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^& -|^$]+)" -| search Username=* -| rex field=Username "@(?.*)" -| stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain -| where UniqueUsernames> 25 -| `web_fraud___account_harvesting_filter` +|^$]+)" +| search Username=* +| rex field=Username "@(?.*)" +| stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain +| where UniqueUsernames> 25 +| `web_fraud___account_harvesting_filter` ====Associated Analytic Story==== @@ -17156,11 +16858,11 @@ As is common with many fraud-related searches, we are usually looking to attribu ---- -==={{visible anchor|Web Fraud - Anomalous User Clickspeed|web-fraud---anomalous-user-clickspeed}}=== +===Web Fraud - Anomalous User Clickspeed=== This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] * '''Last Updated''': 2018-10-08 @@ -17168,13 +16870,13 @@ This search is used to examine web sessions to identify those where the clicks a
====Search==== -`stream_http` http_content_type=text* -| rex field=cookie "form_key=(?\w+)" -| streamstats window=2 current=1 range(_time) as TimeDelta by session_id -| where TimeDelta>0 -|stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id -| where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) -| `web_fraud___anomalous_user_clickspeed_filter` +`stream_http` http_content_type=text* +| rex field=cookie "form_key=(?\w+)" +| streamstats window=2 current=1 range(_time) as TimeDelta by session_id +| where TimeDelta>0 +|stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id +| where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) +| `web_fraud___anomalous_user_clickspeed_filter` ====Associated Analytic Story==== @@ -17226,26 +16928,26 @@ As is common with many fraud-related searches, we are usually looking to attribu ---- -==={{visible anchor|Web Fraud - Password Sharing Across Accounts|web-fraud---password-sharing-across-accounts}}=== +===Web Fraud - Password Sharing Across Accounts=== This search is used to identify user accounts that share a common password. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2018-10-08
====Search==== -`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* +`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data "login\[username\]=(?[^& -|^$]+)" +|^$]+)" | rex field=form_data "login\[password\]=(?[^& -|^$]+)" +|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password -|where UniqueUsernames>5 -| `web_fraud___password_sharing_across_accounts_filter` +|where UniqueUsernames>5 +| `web_fraud___password_sharing_across_accounts_filter` ====Associated Analytic Story==== @@ -17291,263 +16993,7 @@ As is common with many fraud-related searches, we are usually looking to attribu ---- -==={{visible anchor|Web Servers Executing Suspicious Processes|web-servers-executing-suspicious-processes}}=== -This search looks for suspicious processes on all systems labeled as web servers. -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1082/ T1082] -* '''Last Updated''': 2019-04-01 -
-
-====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `web_servers_executing_suspicious_processes_filter` - -====Associated Analytic Story==== - -* Apache Struts Vulnerability - - -====How To Implement==== -You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1082 -| System Information Discovery -| Discovery -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. - -====Reference==== - - -====Test Dataset==== - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Windows AdFind Exe|windows-adfind-exe}}=== -This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1018/ T1018] -* '''Last Updated''': 2020-12-16 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `windows_adfind_exe_filter` - -====Associated Analytic Story==== - -* Sunburst Malware - - -====How To Implement==== -To successfully implement this search, you need to be ingesting logs with the process name, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1018 -| Remote System Discovery -| Discovery -|} - -====Kill Chain Phase==== - -* Exploitation - - -====Known False Positives==== -administrators rarely use adfind, usually not used for legitimate reasons - -====Reference==== - -* https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/ - -* https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log - - -''version'': 1 -
-
- ----- - -==={{visible anchor|Windows Event Log Cleared|windows-event-log-cleared}}=== -This search looks for Windows events that indicate one of the Windows event logs has been purged. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1070.001/ T1070.001] -* '''Last Updated''': 2020-07-06 - -
-
- -====Search==== -(`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) -| stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `windows_event_log_cleared_filter` - -====Associated Analytic Story==== - -* Windows Log Manipulation - -* Ransomware - - -====How To Implement==== -To successfully implement this search, you need to be ingesting Windows event logs from your hosts. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1070.001 -| Clear Windows Event Logs -| Defense Evasion -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -It is possible that these logs may be legitimately cleared by Administrators. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-security.log - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-system.log - - -''version'': 4 -
-
- ----- - -==={{visible anchor|Windows Security Account Manager Stopped|windows-security-account-manager-stopped}}=== -The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1489/ T1489] -* '''Last Updated''': 2020-11-06 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" -| `drop_dm_object_name(Processes)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `windows_security_account_manager_stopped_filter` - -====Associated Analytic Story==== - -* Ryuk Ransomware - - -====How To Implement==== -You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1489 -| Service Stop -| Impact -|} - -====Kill Chain Phase==== - -* Delivery - - -====Known False Positives==== -SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log - - -''version'': 1 -
-
- ----- - - - -[[Category:V:ESSOC:3.15.0]] \ No newline at end of file +[[Category:V:ESSOC:3.15.0]] From 3fc7550cdc50995811dbe19348deea346984dac5 Mon Sep 17 00:00:00 2001 From: divious1 Date: Mon, 1 Mar 2021 22:04:55 -0500 Subject: [PATCH 09/62] working hyper links --- bin/jinja2_templates/doc_detections_wiki.j2 | 4 +- docs/detections.wiki | 3306 +++++++++---------- 2 files changed, 1655 insertions(+), 1655 deletions(-) diff --git a/bin/jinja2_templates/doc_detections_wiki.j2 b/bin/jinja2_templates/doc_detections_wiki.j2 index 9a0aaffc98..46681600db 100644 --- a/bin/jinja2_templates/doc_detections_wiki.j2 +++ b/bin/jinja2_templates/doc_detections_wiki.j2 @@ -22,7 +22,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by ====Associated Analytic Story==== {% for story in detection.tags.analytic_story %} -* {{ story }} +* [[Documentation:ESSOC:stories:UseCase#{{ story|replace(" ", "_") }}|{{ story }}]] {% endfor %} ====How To Implement==== @@ -73,4 +73,4 @@ All the detections shipped to different Splunk products. Below is a breakdown by {% endfor %} -[[Category:V:ESSOC:draft]] +[[Category:V:ESSOC:drafts]] diff --git a/docs/detections.wiki b/docs/detections.wiki index 20b35d7ab6..0844928868 100644 --- a/docs/detections.wiki +++ b/docs/detections.wiki @@ -19,15 +19,15 @@ The search looks at the change-analysis data model and detects email files creat ====Search==== -| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest -| `drop_dm_object_name("Filesystem")` -| `security_content_ctime(firstTime)` +| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest +| `drop_dm_object_name("Filesystem")` +| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `email_files_written_outside_of_the_outlook_directory_filter` ====Associated Analytic Story==== -* Collection and Staging +* [[Documentation:ESSOC:stories:UseCase#Collection_and_Staging|Collection and Staging]] ====How To Implement==== @@ -71,7 +71,7 @@ Administrators and users sometimes prefer backing up their email data by moving This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] * '''Last Updated''': 2020-07-21 @@ -79,17 +79,17 @@ This search detects Okta login failures due to bad credentials for multiple user
====Search==== -`okta` outcome.reason=INVALID_CREDENTIALS -| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city -| stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`okta` outcome.reason=INVALID_CREDENTIALS +| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city +| stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | search distinct_users > 5 | `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` ====Associated Analytic Story==== -* Suspicious Okta Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Okta_Activity|Suspicious Okta Activity]] ====How To Implement==== @@ -131,7 +131,7 @@ A single public IP address servicing multiple legitmate users may trigger this s Detect Okta user lockout events * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] * '''Last Updated''': 2020-07-21 @@ -139,14 +139,14 @@ Detect Okta user lockout events
====Search==== -`okta` displayMessage="Max sign in attempts exceeded" -| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city -| table _time, user, country, state, city, src_ip +`okta` displayMessage="Max sign in attempts exceeded" +| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city +| table _time, user, country, state, city, src_ip | `okta_account_lockout_events_filter` ====Associated Analytic Story==== -* Suspicious Okta Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Okta_Activity|Suspicious Okta Activity]] ====How To Implement==== @@ -188,7 +188,7 @@ None. Account lockouts should be followed up on to determine if the actual user Detect failed Okta SSO events * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] * '''Last Updated''': 2020-07-21 @@ -196,15 +196,15 @@ Detect failed Okta SSO events
====Search==== -`okta` displayMessage="User attempted unauthorized access to app" -| stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`okta` displayMessage="User attempted unauthorized access to app" +| stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `okta_failed_sso_attempts_filter` ====Associated Analytic Story==== -* Suspicious Okta Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Okta_Activity|Suspicious Okta Activity]] ====How To Implement==== @@ -246,7 +246,7 @@ There may be a faulty config preventing legitmate users from accessing apps they This search detects logins from the same user from different cities in a 24 hour period. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] * '''Last Updated''': 2020-07-21 @@ -254,16 +254,16 @@ This search detects logins from the same user from different cities in a 24 hour
====Search==== -`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null -| stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user +`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null +| stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `okta_user_logins_from_multiple_cities_filter` +| `security_content_ctime(lastTime)` +| `okta_user_logins_from_multiple_cities_filter` | search locations > 1 ====Associated Analytic Story==== -* Suspicious Okta Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Okta_Activity|Suspicious Okta Activity]] ====How To Implement==== @@ -315,14 +315,14 @@ This search looks for suspicious processes on all systems labeled as web servers ====Search==== | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter` ====Associated Analytic Story==== -* Apache Struts Vulnerability +* [[Documentation:ESSOC:stories:UseCase#Apache_Struts_Vulnerability|Apache Struts Vulnerability]] ====How To Implement==== @@ -372,7 +372,7 @@ This search looks for AssumeRole events where an IAM role in a different account * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Authentication -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2020-05-28
@@ -380,21 +380,21 @@ This search looks for AssumeRole events where an IAM role in a different account ====Search==== -| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role -| `drop_dm_object_name(Authentication)` -| rex field=user_role "arn:aws:sts:*:(?.*):" -| where vendor_account != dest_account -| rename vendor_account as requestingAccountId dest_account as requestedAccountId -| lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime -| eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") -| where status = "New Cross Account Activity" -| `security_content_ctime(firstTime)` +| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role +| `drop_dm_object_name(Authentication)` +| rex field=user_role "arn:aws:sts:*:(?.*):" +| where vendor_account != dest_account +| rename vendor_account as requestingAccountId dest_account as requestedAccountId +| lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime +| eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") +| where status = "New Cross Account Activity" +| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_cross_account_activity_from_previously_unseen_account_filter` ====Associated Analytic Story==== -* Suspicious Cloud Authentication Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Authentication_Activities|Suspicious Cloud Authentication Activities]] ====How To Implement==== @@ -436,7 +436,7 @@ Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicio This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1486/ T1486] * '''Last Updated''': 2021-01-11 @@ -444,22 +444,22 @@ This search provides detection of KMS keys which action kms:Encrypt is accessibl
====Search==== -`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy -| spath input=requestParameters.policy output=key_policy_statements path=Statement{} -| mvexpand key_policy_statements -| spath input=key_policy_statements output=key_policy_action_1 path=Action -| spath input=key_policy_statements output=key_policy_action_2 path=Action{} -| eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) -| spath input=key_policy_statements output=key_policy_principal path=Principal.AWS -| search key_policy_action="kms:Encrypt" AND key_policy_principal="*" -| stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId +`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy +| spath input=requestParameters.policy output=key_policy_statements path=Statement{} +| mvexpand key_policy_statements +| spath input=key_policy_statements output=key_policy_action_1 path=Action +| spath input=key_policy_statements output=key_policy_action_2 path=Action{} +| eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) +| spath input=key_policy_statements output=key_policy_principal path=Principal.AWS +| search key_policy_action="kms:Encrypt" AND key_policy_principal="*" +| stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` ====Associated Analytic Story==== -* Ransomware Cloud +* [[Documentation:ESSOC:stories:UseCase#Ransomware_Cloud|Ransomware Cloud]] ====How To Implement==== @@ -509,7 +509,7 @@ unknown This search provides detection of users with KMS keys performing encryption specifically against S3 buckets. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1486/ T1486] * '''Last Updated''': 2021-01-11 @@ -517,16 +517,16 @@ This search provides detection of users with KMS keys performing encryption spec
====Search==== -`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" -| rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file -| stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user +`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" +| rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file +| stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter` ====Associated Analytic Story==== -* Ransomware Cloud +* [[Documentation:ESSOC:stories:UseCase#Ransomware_Cloud|Ransomware Cloud]] ====How To Implement==== @@ -576,7 +576,7 @@ bucket with S3 encryption The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] * '''Last Updated''': 2021-01-11 @@ -584,19 +584,19 @@ The search looks for CloudTrail events to detect if any network ACLs were create
====Search==== -`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 -| append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 -| eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' -| where port_range>1024] -| fillnull -| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock +`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 +| append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 +| eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' +| where port_range>1024] +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter` ====Associated Analytic Story==== -* AWS Network ACL Activity +* [[Documentation:ESSOC:stories:UseCase#AWS_Network_ACL_Activity|AWS Network ACL Activity]] ====How To Implement==== @@ -642,7 +642,7 @@ It's possible that an admin has created this ACL with all ports open for some le Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] * '''Last Updated''': 2021-01-12 @@ -650,16 +650,16 @@ Enforcing network-access controls is one of the defensive mechanisms used by clo
====Search==== -`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false -| fillnull -| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent +`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `aws_network_access_control_list_deleted_filter` ====Associated Analytic Story==== -* AWS Network ACL Activity +* [[Documentation:ESSOC:stories:UseCase#AWS_Network_ACL_Activity|AWS Network ACL Activity]] ====How To Implement==== @@ -705,7 +705,7 @@ It's possible that a user has legitimately deleted a network ACL. This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] * '''Last Updated''': 2021-01-26 @@ -713,15 +713,15 @@ This search provides specific SAML access from specific Service Provider, user a
====Search==== -`cloudtrail` eventName=Assumerolewithsaml -| stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent +`cloudtrail` eventName=Assumerolewithsaml +| stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` |`aws_saml_access_by_provider_user_and_principal_filter` ====Associated Analytic Story==== -* Cloud Federated Credential Abuse +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] ====How To Implement==== @@ -773,7 +773,7 @@ Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very diff This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] * '''Last Updated''': 2021-01-26 @@ -781,15 +781,15 @@ This search provides detection of updates to SAML provider in AWS. Updates to SA
====Search==== -`cloudtrail` eventName=UpdateSAMLProvider -| stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId +`cloudtrail` eventName=UpdateSAMLProvider +| stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` |`aws_saml_update_identity_provider_filter` ====Associated Analytic Story==== -* Cloud Federated Credential Abuse +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] ====How To Implement==== @@ -850,26 +850,26 @@ This search will detect a spike in the number of API calls made to your cloud in ====Search==== -| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h -| `drop_dm_object_name("All_Changes")` -| eval HourOfDay=strftime(_time, "%H") -| eval HourOfDay=floor(HourOfDay/4)*4 -| eval DayOfWeek=strftime(_time, "%w") -| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) -| join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] -| where cardinality >=16 -| apply cloud_excessive_api_calls_v1 threshold=0.005 -| rename "IsOutlier(api_calls)" as isOutlier -| where isOutlier=1 -| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) -| where api_calls > expected_upper_threshold -| eval distance_from_threshold = api_calls - expected_upper_threshold -| table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold +| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] +| where cardinality >=16 +| apply cloud_excessive_api_calls_v1 threshold=0.005 +| rename "IsOutlier(api_calls)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| where api_calls > expected_upper_threshold +| eval distance_from_threshold = api_calls - expected_upper_threshold +| table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` ====Associated Analytic Story==== -* Suspicious Cloud User Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_User_Activities|Suspicious Cloud User Activities]] ====How To Implement==== @@ -924,25 +924,25 @@ This search finds for the number successfully destroyed cloud instances for ever ====Search==== -| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h -| `drop_dm_object_name("All_Changes")` -| eval HourOfDay=strftime(_time, "%H") -| eval HourOfDay=floor(HourOfDay/4)*4 -| eval DayOfWeek=strftime(_time, "%w") -| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) -| join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] -| where cardinality >=16 -| apply cloud_excessive_instances_destroyed_v1 threshold=0.005 -| rename "IsOutlier(instances_destroyed)" as isOutlier -| where isOutlier=1 -| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) -| eval distance_from_threshold = instances_destroyed - expected_upper_threshold -| table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id +| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] +| where cardinality >=16 +| apply cloud_excessive_instances_destroyed_v1 threshold=0.005 +| rename "IsOutlier(instances_destroyed)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| eval distance_from_threshold = instances_destroyed - expected_upper_threshold +| table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter` ====Associated Analytic Story==== -* Suspicious Cloud Instance Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Instance_Activities|Suspicious Cloud Instance Activities]] ====How To Implement==== @@ -995,27 +995,27 @@ This search finds for the number successfully created cloud instances for every ====Search==== -| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h -| `drop_dm_object_name("All_Changes")` -| eval HourOfDay=strftime(_time, "%H") -| eval HourOfDay=floor(HourOfDay/4)*4 -| eval DayOfWeek=strftime(_time, "%w") -| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) -| join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] -| where cardinality >=16 -| apply cloud_excessive_instances_created_v1 threshold=0.005 -| rename "IsOutlier(instances_launched)" as isOutlier -| where isOutlier=1 -| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) -| eval distance_from_threshold = instances_launched - expected_upper_threshold -| table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id +| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] +| where cardinality >=16 +| apply cloud_excessive_instances_created_v1 threshold=0.005 +| rename "IsOutlier(instances_launched)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| eval distance_from_threshold = instances_launched - expected_upper_threshold +| table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_launched_filter` ====Associated Analytic Story==== -* Cloud Cryptomining +* [[Documentation:ESSOC:stories:UseCase#Cloud_Cryptomining|Cloud Cryptomining]] -* Suspicious Cloud Instance Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Instance_Activities|Suspicious Cloud Instance Activities]] ====How To Implement==== @@ -1068,26 +1068,26 @@ This search will detect a spike in the number of API calls made to your cloud in ====Search==== -| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h -| `drop_dm_object_name("All_Changes")` -| eval HourOfDay=strftime(_time, "%H") -| eval HourOfDay=floor(HourOfDay/4)*4 -| eval DayOfWeek=strftime(_time, "%w") -| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) -| join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] -| where cardinality >=16 -| apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 -| rename "IsOutlier(security_group_api_calls)" as isOutlier -| where isOutlier=1 -| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) -| where security_group_api_calls > expected_upper_threshold -| eval distance_from_threshold = security_group_api_calls - expected_upper_threshold -| table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold +| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h +| `drop_dm_object_name("All_Changes")` +| eval HourOfDay=strftime(_time, "%H") +| eval HourOfDay=floor(HourOfDay/4)*4 +| eval DayOfWeek=strftime(_time, "%w") +| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) +| join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] +| where cardinality >=16 +| apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 +| rename "IsOutlier(security_group_api_calls)" as isOutlier +| where isOutlier=1 +| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) +| where security_group_api_calls > expected_upper_threshold +| eval distance_from_threshold = security_group_api_calls - expected_upper_threshold +| table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter` ====Associated Analytic Story==== -* Suspicious Cloud User Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_User_Activities|Suspicious Cloud User Activities]] ====How To Implement==== @@ -1142,21 +1142,21 @@ This search looks for new commands from each user role. ====Search==== -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object -| `drop_dm_object_name("All_Changes")` -| lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenUserApiCall=min(firstTimeSeen) -| where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") -| table firstTime, user, object, command -|`security_content_ctime(firstTime)` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUserApiCall=min(firstTimeSeen) +| where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") +| table firstTime, user, object, command +|`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cloud_api_calls_from_previously_unseen_user_roles_filter` ====Associated Analytic Story==== -* Suspicious Cloud User Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_User_Activities|Suspicious Cloud User Activities]] ====How To Implement==== @@ -1209,20 +1209,20 @@ This search looks for cloud compute instances created by users who have not crea ====Search==== -| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region -| `drop_dm_object_name("All_Changes")` -| lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenUser=min(firstTimeSeen) -| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") -| table firstTime, user, dest, count vendor_region -| `security_content_ctime(firstTime)` +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUser=min(firstTimeSeen) +| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count vendor_region +| `security_content_ctime(firstTime)` | `cloud_compute_instance_created_by_previously_unseen_user_filter` ====Associated Analytic Story==== -* Cloud Cryptomining +* [[Documentation:ESSOC:stories:UseCase#Cloud_Cryptomining|Cloud Cryptomining]] ====How To Implement==== @@ -1275,20 +1275,20 @@ This search looks at cloud-infrastructure events where an instance is created in ====Search==== -| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user -| `drop_dm_object_name("All_Changes")` -| lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenRegion=min(firstTimeSeen) -| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") -| table firstTime, user, dest, count , vendor_region -| `security_content_ctime(firstTime)` +| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenRegion=min(firstTimeSeen) +| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count , vendor_region +| `security_content_ctime(firstTime)` | `cloud_compute_instance_created_in_previously_unused_region_filter` ====Associated Analytic Story==== -* Cloud Cryptomining +* [[Documentation:ESSOC:stories:UseCase#Cloud_Cryptomining|Cloud Cryptomining]] ====How To Implement==== @@ -1335,7 +1335,7 @@ This search looks for cloud compute instances being created with previously unse * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Change -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2018-10-12
@@ -1343,22 +1343,22 @@ This search looks for cloud compute instances being created with previously unse ====Search==== -| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user -| `drop_dm_object_name("All_Changes")` -| `drop_dm_object_name("Instance_Changes")` -| where image_id != "unknown" -| lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenImage=min(firstTimeSeen) -| where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") -| table firstTime, user, image_id, count, dest -| `security_content_ctime(firstTime)` +| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| `drop_dm_object_name("Instance_Changes")` +| where image_id != "unknown" +| lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenImage=min(firstTimeSeen) +| where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") +| table firstTime, user, image_id, count, dest +| `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter` ====Associated Analytic Story==== -* Cloud Cryptomining +* [[Documentation:ESSOC:stories:UseCase#Cloud_Cryptomining|Cloud Cryptomining]] ====How To Implement==== @@ -1399,7 +1399,7 @@ Find EC2 instances being created with previously unseen instance types. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Change -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2020-09-12
@@ -1407,22 +1407,22 @@ Find EC2 instances being created with previously unseen instance types. ====Search==== -| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user -| `drop_dm_object_name("All_Changes")` -| `drop_dm_object_name("Instance_Changes")` -| where instance_type != "unknown" -| lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenInstanceType=min(firstTimeSeen) -| where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") -| table firstTime, user, dest, count, instance_type -| `security_content_ctime(firstTime)` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user +| `drop_dm_object_name("All_Changes")` +| `drop_dm_object_name("Instance_Changes")` +| where instance_type != "unknown" +| lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenInstanceType=min(firstTimeSeen) +| where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") +| table firstTime, user, dest, count, instance_type +| `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` ====Associated Analytic Story==== -* Cloud Cryptomining +* [[Documentation:ESSOC:stories:UseCase#Cloud_Cryptomining|Cloud Cryptomining]] ====How To Implement==== @@ -1471,20 +1471,20 @@ This search looks for cloud instances being modified by users who have not previ ====Search==== -| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user -| `drop_dm_object_name("All_Changes")` -| lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenUser=min(firstTimeSeen) -| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") -| table firstTime user command object_id count -| `security_content_ctime(firstTime)` +| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenUser=min(firstTimeSeen) +| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") +| table firstTime user command object_id count +| `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter` ====Associated Analytic Story==== -* Suspicious Cloud Instance Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Instance_Activities|Suspicious Cloud Instance Activities]] ====How To Implement==== @@ -1537,22 +1537,22 @@ This search looks for cloud provisioning activities from previously unseen citie ====Search==== -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command -| `drop_dm_object_name("All_Changes")` -| iplocation src -| where isnotnull(City) -| lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenCity=min(firstTimeSeen) -| where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) -| table firstTime, src, City, user, object, command -| `cloud_provisioning_activity_from_previously_unseen_city_filter` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(City) +| lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenCity=min(firstTimeSeen) +| where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, City, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_city_filter` | `security_content_ctime(firstTime)` ====Associated Analytic Story==== -* Suspicious Cloud Provisioning Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Provisioning_Activities|Suspicious Cloud Provisioning Activities]] ====How To Implement==== @@ -1606,22 +1606,22 @@ This search looks for cloud provisioning activities from previously unseen count ====Search==== -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command -| `drop_dm_object_name("All_Changes")` -| iplocation src -| where isnotnull(Country) -| lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenCountry=min(firstTimeSeen) -| where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") -| table firstTime, src, Country, user, object, command -| `cloud_provisioning_activity_from_previously_unseen_country_filter` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(Country) +| lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenCountry=min(firstTimeSeen) +| where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") +| table firstTime, src, Country, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_country_filter` | `security_content_ctime(firstTime)` ====Associated Analytic Story==== -* Suspicious Cloud Provisioning Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Provisioning_Activities|Suspicious Cloud Provisioning Activities]] ====How To Implement==== @@ -1675,20 +1675,20 @@ This search looks for cloud provisioning activities from previously unseen IP ad ====Search==== -| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command -| `drop_dm_object_name("All_Changes")` -| lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenSrc=min(firstTimeSeen) -| where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) -| table firstTime, src, user, object_id, command -| `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenSrc=min(firstTimeSeen) +| where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, user, object_id, command +| `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` | `security_content_ctime(firstTime)` ====Associated Analytic Story==== -* Suspicious Cloud Provisioning Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Provisioning_Activities|Suspicious Cloud Provisioning Activities]] ====How To Implement==== @@ -1742,22 +1742,22 @@ This search looks for cloud provisioning activities from previously unseen regio ====Search==== -| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command -| `drop_dm_object_name("All_Changes")` -| iplocation src -| where isnotnull(Region) -| lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data -| eventstats max(enough_data) as enough_data -| where enough_data=1 -| eval firstTimeSeenRegion=min(firstTimeSeen) -| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) -| table firstTime, src, Region, user, object, command -| `cloud_provisioning_activity_from_previously_unseen_region_filter` +| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command +| `drop_dm_object_name("All_Changes")` +| iplocation src +| where isnotnull(Region) +| lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data +| eventstats max(enough_data) as enough_data +| where enough_data=1 +| eval firstTimeSeenRegion=min(firstTimeSeen) +| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) +| table firstTime, src, Region, user, object, command +| `cloud_provisioning_activity_from_previously_unseen_region_filter` | `security_content_ctime(firstTime)` ====Associated Analytic Story==== -* Suspicious Cloud Provisioning Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Provisioning_Activities|Suspicious Cloud Provisioning Activities]] ====How To Implement==== @@ -1803,7 +1803,7 @@ This search looks for CloudTrail events wherein a console login event by a user * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Authentication -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2020-05-28
@@ -1811,19 +1811,19 @@ This search looks for CloudTrail events wherein a console login event by a user ====Search==== -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user -| `drop_dm_object_name(Authentication)` -| inputlookup append=t previously_seen_users_console_logins -| stats min(firstTime) as firstTime max(lastTime) as lastTime by user -| eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") -|where userStatus="First Time Logging into AWS Console" -| `security_content_ctime(firstTime)` +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user +| `drop_dm_object_name(Authentication)` +| inputlookup append=t previously_seen_users_console_logins +| stats min(firstTime) as firstTime max(lastTime) as lastTime by user +| eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") +|where userStatus="First Time Logging into AWS Console" +| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_aws_console_login_by_new_user_filter` ====Associated Analytic Story==== -* Suspicious Cloud Authentication Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Authentication_Activities|Suspicious Cloud Authentication Activities]] ====How To Implement==== @@ -1874,27 +1874,27 @@ This search looks for CloudTrail events wherein a console login event by a user ====Search==== -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src -| iplocation Authentication.src -| `drop_dm_object_name(Authentication)` -| table firstTime lastTime user City +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user City | join user type=outer [ -| inputlookup previously_seen_users_console_logins -| stats earliest(firstTime) AS earliestseen by user City -| fields earliestseen user City] -| eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") -| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") -| where userCity = "New City" AND userStatus != "Old User" -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| table firstTime lastTime user City userStatus userCity +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user City +| fields earliestseen user City] +| eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") +| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userCity = "New City" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user City userStatus userCity | `detect_aws_console_login_by_user_from_new_city_filter` ====Associated Analytic Story==== -* Suspicious AWS Login Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_Login_Activities|Suspicious AWS Login Activities]] -* Suspicious Cloud Authentication Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Authentication_Activities|Suspicious Cloud Authentication Activities]] ====How To Implement==== @@ -1949,27 +1949,27 @@ This search looks for CloudTrail events wherein a console login event by a user ====Search==== -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src -| iplocation Authentication.src -| `drop_dm_object_name(Authentication)` -| table firstTime lastTime user Country +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user Country | join user type=outer [ -| inputlookup previously_seen_users_console_logins -| stats earliest(firstTime) AS earliestseen by user Country -| fields earliestseen user Country] -| eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") -| eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") -| where userCountry = "New Country" AND userStatus != "Old User" -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| table firstTime lastTime user Country userStatus userCountry +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user Country +| fields earliestseen user Country] +| eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") +| eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userCountry = "New Country" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user Country userStatus userCountry | `detect_aws_console_login_by_user_from_new_country_filter` ====Associated Analytic Story==== -* Suspicious AWS Login Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_Login_Activities|Suspicious AWS Login Activities]] -* Suspicious Cloud Authentication Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Authentication_Activities|Suspicious Cloud Authentication Activities]] ====How To Implement==== @@ -2024,27 +2024,27 @@ This search looks for CloudTrail events wherein a console login event by a user ====Search==== -| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src -| iplocation Authentication.src -| `drop_dm_object_name(Authentication)` -| table firstTime lastTime user Region +| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src +| iplocation Authentication.src +| `drop_dm_object_name(Authentication)` +| table firstTime lastTime user Region | join user type=outer [ -| inputlookup previously_seen_users_console_logins -| stats earliest(firstTime) AS earliestseen by user Region -| fields earliestseen user Region] -| eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") -| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") -| where userRegion = "New Region" AND userStatus != "Old User" -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| table firstTime lastTime user Region userStatus userRegion +| inputlookup previously_seen_users_console_logins +| stats earliest(firstTime) AS earliestseen by user Region +| fields earliestseen user Region] +| eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") +| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") +| where userRegion = "New Region" AND userStatus != "Old User" +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime lastTime user Region userStatus userRegion | `detect_aws_console_login_by_user_from_new_region_filter` ====Associated Analytic Story==== -* Suspicious AWS Login Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_Login_Activities|Suspicious AWS Login Activities]] -* Suspicious Cloud Authentication Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_Authentication_Activities|Suspicious Cloud Authentication Activities]] ====How To Implement==== @@ -2090,7 +2090,7 @@ When a legitimate new user logins for the first time, this activity will be dete This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] * '''Last Updated''': 2020-08-10 @@ -2098,29 +2098,29 @@ This search looks at GCP Storage bucket-access logs and detects new or previousl
====Search==== -`google_gcp_pubsub_message` -| multikv -| rename sc_status_ as status -| rename cs_object_ as bucket_name -| rename c_ip_ as remote_ip -| rename cs_uri_ as request_uri -| rename cs_method_ as operation -| search status="\"200\"" -| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri -| table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri -| inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv -| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri -| outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv -| eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) -| where newIP=1 -| eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") -| eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") -| table first_time last_time bucket_name remote_ip operation request_uri +`google_gcp_pubsub_message` +| multikv +| rename sc_status_ as status +| rename cs_object_ as bucket_name +| rename c_ip_ as remote_ip +| rename cs_uri_ as request_uri +| rename cs_method_ as operation +| search status="\"200\"" +| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri +| table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri +| inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri +| outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv +| eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) +| where newIP=1 +| eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") +| eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") +| table first_time last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter` ====Associated Analytic Story==== -* Suspicious GCP Storage Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_GCP_Storage_Activities|Suspicious GCP Storage Activities]] ====How To Implement==== @@ -2164,7 +2164,7 @@ GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow i This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] * '''Last Updated''': 2020-08-05 @@ -2172,21 +2172,21 @@ This search looks for GCP PubSub events where a user has created an open/public
====Search==== -`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions -| spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action -| spath output=user path=data.protoPayload.authenticationInfo.principalEmail -| spath output=location path=data.protoPayload.resourceLocation.currentLocations{} -| spath output=src path=data.protoPayload.requestMetadata.callerIp -| spath output=bucketName path=data.protoPayload.resourceName -| spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role -| spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member -| search (member=allUsers AND action=ADD) -| table _time, bucketName, src, user, location, action, role, member +`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions +| spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action +| spath output=user path=data.protoPayload.authenticationInfo.principalEmail +| spath output=location path=data.protoPayload.resourceLocation.currentLocations{} +| spath output=src path=data.protoPayload.requestMetadata.callerIp +| spath output=bucketName path=data.protoPayload.resourceName +| spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role +| spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member +| search (member=allUsers AND action=ADD) +| table _time, bucketName, src, user, location, action, role, member | search `detect_new_open_gcp_storage_buckets_filter` ====Associated Analytic Story==== -* Suspicious GCP Storage Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_GCP_Storage_Activities|Suspicious GCP Storage Activities]] ====How To Implement==== @@ -2230,7 +2230,7 @@ While this search has no known false positives, it is possible that a GCP admin This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] * '''Last Updated''': 2021-01-12 @@ -2238,17 +2238,17 @@ This search looks for CloudTrail events where a user has created an open/public
====Search==== -`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") -| rename requestParameters.bucketName AS bucketName -| fillnull -| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control +`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") +| rename requestParameters.bucketName AS bucketName +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_over_aws_cli_filter` ====Associated Analytic Story==== -* Suspicious AWS S3 Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_S3_Activities|Suspicious AWS S3 Activities]] ====How To Implement==== @@ -2294,7 +2294,7 @@ While this search has no known false positives, it is possible that an AWS admin This search looks for CloudTrail events where a user has created an open/public S3 bucket. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] * '''Last Updated''': 2021-01-12 @@ -2302,24 +2302,24 @@ This search looks for CloudTrail events where a user has created an open/public
====Search==== -`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl -| rex field=_raw "(?{.+})" -| spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} -| search grantees=* -| mvexpand grantees -| spath input=grantees output=uri path=Grantee.URI -| spath input=grantees output=permission path=Permission -| search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") -| search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") -| rename requestParameters.bucketName AS bucketName -| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission bucketName +`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl +| rex field=_raw "(?{.+})" +| spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} +| search grantees=* +| mvexpand grantees +| spath input=grantees output=uri path=Grantee.URI +| spath input=grantees output=permission path=Permission +| search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") +| search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") +| rename requestParameters.bucketName AS bucketName +| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission bucketName | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_filter` ====Associated Analytic Story==== -* Suspicious AWS S3 Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_S3_Activities|Suspicious AWS S3 Activities]] ====How To Implement==== @@ -2365,7 +2365,7 @@ While this search has no known false positives, it is possible that an AWS admin This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] * '''Last Updated''': 2018-06-28 @@ -2373,24 +2373,24 @@ This search looks at S3 bucket-access logs and detects new or previously unseen
====Search==== -`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 -| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip -| inputlookup append=t previously_seen_S3_access_from_remote_ip.csv -| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip -| outputlookup previously_seen_S3_access_from_remote_ip.csv -| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) -| where newIP=1 +`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 +| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip +| inputlookup append=t previously_seen_S3_access_from_remote_ip.csv +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip +| outputlookup previously_seen_S3_access_from_remote_ip.csv +| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newIP=1 | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | table bucket_name remote_ip] -| iplocation remote_ip -|rename remote_ip as src_ip -| table _time bucket_name src_ip City Country operation request_uri +| iplocation remote_ip +|rename remote_ip as src_ip +| table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter` ====Associated Analytic Story==== -* Suspicious AWS S3 Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_S3_Activities|Suspicious AWS S3 Activities]] ====How To Implement==== @@ -2434,27 +2434,27 @@ S3 buckets can be accessed from any IP, as long as it can make a successful conn This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2021-01-26
====Search==== -`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance -| bucket span=4h _time -| stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest -| eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev -| eval threshold_value = 3 -| eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) -| search isOutlier=1 -| table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg +`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance +| bucket span=4h _time +| stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest +| eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev +| eval threshold_value = 3 +| eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) +| search isOutlier=1 +| table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` ====Associated Analytic Story==== -* AWS Security Hub Alerts +* [[Documentation:ESSOC:stories:UseCase#AWS_Security_Hub_Alerts|AWS Security Hub Alerts]] ====How To Implement==== @@ -2494,28 +2494,28 @@ None This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2021-01-26
====Search==== -`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser -| rename findings{}.Resources{}.Id as user -| bucket span=4h _time -| stats count AS alerts by _time user -| eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev -| eval threshold_value = 2 -| eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) -| search isOutlier=1 -| table _time user alerts +`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser +| rename findings{}.Resources{}.Id as user +| bucket span=4h _time +| stats count AS alerts by _time user +| eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev +| eval threshold_value = 2 +| eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) +| search isOutlier=1 +| table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter` ====Associated Analytic Story==== -* AWS Security Hub Alerts +* [[Documentation:ESSOC:stories:UseCase#AWS_Security_Hub_Alerts|AWS Security Hub Alerts]] ====How To Implement==== @@ -2553,7 +2553,7 @@ None This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] * '''Last Updated''': 2018-11-27 @@ -2561,31 +2561,31 @@ This search detects users creating spikes in API activity related to deletion of
====Search==== -`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket -| spath output=arn path=userIdentity.arn -| stats count as apiCalls by arn -| inputlookup s3_deletion_baseline append=t -| fields - latestCount -| stats values(*) as * by arn -| rename apiCalls as latestCount -| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 -| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) -| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) -| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls -| outputlookup s3_deletion_baseline -| eval dataPointThreshold = 15, deviationThreshold = 3 -| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) -| where isSpike=1 -| rename arn as userIdentity.arn -| table userIdentity.arn] -| spath output=user userIdentity.arn -| spath output=bucketName path=requestParameters.bucketName -| stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup s3_deletion_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup s3_deletion_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| spath output=bucketName path=requestParameters.bucketName +| stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter` ====Associated Analytic Story==== -* Suspicious AWS S3 Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_S3_Activities|Suspicious AWS S3 Activities]] ====How To Implement==== @@ -2629,39 +2629,39 @@ Based on the values of`dataPointThreshold` and `deviationThreshold`, the false p This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2018-05-07
====Search==== -`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) -| stats count as numberOfBlockedConnections by src_ip -| inputlookup baseline_blocked_outbound_connections append=t -| fields - latestCount -| stats values(*) as * by src_ip -| rename numberOfBlockedConnections as latestCount -| eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 -| eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) -| eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) -| table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections -| outputlookup baseline_blocked_outbound_connections -| eval dataPointThreshold = 5, deviationThreshold = 3 -| eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) -| where isSpike=1 -| table src_ip] -| stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip +`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) +| stats count as numberOfBlockedConnections by src_ip +| inputlookup baseline_blocked_outbound_connections append=t +| fields - latestCount +| stats values(*) as * by src_ip +| rename numberOfBlockedConnections as latestCount +| eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 +| eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) +| eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections +| outputlookup baseline_blocked_outbound_connections +| eval dataPointThreshold = 5, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| table src_ip] +| stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` ====Associated Analytic Story==== -* AWS Network ACL Activity +* [[Documentation:ESSOC:stories:UseCase#AWS_Network_ACL_Activity|AWS Network ACL Activity]] -* Suspicious AWS Traffic +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_Traffic|Suspicious AWS Traffic]] -* Command and Control +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] ====How To Implement==== @@ -2703,7 +2703,7 @@ The false-positive rate may vary based on the values of`dataPointThreshold` and This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1110.001/ T1110.001] * '''Last Updated''': 2020-12-16 @@ -2711,14 +2711,14 @@ This search will detect more than 5 login failures in Office365 Azure Active Dir
====Search==== -`o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory -| stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app +`o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory +| stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app | search accounts_locked >= 5 | `high_number_of_login_failures_from_a_single_source_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] ====How To Implement==== @@ -2762,7 +2762,7 @@ unknown This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1525/ T1525] * '''Last Updated''': 2020-02-20 @@ -2771,13 +2771,13 @@ This searches show information on uploaded containers including source user, ima ====Search==== -| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type -| `drop_dm_object_name("Compute")` +| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type +| `drop_dm_object_name("Compute")` | `new_container_uploaded_to_aws_ecr_filter` ====Associated Analytic Story==== -* Container Implantation Monitoring and Investigation +* [[Documentation:ESSOC:stories:UseCase#Container_Implantation_Monitoring_and_Investigation|Container Implantation Monitoring and Investigation]] ====How To Implement==== @@ -2819,7 +2819,7 @@ Uploading container is a normal behavior from developers or users with access to This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] * '''Last Updated''': 2021-01-26 @@ -2827,17 +2827,17 @@ This search detects the creation of a new Federation setting by alerting about a
====Search==== -`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." -| stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus +`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." +| stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `o365_add_app_role_assignment_grant_user_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] -* Cloud Federated Credential Abuse +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] ====How To Implement==== @@ -2887,7 +2887,7 @@ The creation of a new Federation is not necessarily malicious, however this even This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] * '''Last Updated''': 2021-01-26 @@ -2895,17 +2895,17 @@ This search detects the creation of a new Federation setting by alerting about a
====Search==== -`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." -| stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature +`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." +| stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `o365_added_service_principal_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] -* Cloud Federated Credential Abuse +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] ====How To Implement==== @@ -2959,7 +2959,7 @@ The creation of a new Federation is not necessarily malicious, however these eve This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] * '''Last Updated''': 2021-01-12 @@ -2967,20 +2967,20 @@ This search detects newly added IP addresses/CIDR blocks to the list of MFA Trus
====Search==== -`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy -| rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" -| rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" -| eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") -| mvexpand ip_addresses_new_added -| where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) -|stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account status user_id action +`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy +| rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" +| rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" +| eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") +| mvexpand ip_addresses_new_added +| where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) +|stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account status user_id action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_bypass_mfa_via_trusted_ip_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] ====How To Implement==== @@ -3030,7 +3030,7 @@ Unless it is a special case, it is uncommon to continually update Trusted IPs to This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1556/ T1556] * '''Last Updated''': 2020-12-16 @@ -3038,15 +3038,15 @@ This search detects when multi factor authentication has been disabled, what ent
====Search==== -`o365_management_activity` Operation="Disable Strong Authentication." -| stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +`o365_management_activity` Operation="Disable Strong Authentication." +| stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` | `o365_disable_mfa_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] ====How To Implement==== @@ -3094,7 +3094,7 @@ Unless it is a special case, it is uncommon to disable MFA or Strong Authenticat This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1110/ T1110] * '''Last Updated''': 2020-12-16 @@ -3102,16 +3102,16 @@ This search detects when an excessive number of authentication failures occur th
====Search==== -`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed -| stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user -| where count > 10 -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed +| stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user +| where count > 10 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` | `o365_excessive_authentication_failures_alert_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] ====How To Implement==== @@ -3159,7 +3159,7 @@ The threshold for alert is above 10 attempts and this should reduce the number o This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1556/ T1556] * '''Last Updated''': 2021-01-26 @@ -3167,18 +3167,18 @@ This search detects accounts with high number of Single Sign ON (SSO) logon erro
====Search==== -`o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired -| stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId -| where count > 5 +`o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired +| stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId +| where count > 5 | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `o365_excessive_sso_logon_errors_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] -* Cloud Federated Credential Abuse +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] ====How To Implement==== @@ -3226,7 +3226,7 @@ Logon errors may not be malicious in nature however it may indicate attempts to This search detects the addition of a new Federated domain. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.003/ T1136.003] * '''Last Updated''': 2021-01-26 @@ -3234,17 +3234,17 @@ This search detects the addition of a new Federated domain.
====Search==== -`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" -| stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" +| stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `o365_new_federated_domain_added_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] -* Cloud Federated Credential Abuse +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] ====How To Implement==== @@ -3300,7 +3300,7 @@ The creation of a new Federated domain is not necessarily malicious, however the This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1114/ T1114] * '''Last Updated''': 2020-12-16 @@ -3308,15 +3308,15 @@ This search detects when a user has performed an Ediscovery search or exported a
====Search==== -`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" -| stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" +| stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` | `o365_pst_export_alert_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] ====How To Implement==== @@ -3364,7 +3364,7 @@ PST export can be done for legitimate purposes but due to the sensitive nature o This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.003/ T1114.003] * '''Last Updated''': 2020-12-16 @@ -3372,19 +3372,19 @@ This search detects when an admin configured a forwarding rule for multiple mail
====Search==== -`o365_management_activity` Operation=Set-Mailbox -| spath input=Parameters -| rename Identity AS src_user -| search ForwardingAddress=* -| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress -| where count_src_user > 1 -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +`o365_management_activity` Operation=Set-Mailbox +| spath input=Parameters +| rename Identity AS src_user +| search ForwardingAddress=* +| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress +| where count_src_user > 1 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` |`o365_suspicious_admin_email_forwarding_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] ====How To Implement==== @@ -3430,7 +3430,7 @@ unknown This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.002/ T1114.002] * '''Last Updated''': 2020-12-15 @@ -3438,18 +3438,18 @@ This search detects the assignment of rights to accesss content from another mai
====Search==== -`o365_management_activity` Operation=Add-MailboxPermission -| spath input=Parameters -| rename User AS src_user, Identity AS dest_user -| search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf -| stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +`o365_management_activity` Operation=Add-MailboxPermission +| spath input=Parameters +| rename User AS src_user, Identity AS dest_user +| search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf +| stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` |`o365_suspicious_rights_delegation_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] ====How To Implement==== @@ -3495,7 +3495,7 @@ Service Accounts This search detects when multiple user configured a forwarding rule to the same destination. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.003/ T1114.003] * '''Last Updated''': 2020-12-16 @@ -3503,19 +3503,19 @@ This search detects when multiple user configured a forwarding rule to the same
====Search==== -`o365_management_activity` Operation=Set-Mailbox -| spath input=Parameters -| rename Identity AS src_user -| search ForwardingSmtpAddress=* -| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress -| where count_src_user > 1 -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +`o365_management_activity` Operation=Set-Mailbox +| spath input=Parameters +| rename Identity AS src_user +| search ForwardingSmtpAddress=* +| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress +| where count_src_user > 1 +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` |`o365_suspicious_user_email_forwarding_filter` ====Associated Analytic Story==== -* Office 365 Detections +* [[Documentation:ESSOC:stories:UseCase#Office_365_Detections|Office 365 Detections]] ====How To Implement==== @@ -3566,7 +3566,7 @@ unknown Detect memory dumping of the LSASS process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] * '''Last Updated''': 2019-12-06 @@ -3574,16 +3574,16 @@ Detect memory dumping of the LSASS process.
====Search==== -`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId -| rename Computer as dest +`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId +| rename Computer as dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -3631,7 +3631,7 @@ Administrators can create memory dumps for debugging purposes, but memory dumps This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1547/ T1547], [https://attack.mitre.org/techniques/T1548/ T1548], [https://attack.mitre.org/techniques/T1554/ T1554], [https://attack.mitre.org/techniques/T1556/ T1556], [https://attack.mitre.org/techniques/T1558/ T1558] * '''Last Updated''': 2020-11-03 @@ -3642,10 +3642,10 @@ This detection indicates use of Mimikatz modules that facilitate Pass-the-Token | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)kerberos::ptt/)=true OR match_regex(cmd_line, /(?i)kerberos::golden/)=true OR match_regex(cmd_line, /(?i)kerberos::silver/)=true OR match_regex(cmd_line, /(?i)misc::skeleton/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -3744,7 +3744,7 @@ None identified. Stolen credentials are applied by methods such as user impersonation, credential injection, spoofing of authentication processes or getting hold of critical accounts. This detection indicates such activities carried out by PowerSploit exploit kit APIs. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1547/ T1547], [https://attack.mitre.org/techniques/T1548/ T1548], [https://attack.mitre.org/techniques/T1554/ T1554], [https://attack.mitre.org/techniques/T1556/ T1556], [https://attack.mitre.org/techniques/T1558/ T1558] * '''Last Updated''': 2020-11-03 @@ -3755,10 +3755,10 @@ Stolen credentials are applied by methods such as user impersonation, credential | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-CredentialInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-TokenManipulation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserImpersonation/)=true OR match_regex(cmd_line, /(?i)Get-System/)=true OR match_regex(cmd_line, /(?i)Invoke-RevertToSelf/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -3855,7 +3855,7 @@ None identified. This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1087/ T1087], [https://attack.mitre.org/techniques/T1201/ T1201], [https://attack.mitre.org/techniques/T1552/ T1552], [https://attack.mitre.org/techniques/T1555/ T1555] * '''Last Updated''': 2020-11-03 @@ -3866,10 +3866,10 @@ This detection identifies use of DSInternals modules that verify password streng | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-PasswordQuality/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -3955,15 +3955,15 @@ Attempt to add a certificate to the certificate store ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter` ====Associated Analytic Story==== -* Disabling Security Tools +* [[Documentation:ESSOC:stories:UseCase#Disabling_Security_Tools|Disabling Security Tools]] ====How To Implement==== @@ -4020,17 +4020,17 @@ Monitor for changes of the ExecutionPolicy in the registry to the values "unrest ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest -| `drop_dm_object_name(Registry)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest +| `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +|`security_content_ctime(lastTime)` | `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter` ====Associated Analytic Story==== -* Malicious PowerShell +* [[Documentation:ESSOC:stories:UseCase#Malicious_PowerShell|Malicious PowerShell]] -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -4087,17 +4087,17 @@ This search looks for attempts to stop security-related services on the endpoint ====Search==== -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -|lookup security_services_lookup service as process OUTPUTNEW category, description -| search category=security +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +|lookup security_services_lookup service as process OUTPUTNEW category, description +| search category=security | `attempt_to_stop_security_service_filter` ====Associated Analytic Story==== -* Disabling Security Tools +* [[Documentation:ESSOC:stories:UseCase#Disabling_Security_Tools|Disabling Security Tools]] ====How To Implement==== @@ -4154,15 +4154,15 @@ Monitor for execution of reg.exe with parameters specifying an export of keys th ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_exe_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -4208,7 +4208,7 @@ None identified. Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-6-04 @@ -4216,18 +4216,18 @@ Monitor for execution of reg.exe with parameters specifying an export of keys th
====Search==== - -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) -| eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) -| where process_name="cmd.exe" OR process_name="reg.exe" -| where cmd_line != null AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) +| where process_name="cmd.exe" OR process_name="reg.exe" +| where cmd_line != null AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -4292,17 +4292,17 @@ This search looks for flags passed to bcdedit.exe modifications to the built-in ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `bcdedit_failure_recovery_modification_filter` ====Associated Analytic Story==== -* Ryuk Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] ====How To Implement==== @@ -4359,17 +4359,17 @@ The search looks for a batch file (.bat) written to the Windows system directory ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` -| rex field=file_name "(?\.[^\.]+)$" -| search file_path=*system32* AND file_extension=.bat +| rex field=file_name "(?\.[^\.]+)$" +| search file_path=*system32* AND file_extension=.bat | `batch_file_write_to_system32_filter` ====Associated Analytic Story==== -* SamSam Ransomware +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] ====How To Implement==== @@ -4416,7 +4416,7 @@ This search looks for arguments to certutil.exe indicating the manipulation or e * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2021-01-26
@@ -4424,17 +4424,17 @@ This search looks for arguments to certutil.exe indicating the manipulation or e ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_filter` ====Associated Analytic Story==== -* Windows Persistence Techniques +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] -* Cloud Federated Credential Abuse +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] ====How To Implement==== @@ -4485,21 +4485,21 @@ The search looks for file modifications with extensions commonly used by Ransomw ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` -| rex field=file_name "(?\.[^\.]+)$" -| `ransomware_extensions` +| rex field=file_name "(?\.[^\.]+)$" +| `ransomware_extensions` | `common_ransomware_extensions_filter` ====Associated Analytic Story==== -* SamSam Ransomware +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] -* Ryuk Ransonware +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransonware|Ryuk Ransonware]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] ====How To Implement==== @@ -4558,20 +4558,20 @@ The search looks for files created with names matching those typically used in r ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `ransomware_notes` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `ransomware_notes` | `common_ransomware_notes_filter` ====Associated Analytic Story==== -* SamSam Ransomware +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] -* Ryuk Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]] ====How To Implement==== @@ -4617,7 +4617,7 @@ It's possible that a legitimate file could be created with the same name used by Detect remote thread creation into LSASS consistent with credential dumping. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] * '''Last Updated''': 2019-12-06 @@ -4625,16 +4625,16 @@ Detect remote thread creation into LSASS consistent with credential dumping.
====Search==== -`sysmon` EventID=8 TargetImage=*lsass.exe -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId -| rename Computer as dest +`sysmon` EventID=8 TargetImage=*lsass.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId +| rename Computer as dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `create_remote_thread_into_lsass_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -4691,15 +4691,15 @@ This search looks for the creation of local administrator accounts using net.exe ====Search==== -| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` |`create_local_admin_accounts_using_net_exe_filter` ====Associated Analytic Story==== -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] ====How To Implement==== @@ -4758,16 +4758,16 @@ This search looks for the creation or deletion of hidden shares using net.exe. ====Search==== -| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| search process=*share* +| `security_content_ctime(lastTime)` +| search process=*share* | `create_or_delete_windows_shares_using_net_exe_filter` ====Associated Analytic Story==== -* Hidden Cobra Malware +* [[Documentation:ESSOC:stories:UseCase#Hidden_Cobra_Malware|Hidden Cobra Malware]] ====How To Implement==== @@ -4824,15 +4824,15 @@ Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -4889,15 +4889,15 @@ This search detects the use of wmic and Powershell to create a shadow copy. ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_with_wmic_and_powershell_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -4945,7 +4945,7 @@ Legtimate administrator usage of wmic to create a shadow copy. Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] * '''Last Updated''': 2020-02-03 @@ -4953,16 +4953,16 @@ Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess
====Search==== -`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `creation_of_lsass_dump_with_taskmgr_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -5023,15 +5023,15 @@ This search detects credential dumping using copy command from a shadow copy. ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `credential_dumping_via_copy_command_from_shadow_copy_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -5088,15 +5088,15 @@ This search detects the creation of a symlink to a shadow copy. ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `credential_dumping_via_symlink_to_shadow_copy_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -5144,7 +5144,7 @@ unknown Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-18 @@ -5152,13 +5152,13 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND process_name != null AND parent_process_name != null AND match_regex(parent_process_name, /(?i)System32\\services.exe/)=true AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-s/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5219,7 +5219,7 @@ None identified. Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-18 @@ -5227,13 +5227,13 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null) | where cmd_line != null AND process_name != null AND process_path != null AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-v/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5292,7 +5292,7 @@ None identified. Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003], [https://attack.mitre.org/techniques/T1555/ T1555] * '''Last Updated''': 2020-10-18 @@ -5300,13 +5300,13 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)all\s+\-oA\s+\-output/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5365,7 +5365,7 @@ None identified. Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-21 @@ -5376,10 +5376,10 @@ Credential extraction is often an illegal recovery of credential material from s | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)ConvertFrom-ADManagedPasswordBlob/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-UnicodePassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-KerberosKey/)=true OR match_regex(cmd_line, /(?i)ConvertTo-LMHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-NTHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-OrgIdHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-UnicodePassword/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5442,7 +5442,7 @@ None identified. Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-21 @@ -5453,10 +5453,10 @@ Credential extraction is often an illegal recovery of credential material from s | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ADDBBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Get-ADDBKdsRootKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBSchemaAttribute/)=true OR match_regex(cmd_line, /(?i)Get-ADKeyCredential/)=true OR match_regex(cmd_line, /(?i)Get-ADReplAccount/)=true OR match_regex(cmd_line, /(?i)Get-ADReplBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADSIAccount/)=true OR match_regex(cmd_line, /(?i)Get-AzureADUserEx/)=true OR match_regex(cmd_line, /(?i)Get-BootKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaPolicyInformation/)=true OR match_regex(cmd_line, /(?i)Get-SamPasswordPolicy/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5519,7 +5519,7 @@ None identified. Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-21 @@ -5530,10 +5530,10 @@ Credential extraction is often an illegal recovery of credential material from s | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)CRYPTO::Certificates/)=true OR match_regex(cmd_line, /(?i)CRYPTO::keys/)=true OR match_regex(cmd_line, /(?i)kerberos::list/)=true OR match_regex(cmd_line, /(?i)kerberos::tgt/)=true OR match_regex(cmd_line, /(?i)lsadump::sam/)=true OR match_regex(cmd_line, /(?i)lsadump::secrets/)=true OR match_regex(cmd_line, /(?i)lsadump::cache/)=true OR match_regex(cmd_line, /(?i)lsadump::lsa/)=true OR match_regex(cmd_line, /(?i)lsadump::trust/)=true OR match_regex(cmd_line, /(?i)lsadump::backupkeys/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5590,7 +5590,7 @@ None identified. Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-21 @@ -5601,10 +5601,10 @@ Credential extraction is often an illegal recovery of credential material from s | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ApplicationHost/)=true OR match_regex(cmd_line, /(?i)Get-CachedGPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-GPPAutologon/)=true OR match_regex(cmd_line, /(?i)Get-GPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-RegistryAutoLogon/)=true OR match_regex(cmd_line, /(?i)Get-SiteListPassword/)=true OR match_regex(cmd_line, /(?i)Get-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Request-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Get-VaultCredential/)=true OR match_regex(cmd_line, /(?i)Invoke-Kerberoast/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5661,7 +5661,7 @@ None identified. Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-18 @@ -5669,13 +5669,13 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND parent_process_name != null AND process_name != null AND ( match_regex(parent_process_name, /(?i)ntkd\.exe/)=true OR match_regex(parent_process_name, /(?i)livekd\.exe/)=true ) AND match_regex(process_name, /(?i)conhost\.exe/)=true AND match_regex(cmd_line, /(?i)0xffffffff/)=true AND match_regex(cmd_line, /(?i)\-ForceV1/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5736,7 +5736,7 @@ Although unlikely, using debuggers this way may be indicative of developers anal Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-18 @@ -5744,13 +5744,13 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) | where cmd_line != null AND process_name != null AND ( match_regex(process_name, /^(?i)ntkd\.exe/)=true OR match_regex(process_name, /^(?i)kd\.exe/)=true ) AND match_regex(cmd_line, /(?i)\-z\s+/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5807,7 +5807,7 @@ Although unlikely, using debuggers this way may be indicative of developers anal Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] * '''Last Updated''': 2020-10-18 @@ -5815,14 +5815,14 @@ Credential extraction is often an illegal recovery of credential material from s
====Search==== - + | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND match_regex(cmd_line, /(?i)Get-ADDBAccount/)=true AND match_regex(cmd_line, /(?i)\-dbpath[\s;:\.\ |]+/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -5886,19 +5886,19 @@ The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `deleting_shadow_copies_filter` ====Associated Analytic Story==== -* Windows Log Manipulation +* [[Documentation:ESSOC:stories:UseCase#Windows_Log_Manipulation|Windows Log Manipulation]] -* SamSam Ransomware +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] ====How To Implement==== @@ -5944,7 +5944,7 @@ vssadmin.exe and wmic.exe are standard applications shipped with modern versions This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1550.002/ T1550.002] * '''Last Updated''': 2020-10-15 @@ -5952,16 +5952,16 @@ This search looks for specific authentication events from the Windows Security E
====Search==== -`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) -| fillnull -| stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest +`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) +| fillnull +| stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `detect_activity_related_to_pass_the_hash_attacks_filter` ====Associated Analytic Story==== -* Lateral Movement +* [[Documentation:ESSOC:stories:UseCase#Lateral_Movement|Lateral Movement]] ====How To Implement==== @@ -6007,7 +6007,7 @@ Legitimate logon activity by authorized NTLM systems may be detected by this sea This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1210/ T1210] * '''Last Updated''': 2020-09-18 @@ -6015,13 +6015,13 @@ This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An ac
====Search==== -`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 -| stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user +`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 +| stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter` ====Associated Analytic Story==== -* Detect Zerologon Attack +* [[Documentation:ESSOC:stories:UseCase#Detect_Zerologon_Attack|Detect Zerologon Attack]] ====How To Implement==== @@ -6067,7 +6067,7 @@ None thus far found This search looks for reading lsass memory consistent with credential dumping. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] * '''Last Updated''': 2019-12-03 @@ -6075,18 +6075,18 @@ This search looks for reading lsass memory consistent with credential dumping.
====Search==== -`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess -| rename Computer as dest +`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess +| rename Computer as dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `detect_credential_dumping_through_lsass_access_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] -* Detect Zerologon Attack +* [[Documentation:ESSOC:stories:UseCase#Detect_Zerologon_Attack|Detect Zerologon Attack]] ====How To Implement==== @@ -6132,7 +6132,7 @@ The activity may be legitimate. Other tools can access lsass for legitimate reas This search detects the memory of lsass.exe being dumped for offline credential theft attack. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.003/ T1003.003] * '''Last Updated''': 2020-09-15 @@ -6141,15 +6141,15 @@ This search detects the memory of lsass.exe being dumped for offline credential ====Search==== -| from read_ssa_enriched_events() -| eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)) -| where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body = "TBD" +| from read_ssa_enriched_events() +| eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)) +| where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -6214,17 +6214,17 @@ This search identifies endpoints that have caused a relatively high number of ac ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result -|`drop_dm_object_name("All_Changes")` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result +|`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| search count > 5 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter` ====Associated Analytic Story==== -* Account Monitoring and Controls +* [[Documentation:ESSOC:stories:UseCase#Account_Monitoring_and_Controls|Account Monitoring and Controls]] ====How To Implement==== @@ -6283,17 +6283,17 @@ This search detects user accounts that have been locked out a relatively high nu ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result -|`drop_dm_object_name("All_Changes")` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result +|`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| search count > 5 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search count > 5 | `detect_excessive_user_account_lockouts_filter` ====Associated Analytic Story==== -* Account Monitoring and Controls +* [[Documentation:ESSOC:stories:UseCase#Account_Monitoring_and_Controls|Account Monitoring and Controls]] ====How To Implement==== @@ -6339,7 +6339,7 @@ It is possible that a legitimate user is experiencing an issue causing multiple The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.001/ T1218.001] * '''Last Updated''': 2021-02-11 @@ -6347,16 +6347,16 @@ The following analytic identifies a renamed instance of hh.exe (HTML Help) execu
====Search==== -`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_html_help_renamed_filter` ====Associated Analytic Story==== -* Suspicious Compiled HTML Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Compiled_HTML_Activity|Suspicious Compiled HTML Activity]] ====How To Implement==== @@ -6417,15 +6417,15 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_html_help_spawn_child_process_filter` ====Associated Analytic Story==== -* Suspicious Compiled HTML Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Compiled_HTML_Activity|Suspicious Compiled HTML Activity]] ====How To Implement==== @@ -6490,15 +6490,15 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_html_help_url_in_command_line_filter` ====Associated Analytic Story==== -* Suspicious Compiled HTML Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Compiled_HTML_Activity|Suspicious Compiled HTML Activity]] ====How To Implement==== @@ -6565,15 +6565,15 @@ The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTM ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_html_help_using_infotech_storage_handlers_filter` ====Associated Analytic Story==== -* Suspicious Compiled HTML Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Compiled_HTML_Activity|Suspicious Compiled HTML Activity]] ====How To Implement==== @@ -6631,7 +6631,7 @@ It is rare to see instances of InfoTech Storage Handlers being used, but it does This search detects a potential kerberoasting attack via service principal name requests * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1558.003/ T1558.003] * '''Last Updated''': 2020-10-21 @@ -6639,14 +6639,14 @@ This search detects a potential kerberoasting attack via service principal name
====Search==== - -| from read_ssa_enriched_events() -| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id") -| where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" -| first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] -| where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID -| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" -| select start_time, end_time, entities, body + +| from read_ssa_enriched_events() +| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id") +| where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" +| first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] +| where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID +| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" +| select start_time, end_time, entities, body | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -6716,15 +6716,15 @@ This analytic identifies when Microsoft HTML Application Host (mshta.exe) utilit ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter` ====Associated Analytic Story==== -* Suspicious MSHTA Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_MSHTA_Activity|Suspicious MSHTA Activity]] ====How To Implement==== @@ -6776,7 +6776,7 @@ It is possible legitimate applications may perform this behavior and will need t This search looks for newly created accounts that have been elevated to local administrators. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.001/ T1136.001] * '''Last Updated''': 2020-07-08 @@ -6784,17 +6784,17 @@ This search looks for newly created accounts that have been elevated to local ad
====Search==== -`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) -| transaction member_id connected=false maxspan=180m -| rename member_id as user -| stats count min(_time) as firstTime max(_time) as lastTime by user dest +`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) +| transaction member_id connected=false maxspan=180m +| rename member_id as user +| stats count min(_time) as firstTime max(_time) as lastTime by user dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `detect_new_local_admin_account_filter` ====Associated Analytic Story==== -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] ====How To Implement==== @@ -6846,7 +6846,7 @@ The activity may be legitimate. For this reason, it's best to verify the account This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1550.002/ T1550.002] * '''Last Updated''': 2020-10-21 @@ -6854,14 +6854,14 @@ This search looks for specific authentication events from the Windows Security E
====Search==== - -| from read_ssa_enriched_events() -| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), LogonType=map_get(input_event, "logon_type"), LogonProcess=map_get(input_event, "logon_process"), ComputerName=map_get(input_event, "dest_ip_primary_artifact"), AccountName=map_get(input_event, "dest_user_primary_artifact") -| where (LogonType="3" AND LogonProcess="NtLmSsp" AND AccountName IS NOT NULL) OR (LogonType="9" AND LogonProcess="seclogo") -| first_time_event input_columns=["EventCode","LogonProcess","ComputerName"] -| where first_time_EventCode_LogonProcess_ComputerName -| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" -| select start_time, end_time, entities, body + +| from read_ssa_enriched_events() +| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), LogonType=map_get(input_event, "logon_type"), LogonProcess=map_get(input_event, "logon_process"), ComputerName=map_get(input_event, "dest_ip_primary_artifact"), AccountName=map_get(input_event, "dest_user_primary_artifact") +| where (LogonType="3" AND LogonProcess="NtLmSsp" AND AccountName IS NOT NULL) OR (LogonType="9" AND LogonProcess="seclogo") +| first_time_event input_columns=["EventCode","LogonProcess","ComputerName"] +| where first_time_EventCode_LogonProcess_ComputerName +| eval start_time=_time, end_time=_time, body="TBD", entities="TBD" +| select start_time, end_time, entities, body | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -6919,7 +6919,7 @@ Legitimate logon activity by authorized NTLM systems may be detected by this sea ---- ===Detect Path Interception By Creation Of program exe=== -The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. +The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint @@ -6931,22 +6931,22 @@ The detection Detect Path Interception By Creation Of program exe is detecting t ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest index -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest index +| `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?[^\\\\]*\.(?:exe |bat |com -|ps1))" -| eval process_name = lower(process_name) -| eval service_process = lower(service_process) -| where process_name != service_process -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +|ps1))" +| eval process_name = lower(process_name) +| eval service_process = lower(service_process) +| where process_name != service_process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter` ====Associated Analytic Story==== -* Windows Persistence Techniques +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] ====How To Implement==== @@ -7004,21 +7004,21 @@ This search looks for executions of cmd.exe spawned by a process that is often a ====Search==== | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -|search [`prohibited_apps_launching_cmd`] +| `security_content_ctime(lastTime)` +|search [`prohibited_apps_launching_cmd`] | `detect_prohibited_applications_spawning_cmd_exe_filter` ====Associated Analytic Story==== -* Suspicious Command-Line Executions +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Command-Line_Executions|Suspicious Command-Line Executions]] -* Suspicious MSHTA Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_MSHTA_Activity|Suspicious MSHTA Activity]] -* Suspicious Zoom Child Processes +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Zoom_Child_Processes|Suspicious Zoom Child Processes]] -* Sunburst Malware +* [[Documentation:ESSOC:stories:UseCase#Sunburst_Malware|Sunburst Malware]] ====How To Implement==== @@ -7064,7 +7064,7 @@ There are circumstances where an application may legitimately execute and intera This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. This is a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe` by @bpatel. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1059/ T1059] * '''Last Updated''': 2020-7-13 @@ -7075,14 +7075,14 @@ This search looks for executions of cmd.exe spawned by a process that is often a | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) -| where process_name="cmd.exe" -| rex field=parent_process "(?[^\\\\]+)$" +| where process_name="cmd.exe" +| rex field=parent_process "(?[^\\\\]+)$" | where field0="winword.exe" OR field0="excel.exe" OR field0="outlook.exe" OR field0="powerpnt.exe" OR field0="visio.exe" OR field0="mspub.exe" OR field0="acrobat.exe" OR field0="acrord32.exe" OR field0="chrome.exe" OR field0="iexplore.exe" OR field0="opera.exe" OR field0="firefox.exe" OR field0="java.exe" OR field0="powershell.exe" -| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body="TBD" +| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body="TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -7148,17 +7148,17 @@ This search looks for events where `PsExec.exe` is run with the `accepteula` fla ====Search==== -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest Processes.parent_process_name +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter` ====Associated Analytic Story==== -* SamSam Ransomware +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] ====How To Implement==== @@ -7205,7 +7205,7 @@ This search will return a table of rare processes, the names of the systems runn * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2020-03-16
@@ -7213,26 +7213,26 @@ This search will return a table of rare processes, the names of the systems runn ====Search==== -| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name -| rename Processes.process_name as process -| rex field=user "(?.*)\\\\(?.*)" +| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name +| rename Processes.process_name as process +| rex field=user "(?.*)\\\\(?.*)" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [ -| tstats count from datamodel=Endpoint.Processes by Processes.process_name -| rare Processes.process_name limit=30 +| tstats count from datamodel=Endpoint.Processes by Processes.process_name +| rare Processes.process_name limit=30 | rename Processes.process_name as process | `filter_rare_process_allow_list` -| table process ] +| table process ] | `detect_rare_executables_filter` ====Associated Analytic Story==== -* Emotet Malware DHS Report TA18-201A +* [[Documentation:ESSOC:stories:UseCase#Emotet_Malware__DHS_Report_TA18-201A_|Emotet Malware DHS Report TA18-201A ]] -* Unusual Processes +* [[Documentation:ESSOC:stories:UseCase#Unusual_Processes|Unusual Processes]] -* Cloud Federated Credential Abuse +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] ====How To Implement==== @@ -7285,15 +7285,15 @@ The following analytic identifies regasm.exe spawning a process. This particular ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_regasm_spawning_a_process_filter` ====Associated Analytic Story==== -* Suspicious Regsvcs Regasm Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Regsvcs_Regasm_Activity|Suspicious Regsvcs Regasm Activity]] ====How To Implement==== @@ -7347,7 +7347,7 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] * '''Last Updated''': 2021-02-16 @@ -7355,16 +7355,16 @@ The following analytic identifies regasm.exe with a network connection to a publ
====Search==== -`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe -| rename Computer as dest -| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_regasm_with_network_connection_filter` ====Associated Analytic Story==== -* Suspicious Regsvcs Regasm Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Regsvcs_Regasm_Activity|Suspicious Regsvcs Regasm Activity]] ====How To Implement==== @@ -7416,7 +7416,7 @@ Although unlikely, limited instances of regasm.exe with a network connection may The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] * '''Last Updated''': 2021-02-12 @@ -7424,17 +7424,17 @@ The following analytic identifies regasm.exe with no command line arguments. Thi
====Search==== -`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) -| regex CommandLine="(regasm\.exe.{0,4}$)" -| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) +| regex CommandLine="(regasm\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_regasm_with_no_command_line_arguments_filter` ====Associated Analytic Story==== -* Suspicious Regsvcs Regasm Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Regsvcs_Regasm_Activity|Suspicious Regsvcs Regasm Activity]] ====How To Implement==== @@ -7495,15 +7495,15 @@ The following analytic identifies regsvcs.exe spawning a process. This particula ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_regsvcs_spawning_a_process_filter` ====Associated Analytic Story==== -* Suspicious Regsvcs Regasm Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Regsvcs_Regasm_Activity|Suspicious Regsvcs Regasm Activity]] ====How To Implement==== @@ -7555,7 +7555,7 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] * '''Last Updated''': 2021-02-16 @@ -7563,16 +7563,16 @@ The following analytic identifies Regsvcs.exe with a network connection to a pub
====Search==== -`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe -| rename Computer as dest -| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_regsvcs_with_network_connection_filter` ====Associated Analytic Story==== -* Suspicious Regsvcs Regasm Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Regsvcs_Regasm_Activity|Suspicious Regsvcs Regasm Activity]] ====How To Implement==== @@ -7624,7 +7624,7 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive. The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] * '''Last Updated''': 2021-02-12 @@ -7632,17 +7632,17 @@ The following analytic identifies regsvcs.exe with no command line arguments. Th
====Search==== -`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) -| regex CommandLine="(regsvcs\.exe.{0,4}$)" -| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) +| regex CommandLine="(regsvcs\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_regsvcs_with_no_command_line_arguments_filter` ====Associated Analytic Story==== -* Suspicious Regsvcs Regasm Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Regsvcs_Regasm_Activity|Suspicious Regsvcs Regasm Activity]] ====How To Implement==== @@ -7692,7 +7692,7 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive. ===Detect Regsvr32 Application Control Bypass=== Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ -Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. +Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint @@ -7704,15 +7704,15 @@ Upon investigating, look for network connections to remote destinations (interna ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `detect_regsvr32_application_control_bypass_filter` ====Associated Analytic Story==== -* Suspicious Regsvr32 Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Regsvr32_Activity|Suspicious Regsvr32 Activity]] ====How To Implement==== @@ -7775,15 +7775,15 @@ The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___advpack_filter` ====Associated Analytic Story==== -* Suspicious Rundll32 Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Rundll32_Activity|Suspicious Rundll32 Activity]] ====How To Implement==== @@ -7848,15 +7848,15 @@ The following analytic identifies rundll32.exe loading setupapi.dll and iesetupa ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___setupapi_filter` ====Associated Analytic Story==== -* Suspicious Rundll32 Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Rundll32_Activity|Suspicious Rundll32 Activity]] ====How To Implement==== @@ -7921,15 +7921,15 @@ The following analytic identifies rundll32.exe loading syssetup.dll by calling t ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___syssetup_filter` ====Associated Analytic Story==== -* Suspicious Rundll32 Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Rundll32_Activity|Suspicious Rundll32 Activity]] ====How To Implement==== @@ -7994,15 +7994,15 @@ The following analytic identifies "rundll32.exe" execution with inline protocol ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `detect_rundll32_inline_hta_execution_filter` ====Associated Analytic Story==== -* Suspicious MSHTA Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_MSHTA_Activity|Suspicious MSHTA Activity]] ====How To Implement==== @@ -8063,17 +8063,17 @@ This search looks for the execution of the cscript.exe or wscript.exe processes, ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest -| `drop_dm_object_name("Processes")` +| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +|`security_content_ctime(lastTime)` | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` ====Associated Analytic Story==== -* Emotet Malware DHS Report TA18-201A +* [[Documentation:ESSOC:stories:UseCase#Emotet_Malware__DHS_Report_TA18-201A_|Emotet Malware DHS Report TA18-201A ]] -* Suspicious Command-Line Executions +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Command-Line_Executions|Suspicious Command-Line Executions]] ====How To Implement==== @@ -8128,15 +8128,15 @@ The following analytic identifies "mshta.exe" execution with inline protocol han ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `detect_mshta_inline_hta_execution_filter` ====Associated Analytic Story==== -* Suspicious MSHTA Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_MSHTA_Activity|Suspicious MSHTA Activity]] ====How To Implement==== @@ -8188,7 +8188,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.005/ T1218.005] * '''Last Updated''': 2021-01-20 @@ -8196,16 +8196,16 @@ The following analytic identifies renamed instances of mshta.exe executing. Msht
====Search==== -`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest +`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_mshta_renamed_filter` ====Associated Analytic Story==== -* Suspicious MSHTA Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_MSHTA_Activity|Suspicious MSHTA Activity]] ====How To Implement==== @@ -8264,19 +8264,19 @@ This search looks for fast execution of processes used for system network config ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name(Processes)` -| search `system_network_configuration_discovery_tools` -| transaction dest connected=false maxpause=5m -|where eventcount>=5 -| table firstTime lastTime dest user process_name process parent_process eventcount +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| search `system_network_configuration_discovery_tools` +| transaction dest connected=false maxpause=5m +|where eventcount>=5 +| table firstTime lastTime dest user process_name process parent_process eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter` ====Associated Analytic Story==== -* Unusual Processes +* [[Documentation:ESSOC:stories:UseCase#Unusual_Processes|Unusual Processes]] ====How To Implement==== @@ -8326,7 +8326,7 @@ It is uncommon for normal users to execute a series of commands used for network The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1548.002/ T1548.002] * '''Last Updated''': 2020-11-18 @@ -8335,15 +8335,15 @@ The search looks for modifications to registry keys that control the enforcement ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action -| `drop_dm_object_name(Registry)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action +| `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter` ====Associated Analytic Story==== -* Windows Defense Evasion Tactics +* [[Documentation:ESSOC:stories:UseCase#Windows_Defense_Evasion_Tactics|Windows Defense Evasion Tactics]] -* Suspicious Windows Registry Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Windows_Registry_Activities|Suspicious Windows Registry Activities]] ====How To Implement==== @@ -8398,17 +8398,17 @@ Detect the usage of comsvcs.dll for dumping the lsass process. ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `dump_lsass_via_comsvcs_dll_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] -* Suspicious Rundll32 Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Rundll32_Activity|Suspicious Rundll32 Activity]] ====How To Implement==== @@ -8468,15 +8468,15 @@ During triage, confirm this is procdump.exe executing. If it is the first time a ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -8529,7 +8529,7 @@ Detect a renamed instance of procdump.exe dumping the lsass process. This query During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] * '''Last Updated''': 2021-02-01 @@ -8537,16 +8537,16 @@ During triage, confirm this is procdump.exe executing. If it is the first time a
====Search==== -`sysmon` OriginalFileName=procdump process_name!=procdump*.exe EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* -| rename Computer as dest -| stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`sysmon` OriginalFileName=procdump process_name!=procdump*.exe EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* +| rename Computer as dest +| stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_rename_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -8607,15 +8607,15 @@ This search looks for processes launched from files that have double extensions ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` | `execution_of_file_with_multiple_extensions_filter` ====Associated Analytic Story==== -* Windows File Extension and Association Abuse +* [[Documentation:ESSOC:stories:UseCase#Windows_File_Extension_and_Association_Abuse|Windows File Extension and Association Abuse]] ====How To Implement==== @@ -8662,7 +8662,7 @@ The search looks for file writes with extensions consistent with a SamSam ransom * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2018-12-14
@@ -8670,17 +8670,17 @@ The search looks for file writes with extensions consistent with a SamSam ransom ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` -| rex field=file_name "(?\.[^\.]+)$" -| search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml +| rex field=file_name "(?\.[^\.]+)$" +| search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml | `file_with_samsam_extension_filter` ====Associated Analytic Story==== -* SamSam Ransomware +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] ====How To Implement==== @@ -8731,17 +8731,17 @@ This search looks for child processes spawned by zoom.exe or zoom.us that has no ====Search==== -| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest -| `drop_dm_object_name(Processes)` -| lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen -| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") -| `security_content_ctime(firstTime)` -| table firstTime dest, process_id, process_name, parent_process_id, parent_process_name +| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest +| `drop_dm_object_name(Processes)` +| lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen +| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") +| `security_content_ctime(firstTime)` +| table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter` ====Associated Analytic Story==== -* Suspicious Zoom Child Processes +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Zoom_Child_Processes|Suspicious Zoom Child Processes]] ====How To Implement==== @@ -8787,7 +8787,7 @@ A new child process of zoom isn't malicious by that fact alone. Further investig This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1059/ T1059], [https://attack.mitre.org/techniques/T1117/ T1117], [https://attack.mitre.org/techniques/T1202/ T1202] * '''Last Updated''': 2021-2-1 @@ -8796,16 +8796,16 @@ This search looks for command-line arguments that use a `/c` parameter to execut ====Search==== -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) | eval dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), cmd_line_norm=lower(cmd_line), cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/, "GUID"), cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s |$)+)/, "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s -|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N") -| where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true -| select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id -| first_time_event input_columns=["cmd_line_norm"] -| where first_time_cmd_line_norm -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N") +| where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true +| select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id +| first_time_event input_columns=["cmd_line_norm"] +| where first_time_cmd_line_norm +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -8837,9 +8837,9 @@ You must be populating the endpoint data model for SSA and specifically the proc | Command and Scripting Interpreter | Execution |- -| -| -| +| +| +| |- | T1202 | Indirect Command Execution @@ -8881,17 +8881,17 @@ Attackers leverage an existing Windows binary, attrib.exe, to mark specific as h ====Search==== -| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest -| `drop_dm_object_name("Processes")` +| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `hiding_files_and_directories_with_attrib_exe_filter` ====Associated Analytic Story==== -* Windows Defense Evasion Tactics +* [[Documentation:ESSOC:stories:UseCase#Windows_Defense_Evasion_Tactics|Windows Defense Evasion Tactics]] -* Windows Persistence Techniques +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] ====How To Implement==== @@ -8917,7 +8917,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Known False Positives==== -Some applications and users may legitimately use attrib.exe to interact with the files. +Some applications and users may legitimately use attrib.exe to interact with the files. ====Reference==== @@ -8937,7 +8937,7 @@ Some applications and users may legitimately use attrib.exe to interact with the This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1021/ T1021], [https://attack.mitre.org/techniques/T1113/ T1113], [https://attack.mitre.org/techniques/T1123/ T1123], [https://attack.mitre.org/techniques/T1563/ T1563] * '''Last Updated''': 2020-11-09 @@ -8948,10 +8948,10 @@ This detection identifies access to PowerSploit modules that enable illegaly acc | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-HttpStatus/)=true OR match_regex(cmd_line, /(?i)Get-Keystrokes/)=true OR match_regex(cmd_line, /(?i)Get-MicrophoneAudio/)=true OR match_regex(cmd_line, /(?i)Get-NetRDPSession/)=true OR match_regex(cmd_line, /(?i)Get-TimedScreenshot/)=true OR match_regex(cmd_line, /(?i)Get-WebConfig/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9020,7 +9020,7 @@ None identified. This detection identifies access to PowerSploit modules that create accounts illegaly. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1585/ T1585] * '''Last Updated''': 2020-11-09 @@ -9031,10 +9031,10 @@ This detection identifies access to PowerSploit modules that create accounts ill | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)New-DomainUser/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9091,7 +9091,7 @@ None identified. This detection identifies access to PowerSploit modules that delete event logs. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1070/ T1070] * '''Last Updated''': 2020-11-09 @@ -9102,10 +9102,10 @@ This detection identifies access to PowerSploit modules that delete event logs. | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)event::drop/)=true OR match_regex(cmd_line, /(?i)event::clear/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9162,7 +9162,7 @@ None identified. This detection identifies use of DSInternals modules that enable or disable accounts illegaly. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-09 @@ -9173,10 +9173,10 @@ This detection identifies use of DSInternals modules that enable or disable acco | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Disable-ADDBAccount/)=true OR match_regex(cmd_line, /(?i)Enable-ADDBAccount/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9237,7 +9237,7 @@ None identified. This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1207/ T1207], [https://attack.mitre.org/techniques/T1484/ T1484] * '''Last Updated''': 2020-11-09 @@ -9248,10 +9248,10 @@ This detection identifies use of DSInternals modules for illegal management of A | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Remove-ADDBObject/)=true OR match_regex(cmd_line, /(?i)Set-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Set-ADDBPrimaryGroup/)=true OR match_regex(cmd_line, /(?i)Set-LsaPolicyInformation/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9316,7 +9316,7 @@ None identified. This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1098/ T1098], [https://attack.mitre.org/techniques/T1207/ T1207], [https://attack.mitre.org/techniques/T1484/ T1484] * '''Last Updated''': 2020-11-09 @@ -9327,11 +9327,11 @@ This detection identifies access to PowerSploit modules that enable illegal mana | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainObject/)=true OR match_regex(cmd_line, /(?i)Set-ADObject/)=true OR match_regex(cmd_line, /(?i)Set-DomainObjectOwner/)=true OR match_regex(cmd_line, /(?i)Set-MasterBootRecord/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9396,7 +9396,7 @@ None identified. This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1053/ T1053], [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1548/ T1548] * '''Last Updated''': 2020-11-09 @@ -9407,10 +9407,10 @@ This detection identifies access to PowerSploit modules that illegaly elevate ge | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Add-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Enable-Privilege/)=true OR match_regex(cmd_line, /(?i)New-ElevatedPersistenceOption/)=true OR match_regex(cmd_line, /(?i)New-UserPersistenceOption/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9475,7 +9475,7 @@ None identified. This detection identifies use of Mimikatz modules for illegal privilege elevation. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1134/ T1134], [https://attack.mitre.org/techniques/T1548/ T1548] * '''Last Updated''': 2020-11-09 @@ -9486,10 +9486,10 @@ This detection identifies use of Mimikatz modules for illegal privilege elevatio | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)privilege::debug/)=true OR match_regex(cmd_line, /(?i)token::elevate/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9550,7 +9550,7 @@ None identified. This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1106/ T1106], [https://attack.mitre.org/techniques/T1569/ T1569] * '''Last Updated''': 2020-11-09 @@ -9561,10 +9561,10 @@ This detection identifies use of Mimikatz modules for illegal control over servi | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::start/)=true OR match_regex(cmd_line, /(?i)service::\+/)=true OR match_regex(cmd_line, /(?i)service::\-/)=true OR match_regex(cmd_line, /(?i)service::start/)=true OR match_regex(cmd_line, /(?i)service::stop/)=true OR match_regex(cmd_line, /(?i)service::suspend/)=true OR match_regex(cmd_line, /(?i)misc::memssp/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9629,7 +9629,7 @@ None identified. This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1106/ T1106], [https://attack.mitre.org/techniques/T1569/ T1569] * '''Last Updated''': 2020-11-09 @@ -9640,11 +9640,11 @@ This detection identifies access to PowerSploit modules that enable illegal cont | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Install-SSP/)=true OR match_regex(cmd_line, /(?i)Set-CriticalProcess/)=true OR match_regex(cmd_line, /(?i)Install-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Restore-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Write-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Set-ServiceBinaryPath/)=true OR match_regex(cmd_line, /(?i)Invoke-ReflectivePEInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-DllInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-ServiceAbuse/)=true OR match_regex(cmd_line, /(?i)Invoke-Shellcode/)=true OR match_regex(cmd_line, /(?i)Invoke-WScriptUACBypass/)=true OR match_regex(cmd_line, /(?i)Invoke-WmiCommand/)=true OR match_regex(cmd_line, /(?i)Write-HijackDll/)=true OR match_regex(cmd_line, /(?i)Add-ServiceDacl/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -9709,7 +9709,7 @@ None identified. This search detects a potential kerberoasting attack via service principal name requests * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1558.003/ T1558.003] * '''Last Updated''': 2020-10-16 @@ -9717,15 +9717,15 @@ This search detects a potential kerberoasting attack via service principal name
====Search==== -`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 -| stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` +`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 +| stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` | `kerberoasting_spn_request_with_rc4_encryption_filter` ====Associated Analytic Story==== -* Lateral Movement +* [[Documentation:ESSOC:stories:UseCase#Lateral_Movement|Lateral Movement]] ====How To Implement==== @@ -9784,17 +9784,17 @@ This search looks for PowerShell processes started with parameters to modify the ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` ====Associated Analytic Story==== -* Malicious PowerShell +* [[Documentation:ESSOC:stories:UseCase#Malicious_PowerShell|Malicious PowerShell]] -* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns +* [[Documentation:ESSOC:stories:UseCase#Possible_Backdoor_Activity_Associated_With_MUDCARP_Espionage_Campaigns|Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns]] ====How To Implement==== @@ -9851,17 +9851,17 @@ This search looks for PowerShell processes that have encoded the script within t ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `malicious_powershell_process___encoded_command_filter` ====Associated Analytic Story==== -* Malicious PowerShell +* [[Documentation:ESSOC:stories:UseCase#Malicious_PowerShell|Malicious PowerShell]] -* Sunburst Malware +* [[Documentation:ESSOC:stories:UseCase#Sunburst_Malware|Sunburst Malware]] ====How To Implement==== @@ -9918,15 +9918,15 @@ This search looks for PowerShell processes started with parameters used to bypas ====Search==== -| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter` ====Associated Analytic Story==== -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] ====How To Implement==== @@ -9983,17 +9983,17 @@ This search looks for PowerShell processes launched with arguments that have cha ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) -| `malicious_powershell_process_with_obfuscation_techniques_filter` +| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) +| `malicious_powershell_process_with_obfuscation_techniques_filter` | search num_obfuscation > 10 ====Associated Analytic Story==== -* Malicious PowerShell +* [[Documentation:ESSOC:stories:UseCase#Malicious_PowerShell|Malicious PowerShell]] ====How To Implement==== @@ -10041,7 +10041,7 @@ These characters might be legitimately on the command-line, but it is not common This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1547.010/ T1547.010] * '''Last Updated''': 2020-11-23 @@ -10050,15 +10050,15 @@ This search looks for registry activity associated with modifications to the reg ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action -| `drop_dm_object_name(Registry)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action +| `drop_dm_object_name(Registry)` | `monitor_registry_keys_for_print_monitors_filter` ====Associated Analytic Story==== -* Suspicious Windows Registry Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Windows_Registry_Activities|Suspicious Windows Registry Activities]] -* Windows Persistence Techniques +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] ====How To Implement==== @@ -10104,7 +10104,7 @@ You will encounter noise from legitimate print-monitor registry entries. Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1059/ T1059], [https://attack.mitre.org/techniques/T1053/ T1053] * '''Last Updated''': 2020-08-25 @@ -10112,16 +10112,16 @@ Attacker activity may compromise executing several LOLBAS applications in conjun
====Search==== - -| from read_ssa_enriched_events() -| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) -| where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" -| stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) -| eval lolbas_counter=lolbas_counter*1.0 -| rename window_end as timestamp -| adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L -| where label AND quantile>0.99 -| eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body = "TBD" + +| from read_ssa_enriched_events() +| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" +| stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) +| eval lolbas_counter=lolbas_counter*1.0 +| rename window_end as timestamp +| adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L +| where label AND quantile>0.99 +| eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -10190,15 +10190,15 @@ This search looks for the execution of `nltest.exe` with command-line arguments ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `nltest_domain_trust_discovery_filter` ====Associated Analytic Story==== -* Ryuk Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]] ====How To Implement==== @@ -10269,15 +10269,15 @@ This technique uses "Install from Media" (IFM), which will extract a copy of the ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `ntdsutil_export_ntds_filter` ====Associated Analytic Story==== -* Credential Dumping +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] ====How To Implement==== @@ -10340,15 +10340,15 @@ Microsoft Windows contains accessibility features that can be launched with a ke ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter` ====Associated Analytic Story==== -* Windows Privilege Escalation +* [[Documentation:ESSOC:stories:UseCase#Windows_Privilege_Escalation|Windows Privilege Escalation]] ====How To Implement==== @@ -10394,7 +10394,7 @@ Microsoft may provide updates to these binaries. Verify that these changes do no This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-04 @@ -10405,10 +10405,10 @@ This detection identifies use of PowerSploit modules that facilitate access prob | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-AdminAccess/)=true OR match_regex(cmd_line, /(?i)Invoke-CheckLocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Test-ServiceDaclPermission/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -10469,7 +10469,7 @@ None identified. This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1566.002/ T1566.002] * '''Last Updated''': 2021-01-28 @@ -10478,22 +10478,22 @@ This search looks for a process launching an `*.lnk` file under `C:\User*` or `* ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user -| `drop_dm_object_name(Filesystem)` -| rename process_id as lnk_pid +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user +| `drop_dm_object_name(Filesystem)` +| rename process_id as lnk_pid | join lnk_pid, _time [ -| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process -| `drop_dm_object_name(Processes)` -| rename parent_process_id as lnk_pid -| fields _time lnk_pid process_id dest process_name process_path process] -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process +| `drop_dm_object_name(Processes)` +| rename parent_process_id as lnk_pid +| fields _time lnk_pid process_id dest process_name process_path process] +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `process_creating_lnk_file_in_suspicious_location_filter` ====Associated Analytic Story==== -* Phishing Payloads +* [[Documentation:ESSOC:stories:UseCase#Phishing_Payloads|Phishing Payloads]] ====How To Implement==== @@ -10545,7 +10545,7 @@ This detection should yield little or no false positive results. It is uncommon This search looks for processes launched via WMI. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1047/ T1047] * '''Last Updated''': 2020-03-16 @@ -10554,15 +10554,15 @@ This search looks for processes launched via WMI. ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name -| `drop_dm_object_name("Processes")` +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_execution_via_wmi_filter` ====Associated Analytic Story==== -* Suspicious WMI Use +* [[Documentation:ESSOC:stories:UseCase#Suspicious_WMI_Use|Suspicious WMI Use]] ====How To Implement==== @@ -10617,19 +10617,19 @@ This search looks for processes launching netsh.exe. Netsh is a command-line scr ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest -|`drop_dm_object_name("Processes")` -|`security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest +|`drop_dm_object_name("Processes")` +|`security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` |`processes_launching_netsh_filter` ====Associated Analytic Story==== -* Netsh Abuse +* [[Documentation:ESSOC:stories:UseCase#Netsh_Abuse|Netsh Abuse]] -* Disabling Security Tools +* [[Documentation:ESSOC:stories:UseCase#Disabling_Security_Tools|Disabling Security Tools]] -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] ====How To Implement==== @@ -10675,7 +10675,7 @@ Some VPN applications are known to launch netsh.exe. Outside of these instances, An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203], [https://attack.mitre.org/techniques/T1059/ T1059], [https://attack.mitre.org/techniques/T1053/ T1053], [https://attack.mitre.org/techniques/T1072/ T1072] * '''Last Updated''': 2020-08-13 @@ -10684,18 +10684,18 @@ An attacker may use LOLBAS tools spawned from vulnerable applications not typica ====Search==== -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) -| eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) -| where parent_process_name!=null -| select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id -| conditional_anomaly conditional="parent_process_name" target="process_name" -| rename output as input -| where input < 1 -| adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) +| where parent_process_name!=null +| select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id +| conditional_anomaly conditional="parent_process_name" target="process_name" +| rename output as input +| where input < 1 +| adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L | where label AND quantile<0.1 AND (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe") -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -10765,7 +10765,7 @@ Some custom tools used by admins could be used rarely to launch remotely applica This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1087/ T1087], [https://attack.mitre.org/techniques/T1484/ T1484] * '''Last Updated''': 2020-11-05 @@ -10776,10 +10776,10 @@ This detection identifies access to PowerSploit modules that discover accounts, | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Invoke-EnumerateLocalAdmin/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Invoke-EventHunter/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserLocation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserHunter/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignGroupMember/)=true OR match_regex(cmd_line, /(?i)Find-ForeignGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignUser/)=true OR match_regex(cmd_line, /(?i)Find-ForeignUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPO/)=true OR match_regex(cmd_line, /(?i)Get-NetGPO/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOComputerLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOComputerAdmin/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGPOGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOUserLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOLocation/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-DomainManagedSecurityGroup/)=true OR match_regex(cmd_line, /(?i)Find-ManagedSecurityGroups/)=true OR match_regex(cmd_line, /(?i)Get-DomainOU/)=true OR match_regex(cmd_line, /(?i)Get-NetOU/)=true OR match_regex(cmd_line, /(?i)Get-DomainUser/)=true OR match_regex(cmd_line, /(?i)Get-NetUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Get-UserEvent/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetLoggedon/)=true OR match_regex(cmd_line, /(?i)Get-RegLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegLastLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-LastLoggedOn/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -10844,7 +10844,7 @@ None identified. This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1087/ T1087], [https://attack.mitre.org/techniques/T1484/ T1484] * '''Last Updated''': 2020-11-05 @@ -10855,10 +10855,10 @@ This detection identifies use of Mimikatz modules for discovery of accounts and | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::user/)=true OR match_regex(cmd_line, /(?i)net::group/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -10923,7 +10923,7 @@ None identified. This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1199/ T1199], [https://attack.mitre.org/techniques/T1482/ T1482], [https://attack.mitre.org/techniques/T1590/ T1590], [https://attack.mitre.org/techniques/T1591/ T1591], [https://attack.mitre.org/techniques/T1595/ T1595] * '''Last Updated''': 2020-11-06 @@ -10934,10 +10934,10 @@ This detection identifies access to PowerSploit modules for reconnaissance and a | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainSID/)=true OR match_regex(cmd_line, /(?i)Get-DomainSite/)=true OR match_regex(cmd_line, /(?i)Get-NetSite/)=true OR match_regex(cmd_line, /(?i)Get-DomainSubnet/)=true OR match_regex(cmd_line, /(?i)Get-NetSubnet/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrustMapping/)=true OR match_regex(cmd_line, /(?i)Invoke-MapDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-Forest/)=true OR match_regex(cmd_line, /(?i)Get-NetForest/)=true OR match_regex(cmd_line, /(?i)Get-ForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-NetForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-ForestGlobalCatalog/)=true OR match_regex(cmd_line, /(?i)Get-NetForestCatalog/)=true OR match_regex(cmd_line, /(?i)Get-ForestTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetForestTrust/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11010,7 +11010,7 @@ None identified. This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1592/ T1592], [https://attack.mitre.org/techniques/T1590/ T1590], [https://attack.mitre.org/techniques/T1087/ T1087] * '''Last Updated''': 2020-11-06 @@ -11021,10 +11021,10 @@ This detection identifies access to PowerSploit modules that discover computers, | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ComputerDetail/)=true OR match_regex(cmd_line, /(?i)Get-Domain/)=true OR match_regex(cmd_line, /(?i)Get-NetDomain/)=true OR match_regex(cmd_line, /(?i)Get-DomainComputer/)=true OR match_regex(cmd_line, /(?i)Get-NetComputer/)=true OR match_regex(cmd_line, /(?i)Get-DomainController/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainController/)=true OR match_regex(cmd_line, /(?i)Get-DomainFileServer/)=true OR match_regex(cmd_line, /(?i)Get-NetFileServer/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11089,7 +11089,7 @@ None identified. This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1592/ T1592] * '''Last Updated''': 2020-11-06 @@ -11100,10 +11100,10 @@ This detection identifies use of Mimikatz modules for discovery of computers and | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::ServerInfo/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11160,7 +11160,7 @@ None identified. This detection identifies access to PowerSploit modules that discover and access operating system elements, such as processes, services, registry locations, security packages and files. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1007/ T1007], [https://attack.mitre.org/techniques/T1012/ T1012], [https://attack.mitre.org/techniques/T1046/ T1046], [https://attack.mitre.org/techniques/T1047/ T1047], [https://attack.mitre.org/techniques/T1057/ T1057], [https://attack.mitre.org/techniques/T1083/ T1083], [https://attack.mitre.org/techniques/T1518/ T1518], [https://attack.mitre.org/techniques/T1592.002/ T1592.002] * '''Last Updated''': 2020-11-06 @@ -11171,10 +11171,10 @@ This detection identifies access to PowerSploit modules that discover and access | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainProcess/)=true OR match_regex(cmd_line, /(?i)Invoke-ProcessHunter/)=true OR match_regex(cmd_line, /(?i)Get-ServiceDetail/)=true OR match_regex(cmd_line, /(?i)Get-WMIProcess/)=true OR match_regex(cmd_line, /(?i)Get-NetProcess/)=true OR match_regex(cmd_line, /(?i)Get-SecurityPackage/)=true OR match_regex(cmd_line, /(?i)Find-DomainObjectPropertyOutlier/)=true OR match_regex(cmd_line, /(?i)Get-DomainObject/)=true OR match_regex(cmd_line, /(?i)Get-ADObject/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegMountedDrive/)=true OR match_regex(cmd_line, /(?i)Get-RegistryMountedDrive/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11259,7 +11259,7 @@ None identified. This detection identifies use of Mimikatz modules for discovery and access to services and processes. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1007/ T1007], [https://attack.mitre.org/techniques/T1046/ T1046], [https://attack.mitre.org/techniques/T1057/ T1057] * '''Last Updated''': 2020-11-06 @@ -11270,10 +11270,10 @@ This detection identifies use of Mimikatz modules for discovery and access to se | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::list/)=true OR match_regex(cmd_line, /(?i)service::list/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11338,7 +11338,7 @@ None identified. This detection identifies use of Mimikatz modules for discovery and access to network shares. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1135/ T1135], [https://attack.mitre.org/techniques/T1039/ T1039] * '''Last Updated''': 2020-11-06 @@ -11349,10 +11349,10 @@ This detection identifies use of Mimikatz modules for discovery and access to ne | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::share/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11417,7 +11417,7 @@ None identified. This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1135/ T1135], [https://attack.mitre.org/techniques/T1039/ T1039] * '''Last Updated''': 2020-11-06 @@ -11428,10 +11428,10 @@ This detection identifies access to PowerSploit modules that discover and access | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainShare/)=true OR match_regex(cmd_line, /(?i)Invoke-ShareFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainShareFile/)=true OR match_regex(cmd_line, /(?i)Invoke-FileFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingFile/)=true OR match_regex(cmd_line, /(?i)Get-DomainDFSShare/)=true OR match_regex(cmd_line, /(?i)Get-DFSshare/)=true OR match_regex(cmd_line, /(?i)Get-NetShare/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11496,7 +11496,7 @@ None identified. This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1053/ T1053], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1547/ T1547], [https://attack.mitre.org/techniques/T1574/ T1574] * '''Last Updated''': 2020-11-05 @@ -11507,10 +11507,10 @@ This detection identifies use of PowerSploit modules that discover opportunities | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-LocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainAcl/)=true OR match_regex(cmd_line, /(?i)Invoke-ACLScanner/)=true OR match_regex(cmd_line, /(?i)Find-PathDLLHijack/)=true OR match_regex(cmd_line, /(?i)Find-ProcessDLLHijack/)=true OR match_regex(cmd_line, /(?i)Get-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-DomainPolicy/)=true OR match_regex(cmd_line, /(?i)Get-ModifiablePath/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableRegistryAutoRun/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableScheduledTaskFile/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableService/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableServiceFile/)=true OR match_regex(cmd_line, /(?i)Get-PathAcl/)=true OR match_regex(cmd_line, /(?i)Get-UnattendedInstallFile/)=true OR match_regex(cmd_line, /(?i)Get-UnquotedService/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11587,7 +11587,7 @@ None identified. This detection identifies access to PowerSploit modules for reconnaissance of connectivity. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1135/ T1135], [https://attack.mitre.org/techniques/T1039/ T1039] * '''Last Updated''': 2020-11-06 @@ -11598,10 +11598,10 @@ This detection identifies access to PowerSploit modules for reconnaissance of co | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainDNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DomainDNSZone/)=true OR match_regex(cmd_line, /(?i)Get-DNSZone/)=true OR match_regex(cmd_line, /(?i)Invoke-ReverseDnsLookup/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegCachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-CachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegProxy/)=true OR match_regex(cmd_line, /(?i)Get-Proxy/)=true OR match_regex(cmd_line, /(?i)Invoke-Portscan/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11666,7 +11666,7 @@ None identified. This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1589.001/ T1589.001], [https://attack.mitre.org/techniques/T1590.001/ T1590.001], [https://attack.mitre.org/techniques/T1590.003/ T1590.003], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-03 @@ -11677,10 +11677,10 @@ This detection identifies reconnaissance of credential stores and use of CryptoA | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)crypto::capi/)=true OR match_regex(cmd_line, /(?i)crypto::cng/)=true OR match_regex(cmd_line, /(?i)crypto::providers/)=true OR match_regex(cmd_line, /(?i)crypto::stores/)=true OR match_regex(cmd_line, /(?i)crypto::sc/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11757,7 +11757,7 @@ None identified. This detection identifies use of PowerSploit modules for assessment of presence of defensive tools. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1595.002/ T1595.002], [https://attack.mitre.org/techniques/T1592.002/ T1592.002] * '''Last Updated''': 2020-11-05 @@ -11768,10 +11768,10 @@ This detection identifies use of PowerSploit modules for assessment of presence | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-AVSignature/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11832,7 +11832,7 @@ None identified. This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-05 @@ -11843,10 +11843,10 @@ This detection identifies use of PowerSploit modules for assessment of privilege | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-PrivescAudit/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -11911,7 +11911,7 @@ None identified. This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1543/ T1543], [https://attack.mitre.org/techniques/T1055/ T1055], [https://attack.mitre.org/techniques/T1574/ T1574] * '''Last Updated''': 2020-11-05 @@ -11922,10 +11922,10 @@ This detection identifies use of Mimikatz modules for discovery of process or se | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::detours/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12001,17 +12001,17 @@ The search looks for reg.exe modifying registry keys that define Windows service ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter` ====Associated Analytic Story==== -* Windows Service Abuse +* [[Documentation:ESSOC:stories:UseCase#Windows_Service_Abuse|Windows Service Abuse]] -* Windows Persistence Techniques +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] ====How To Implement==== @@ -12057,7 +12057,7 @@ It is unusual for a service to be created or modified by directly manipulating t The search looks for modifications to registry keys that can be used to launch an application or service at system startup. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1547.001/ T1547.001] * '''Last Updated''': 2020-11-27 @@ -12066,27 +12066,27 @@ The search looks for modifications to registry keys that can be used to launch a ====Search==== -| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `drop_dm_object_name(Registry)` +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` | `registry_keys_used_for_persistence_filter` ====Associated Analytic Story==== -* Suspicious Windows Registry Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Windows_Registry_Activities|Suspicious Windows Registry Activities]] -* Suspicious MSHTA Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_MSHTA_Activity|Suspicious MSHTA Activity]] -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] -* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns +* [[Documentation:ESSOC:stories:UseCase#Possible_Backdoor_Activity_Associated_With_MUDCARP_Espionage_Campaigns|Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] -* Windows Persistence Techniques +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] -* Emotet Malware DHS Report TA18-201A +* [[Documentation:ESSOC:stories:UseCase#Emotet_Malware__DHS_Report_TA18-201A_|Emotet Malware DHS Report TA18-201A ]] ====How To Implement==== @@ -12132,7 +12132,7 @@ There are many legitimate applications that must execute on system startup and w This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.012/ T1546.012] * '''Last Updated''': 2020-11-27 @@ -12141,19 +12141,19 @@ This search looks for modifications to registry keys that can be used to elevate ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `drop_dm_object_name(Registry)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` | `registry_keys_used_for_privilege_escalation_filter` ====Associated Analytic Story==== -* Windows Privilege Escalation +* [[Documentation:ESSOC:stories:UseCase#Windows_Privilege_Escalation|Windows Privilege Escalation]] -* Suspicious Windows Registry Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Windows_Registry_Activities|Suspicious Windows Registry Activities]] -* Cloud Federated Credential Abuse +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] ====How To Implement==== @@ -12201,7 +12201,7 @@ There are many legitimate applications that must execute upon system startup and This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.011/ T1546.011] * '''Last Updated''': 2020-11-26 @@ -12210,17 +12210,17 @@ This search looks for registry activity associated with application compatibilit ====Search==== -| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `drop_dm_object_name(Registry)` +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` | `registry_keys_for_creating_shim_databases_filter` ====Associated Analytic Story==== -* Suspicious Windows Registry Activities +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Windows_Registry_Activities|Suspicious Windows Registry Activities]] -* Windows Persistence Techniques +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] ====How To Implement==== @@ -12275,17 +12275,17 @@ This search looks for wmic.exe being launched with parameters to spawn a process ====Search==== -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter` ====Associated Analytic Story==== -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] -* Suspicious WMI Use +* [[Documentation:ESSOC:stories:UseCase#Suspicious_WMI_Use|Suspicious WMI Use]] ====How To Implement==== @@ -12340,15 +12340,15 @@ This search looks for executing scripts with rundll32. Adversaries may abuse run ====Search==== -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `rundll_loading_dll_by_ordinal_filter` ====Associated Analytic Story==== -* Unusual Processes +* [[Documentation:ESSOC:stories:UseCase#Unusual_Processes|Unusual Processes]] ====How To Implement==== @@ -12394,7 +12394,7 @@ While not common, loading a DLL under %AppData% and calling a function by ordina The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1486/ T1486] * '''Last Updated''': 2020-11-06 @@ -12403,15 +12403,15 @@ The search looks for files that contain the key word *Ryuk* under any folder in ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter` ====Associated Analytic Story==== -* Ryuk Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]] ====How To Implement==== @@ -12466,15 +12466,15 @@ The search looks for a file named "test.txt" written to the windows system direc ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` | `samsam_test_file_write_filter` ====Associated Analytic Story==== -* SamSam Ransomware +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] ====How To Implement==== @@ -12529,25 +12529,25 @@ This search looks for arguments to sc.exe indicating the creation or modificatio ====Search==== -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter` ====Associated Analytic Story==== -* Windows Service Abuse +* [[Documentation:ESSOC:stories:UseCase#Windows_Service_Abuse|Windows Service Abuse]] -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] -* Orangeworm Attack Group +* [[Documentation:ESSOC:stories:UseCase#Orangeworm_Attack_Group|Orangeworm Attack Group]] -* Windows Persistence Techniques +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] -* Disabling Security Tools +* [[Documentation:ESSOC:stories:UseCase#Disabling_Security_Tools|Disabling Security Tools]] -* Sunburst Malware +* [[Documentation:ESSOC:stories:UseCase#Sunburst_Malware|Sunburst Malware]] ====How To Implement==== @@ -12602,17 +12602,17 @@ This search looks for flags passed to schtasks.exe on the command-line that indi ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `scheduled_task_deleted_or_created_via_cmd_filter` ====Associated Analytic Story==== -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] -* Sunburst Malware +* [[Documentation:ESSOC:stories:UseCase#Sunburst_Malware|Sunburst Malware]] ====How To Implement==== @@ -12667,17 +12667,17 @@ This search looks for flags passed to schtasks.exe on the command-line that indi ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter` ====Associated Analytic Story==== -* Lateral Movement +* [[Documentation:ESSOC:stories:UseCase#Lateral_Movement|Lateral Movement]] -* Sunburst Malware +* [[Documentation:ESSOC:stories:UseCase#Sunburst_Malware|Sunburst Malware]] ====How To Implement==== @@ -12732,17 +12732,17 @@ This search looks for flags passed to schtasks.exe on the command-line that indi ====Search==== -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `schtasks_used_for_forcing_a_reboot_filter` ====Associated Analytic Story==== -* Windows Persistence Techniques +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] ====How To Implement==== @@ -12788,7 +12788,7 @@ Administrators may create jobs on systems forcing reboots to perform updates, ma This search looks for scripts launched via WMI. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1047/ T1047] * '''Last Updated''': 2020-03-16 @@ -12797,15 +12797,15 @@ This search looks for scripts launched via WMI. ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name -| `drop_dm_object_name("Processes")` +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `script_execution_via_wmi_filter` ====Associated Analytic Story==== -* Suspicious WMI Use +* [[Documentation:ESSOC:stories:UseCase#Suspicious_WMI_Use|Suspicious WMI Use]] ====How To Implement==== @@ -12851,7 +12851,7 @@ Although unlikely, administrators may use wmi to launch scripts for legitimate p This detection identifies illegal setting of credentials via DSInternals modules. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-03 @@ -12862,10 +12862,10 @@ This detection identifies illegal setting of credentials via DSInternals modules | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-ADDBSidHistory/)=true OR match_regex(cmd_line, /(?i)Add-ADReplNgcKey/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPassword/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-ADDBBootKey/)=true OR match_regex(cmd_line, /(?i)Set-SamAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-AzureADUserEx/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -12936,7 +12936,7 @@ None identified. This detection identifies illegal setting of credentials via Mimikatz modules. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-03 @@ -12947,10 +12947,10 @@ This detection identifies illegal setting of credentials via Mimikatz modules. | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::addsid/)=true OR match_regex(cmd_line, /(?i)CRYPTO::scauth/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -13015,7 +13015,7 @@ None identified. This detection identifies illegal setting of credentials via PowerSploit modules. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1098/ T1098] * '''Last Updated''': 2020-11-03 @@ -13026,10 +13026,10 @@ This detection identifies illegal setting of credentials via PowerSploit modules | from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) | where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainUserPassword/)=true ) -| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -13094,7 +13094,7 @@ None identified. This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.011/ T1546.011] * '''Last Updated''': 2020-12-08 @@ -13103,15 +13103,15 @@ This search looks for shim database files being written to default directories. ====Search==== -| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -|`drop_dm_object_name(Filesystem)` +| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +|`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter` ====Associated Analytic Story==== -* Windows Persistence Techniques +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] ====How To Implement==== @@ -13166,15 +13166,15 @@ This search detects the process execution and arguments required to silently cre ====Search==== -| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter` ====Associated Analytic Story==== -* Windows Persistence Techniques +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] ====How To Implement==== @@ -13229,18 +13229,18 @@ This search detects accounts that were created and deleted in a short time perio ====Search==== -| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| `drop_dm_object_name("All_Changes")` -| search result_id = 4720 result_id=4726 -| transaction user connected=false maxspan=240m -| table firstTime lastTime count user dest result_id +| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name("All_Changes")` +| search result_id = 4720 result_id=4726 +| transaction user connected=false maxspan=240m +| table firstTime lastTime count user dest result_id | `short_lived_windows_accounts_filter` ====Associated Analytic Story==== -* Account Monitoring and Controls +* [[Documentation:ESSOC:stories:UseCase#Account_Monitoring_and_Controls|Account Monitoring and Controls]] ====How To Implement==== @@ -13297,18 +13297,18 @@ This search looks for process names that consist only of a single letter. ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name -| `drop_dm_object_name(Processes)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` -| eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) -| search process_name_length=5 AND endExe=1 -| table count, firstTime, lastTime, dest, user, process, process_name +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name +| `drop_dm_object_name(Processes)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) +| search process_name_length=5 AND endExe=1 +| table count, firstTime, lastTime, dest, user, process, process_name | `single_letter_process_on_endpoint_filter` ====Associated Analytic Story==== -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] ====How To Implement==== @@ -13354,7 +13354,7 @@ Single-letter executables are not always malicious. Investigate this activity wi The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1127.001/ T1127.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2021-01-12 @@ -13362,16 +13362,16 @@ The following analytic identifies renamed instances of msbuild.exe executing. Ms
====Search==== -`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest +`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_msbuild_rename_filter` ====Associated Analytic Story==== -* Trusted Developer Utilities Proxy Execution MSBuild +* [[Documentation:ESSOC:stories:UseCase#Trusted_Developer_Utilities_Proxy_Execution_MSBuild|Trusted Developer Utilities Proxy Execution MSBuild]] ====How To Implement==== @@ -13436,15 +13436,15 @@ The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavi ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `suspicious_msbuild_spawn_filter` ====Associated Analytic Story==== -* Trusted Developer Utilities Proxy Execution MSBuild +* [[Documentation:ESSOC:stories:UseCase#Trusted_Developer_Utilities_Proxy_Execution_MSBuild|Trusted Developer Utilities Proxy Execution MSBuild]] ====How To Implement==== @@ -13494,7 +13494,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1112/ T1112] * '''Last Updated''': 2020-07-22 @@ -13503,27 +13503,27 @@ This search looks for reg.exe being launched from a command prompt not started b ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | search [ -| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name -| `drop_dm_object_name("Processes")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| rename parent_process_id as process_id +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename parent_process_id as process_id |dedup process_id -| table process_id dest] +| table process_id dest] | `suspicious_reg_exe_process_filter` ====Associated Analytic Story==== -* Windows Defense Evasion Tactics +* [[Documentation:ESSOC:stories:UseCase#Windows_Defense_Evasion_Tactics|Windows Defense Evasion Tactics]] -* Disabling Security Tools +* [[Documentation:ESSOC:stories:UseCase#Disabling_Security_Tools|Disabling Security Tools]] -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] ====How To Implement==== @@ -13580,15 +13580,15 @@ Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `suspicious_regsvr32_register_suspicious_path_filter` ====Associated Analytic Story==== -* Suspicious Regsvr32 Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Regsvr32_Activity|Suspicious Regsvr32 Activity]] ====How To Implement==== @@ -13644,7 +13644,7 @@ Limited false positives with the query restricted to specified paths. Add more w The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2021-02-04 @@ -13652,16 +13652,16 @@ The following analytic identifies renamed instances of rundll32.exe executing. r
====Search==== -`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest +`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_rename_filter` ====Associated Analytic Story==== -* Suspicious Rundll32 Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Rundll32_Activity|Suspicious Rundll32 Activity]] ====How To Implement==== @@ -13726,17 +13726,17 @@ The following analytic identifies rundll32.exe executing a DLL function name, St ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `suspicious_rundll32_startw_filter` ====Associated Analytic Story==== -* Suspicious Rundll32 Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Rundll32_Activity|Suspicious Rundll32 Activity]] -* Cobalt Strike +* [[Documentation:ESSOC:stories:UseCase#Cobalt_Strike|Cobalt Strike]] ====How To Implement==== @@ -13801,15 +13801,15 @@ The following analytic identifies rundll32.exe using dllregisterserver on the co ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `suspicious_rundll32_dllregisterserver_filter` ====Associated Analytic Story==== -* Suspicious Rundll32 Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Rundll32_Activity|Suspicious Rundll32 Activity]] ====How To Implement==== @@ -13869,7 +13869,7 @@ This is likely to produce false positives and will require some filtering. Tune The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011] * '''Last Updated''': 2021-02-09 @@ -13877,19 +13877,19 @@ The following analytic identifies rundll32.exe with no command line arguments. I
====Search==== -`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) -| regex CommandLine="(rundll32\.exe.{0,4}$)" -| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) +| regex CommandLine="(rundll32\.exe.{0,4}$)" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `suspicious_rundll32_no_commandline_arguments_filter` ====Associated Analytic Story==== -* Suspicious Rundll32 Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Rundll32_Activity|Suspicious Rundll32 Activity]] -* Cobalt Strike +* [[Documentation:ESSOC:stories:UseCase#Cobalt_Strike|Cobalt Strike]] ====How To Implement==== @@ -13943,7 +13943,7 @@ Although unlikely, some legitimate applications may use a moved copy of rundll32 The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1127, T1036.003/ T1127, T1036.003] * '''Last Updated''': 2021-01-12 @@ -13951,16 +13951,16 @@ The following analytic identifies a renamed instance of microsoft.workflow.compi
====Search==== -`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) -| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine -| rename Computer as dest +`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine +| rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_rename_filter` ====Associated Analytic Story==== -* Trusted Developer Utilities Proxy Execution +* [[Documentation:ESSOC:stories:UseCase#Trusted_Developer_Utilities_Proxy_Execution|Trusted Developer Utilities Proxy Execution]] ====How To Implement==== @@ -13975,9 +13975,9 @@ To successfully implement this search, you need to be ingesting logs with the pr ! Technique ! Tactic |- -| -| -| +| +| +| |} ====Kill Chain Phase==== @@ -14019,15 +14019,15 @@ The following analytic identifies microsoft.workflow.compiler.exe usage. microso ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_usage_filter` ====Associated Analytic Story==== -* Trusted Developer Utilities Proxy Execution +* [[Documentation:ESSOC:stories:UseCase#Trusted_Developer_Utilities_Proxy_Execution|Trusted Developer Utilities Proxy Execution]] ====How To Implement==== @@ -14086,15 +14086,15 @@ The following analytic identifies msbuild.exe executing from a non-standard path ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_msbuild_path_filter` ====Associated Analytic Story==== -* Trusted Developer Utilities Proxy Execution MSBuild +* [[Documentation:ESSOC:stories:UseCase#Trusted_Developer_Utilities_Proxy_Execution_MSBuild|Trusted Developer Utilities Proxy Execution MSBuild]] ====How To Implement==== @@ -14157,15 +14157,15 @@ The following analytic identifies child processes spawning from "mshta.exe". Th ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `suspicious_mshta_child_process_filter` ====Associated Analytic Story==== -* Suspicious MSHTA Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_MSHTA_Activity|Suspicious MSHTA Activity]] ====How To Implement==== @@ -14224,15 +14224,15 @@ The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `suspicious_mshta_spawn_filter` ====Associated Analytic Story==== -* Suspicious MSHTA Activity +* [[Documentation:ESSOC:stories:UseCase#Suspicious_MSHTA_Activity|Suspicious MSHTA Activity]] ====How To Implement==== @@ -14294,16 +14294,16 @@ The wevtutil.exe application is the windows event log utility. This searches for ====Search==== | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` | `suspicious_wevtutil_usage_filter` ====Associated Analytic Story==== -* Windows Log Manipulation +* [[Documentation:ESSOC:stories:UseCase#Windows_Log_Manipulation|Windows Log Manipulation]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] ====How To Implement==== @@ -14349,7 +14349,7 @@ The wevtutil.exe application is a legitimate Windows event log utility. Administ This search detects writes to the recycle bin by a process other than explorer.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1036/ T1036] * '''Last Updated''': 2020-07-22 @@ -14358,17 +14358,17 @@ This search detects writes to the recycle bin by a process other than explorer.e ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")` | search [ | tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest -| `drop_dm_object_name("Processes")` -| table process_id dest] +| `drop_dm_object_name("Processes")` +| table process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter` ====Associated Analytic Story==== -* Collection and Staging +* [[Documentation:ESSOC:stories:UseCase#Collection_and_Staging|Collection and Staging]] ====How To Implement==== @@ -14421,18 +14421,18 @@ Detect system information discovery techniques used by attackers to understand c ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| eventstats dc(process) as dc_processes_by_dest by dest -| where dc_processes_by_dest > 2 -| stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| eventstats dc(process) as dc_processes_by_dest by dest +| where dc_processes_by_dest > 2 +| stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter` ====Associated Analytic Story==== -* Discovery Techniques +* [[Documentation:ESSOC:stories:UseCase#Discovery_Techniques|Discovery Techniques]] ====How To Implement==== @@ -14480,7 +14480,7 @@ Administrators debugging servers An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml * '''Product''': UEBA for Security Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1036/ T1036] * '''Last Updated''': 2020-08-25 @@ -14488,36 +14488,36 @@ An attacker tries might try to use different version of a system command without
====Search==== - $ssa_input = -| from read_ssa_enriched_events() + $ssa_input = +| from read_ssa_enriched_events() | eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), user=ucast(map_get(input_event, "dest_user_id"), "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=lower(ucast(map_get(input_event, "process_path"), "string", null)); -$cond_1 = -| from $ssa_input +$cond_1 = +| from $ssa_input | where process_name="arp.exe" OR process_name="adaptertroubleshooter.exe" OR process_name="applicationframehost.exe" OR process_name="atbroker.exe" OR process_name="authhost.exe" OR process_name="autoworkplace.exe" OR process_name="axinstui.exe" OR process_name="backgroundtransferhost.exe" OR process_name="bdehdcfg.exe" OR process_name="bdeuisrv.exe" OR process_name="bdeunlockwizard.exe" OR process_name="bitlockerdeviceencryption.exe" OR process_name="bitlockerwizard.exe" OR process_name="bitlockerwizardelev.exe" OR process_name="bytecodegenerator.exe" OR process_name="camerasettingsuihost.exe" OR process_name="castsrv.exe" OR process_name="certenrollctrl.exe" OR process_name="checknetisolation.exe" OR process_name="clipup.exe" OR process_name="cloudexperiencehostbroker.exe" OR process_name="cloudnotifications.exe" OR process_name="cloudstoragewizard.exe" OR process_name="compmgmtlauncher.exe" OR process_name="compattelrunner.exe" OR process_name="computerdefaults.exe" OR process_name="credentialuibroker.exe" OR process_name="dfdwiz.exe" OR process_name="dwwin.exe" OR process_name="dataexchangehost.exe" OR process_name="defrag.exe" OR process_name="devicedisplayobjectprovider.exe" OR process_name="deviceeject.exe" OR process_name="deviceenroller.exe" OR process_name="devicepairingwizard.exe" OR process_name="deviceproperties.exe" OR process_name="disksnapshot.exe" OR process_name="dism.exe" OR process_name="displayswitch.exe" OR process_name="dmnotificationbroker.exe" OR process_name="dmomacpmo.exe" OR process_name="dpiscaling.exe" OR process_name="dsmusertask.exe" OR process_name="dxpserver.exe" OR process_name="edpcleanup.exe" OR process_name="eosnotify.exe" OR process_name="eap3host.exe" OR process_name="easpoliciesbrokerhost.exe" OR process_name="easeofaccessdialog.exe" OR process_name="ehstorauthn.exe" OR process_name="fxscover.exe" OR process_name="fxssvc.exe" OR process_name="fxsunatd.exe" OR process_name="filehistory.exe" OR process_name="fondue.exe" OR process_name="gamepanel.exe" OR process_name="genvalobj.exe" OR process_name="gettingstarted.exe" OR process_name="hostname.exe" OR process_name="icsentitlementhost.exe" OR process_name="infdefaultinstall.exe" OR process_name="installagent.exe" OR process_name="languagecomponentsinstallercomhandler.exe" OR process_name="launchtm.exe" OR process_name="launchwinapp.exe" OR process_name="legacynetuxhost.exe" OR process_name="licensemanagershellext.exe" OR process_name="licensingui.exe" OR process_name="locationnotificationwindows.exe" OR process_name="locationnotifications.exe" OR process_name="locator.exe" OR process_name="lockapphost.exe" OR process_name="lockscreencontentserver.exe" OR process_name="logonui.exe" OR process_name="lsaiso.exe" OR process_name="mdeserver.exe" OR process_name="mdmagent.exe" OR process_name="mdmappinstaller.exe" OR process_name="mrinfo.exe" OR process_name="mrt.exe" OR process_name="mschedexe.exe" OR process_name="magnify.exe" OR process_name="mbaeparsertask.exe" OR process_name="mdres.exe" OR process_name="mdsched.exe" OR process_name="migautoplay.exe" OR process_name="mpsigstub.exe" OR process_name="msspellcheckinghost.exe" OR process_name="muiunattend.exe" OR process_name="multidigimon.exe" OR process_name="musnotification.exe" OR process_name="musnotificationux.exe" OR process_name="napstat.exe" OR process_name="netstat.exe" OR process_name="narrator.exe" OR process_name="netcfgnotifyobjecthost.exe" OR process_name="netevtfwdr.exe" OR process_name="netproj.exe" OR process_name="netplwiz.exe" OR process_name="networkuxbroker.exe"; -$cond_2 = -| from $ssa_input +$cond_2 = +| from $ssa_input | where process_name="openwith.exe" OR process_name="optionalfeatures.exe" OR process_name="pathping.exe" OR process_name="ping.exe" OR process_name="passwordonwakesettingflyout.exe" OR process_name="pickerhost.exe" OR process_name="pkgmgr.exe" OR process_name="pnpunattend.exe" OR process_name="pnputil.exe" OR process_name="presentationhost.exe" OR process_name="presentationsettings.exe" OR process_name="printbrmui.exe" OR process_name="printdialoghost.exe" OR process_name="printdialoghost3d.exe" OR process_name="printisolationhost.exe" OR process_name="proximityuxhost.exe" OR process_name="rdspnf.exe" OR process_name="rmactivate.exe" OR process_name="rmactivate_isv.exe" OR process_name="rmactivate_ssp.exe" OR process_name="rmactivate_ssp_isv.exe" OR process_name="route.exe" OR process_name="rdpsa.exe" OR process_name="rdpsaproxy.exe" OR process_name="rdpsauachelper.exe" OR process_name="reagentc.exe" OR process_name="recoverydrive.exe" OR process_name="register-cimprovider.exe" OR process_name="registeriepkeys.exe" OR process_name="relpost.exe" OR process_name="remoteposworker.exe" OR process_name="rmclient.exe" OR process_name="robocopy.exe" OR process_name="rpcping.exe" OR process_name="runlegacycplelevated.exe" OR process_name="runtimebroker.exe" OR process_name="sihclient.exe" OR process_name="searchfilterhost.exe" OR process_name="searchindexer.exe" OR process_name="searchprotocolhost.exe" OR process_name="secedit.exe" OR process_name="sensordataservice.exe" OR process_name="setieinstalleddate.exe" OR process_name="settingsynchost.exe" OR process_name="slidetoshutdown.exe" OR process_name="smartscreensettings.exe" OR process_name="sndvol.exe" OR process_name="snippingtool.exe" OR process_name="soundrecorder.exe" OR process_name="spaceagent.exe" OR process_name="sppextcomobj.exe" OR process_name="srtasks.exe" OR process_name="stikynot.exe" OR process_name="synchost.exe" OR process_name="sysreseterr.exe" OR process_name="systempropertiesadvanced.exe" OR process_name="systempropertiescomputername.exe" OR process_name="systempropertiesdataexecutionprevention.exe" OR process_name="systempropertieshardware.exe" OR process_name="systempropertiesperformance.exe" OR process_name="systempropertiesprotection.exe" OR process_name="systempropertiesremote.exe" OR process_name="systemsettingsadminflows.exe" OR process_name="systemsettingsbroker.exe" OR process_name="systemsettingsremovedevice.exe" OR process_name="tcpsvcs.exe" OR process_name="tracert.exe" OR process_name="tstheme.exe" OR process_name="tswbprxy.exe" OR process_name="tapiunattend.exe" OR process_name="taskmgr.exe" OR process_name="thumbnailextractionhost.exe" OR process_name="tokenbrokercookies.exe" OR process_name="tpminit.exe" OR process_name="tswpfwrp.exe" OR process_name="ui0detect.exe" OR process_name="upgraderesultsui.exe" OR process_name="useraccountbroker.exe" OR process_name="useraccountcontrolsettings.exe" OR process_name="usoclient.exe" OR process_name="utilman.exe" OR process_name="vssvc.exe" OR process_name="vaultcmd.exe" OR process_name="vaultsysui.exe" OR process_name="wfs.exe" OR process_name="wmpdmc.exe" OR process_name="wpdshextautoplay.exe" OR process_name="wscollect.exe" OR process_name="wsmanhttpconfig.exe" OR process_name="wsreset.exe" OR process_name="wudfhost.exe" OR process_name="wwahost.exe" OR process_name="wallpaperhost.exe" OR process_name="webcache.exe" OR process_name="werfault.exe" OR process_name="werfaultsecure.exe" OR process_name="winsat.exe" OR process_name="windows.media.backgroundplayback.exe" OR process_name="windowsactiondialog.exe" OR process_name="windowsanytimeupgrade.exe" OR process_name="windowsanytimeupgraderesults.exe"; -$cond_3 = -| from $ssa_input +$cond_3 = +| from $ssa_input | where process_name="windowsanytimeupgradeui.exe" OR process_name="windowsupdateelevatedinstaller.exe" OR process_name="workfolders.exe" OR process_name="wpcmon.exe" OR process_name="acu.exe" OR process_name="aitagent.exe" OR process_name="aitstatic.exe" OR process_name="alg.exe" OR process_name="appidcertstorecheck.exe" OR process_name="appidpolicyconverter.exe" OR process_name="at.exe" OR process_name="attrib.exe" OR process_name="audiodg.exe" OR process_name="auditpol.exe" OR process_name="autochk.exe" OR process_name="autoconv.exe" OR process_name="autofmt.exe" OR process_name="baaupdate.exe" OR process_name="backgroundtaskhost.exe" OR process_name="bcastdvr.exe" OR process_name="bcdboot.exe" OR process_name="bcdedit.exe" OR process_name="bdechangepin.exe" OR process_name="bdeunlock.exe" OR process_name="bitsadmin.exe" OR process_name="bootcfg.exe" OR process_name="bootim.exe" OR process_name="bootsect.exe" OR process_name="bridgeunattend.exe" OR process_name="browser_broker.exe" OR process_name="bthudtask.exe" OR process_name="cacls.exe" OR process_name="calc.exe" OR process_name="cdpreference.exe" OR process_name="certreq.exe" OR process_name="certutil.exe" OR process_name="change.exe" OR process_name="changepk.exe" OR process_name="charmap.exe" OR process_name="chglogon.exe" OR process_name="chgport.exe" OR process_name="chgusr.exe" OR process_name="chkdsk.exe" OR process_name="chkntfs.exe" OR process_name="choice.exe" OR process_name="cipher.exe" OR process_name="cleanmgr.exe" OR process_name="cliconfg.exe" OR process_name="clip.exe" OR process_name="cmd.exe" OR process_name="cmdkey.exe" OR process_name="cmdl32.exe" OR process_name="cmmon32.exe" OR process_name="cmstp.exe" OR process_name="cofire.exe" OR process_name="colorcpl.exe" OR process_name="comp.exe" OR process_name="compact.exe" OR process_name="conhost.exe" OR process_name="consent.exe" OR process_name="control.exe" OR process_name="convert.exe" OR process_name="credwiz.exe" OR process_name="cscript.exe" OR process_name="csrss.exe" OR process_name="ctfmon.exe" OR process_name="cttune.exe" OR process_name="cttunesvr.exe" OR process_name="dashost.exe" OR process_name="dccw.exe" OR process_name="dcomcnfg.exe" OR process_name="ddodiag.exe" OR process_name="dfrgui.exe" OR process_name="dialer.exe" OR process_name="diantz.exe" OR process_name="dinotify.exe" OR process_name="diskpart.exe" OR process_name="diskperf.exe" OR process_name="diskraid.exe" OR process_name="dispdiag.exe" OR process_name="djoin.exe" OR process_name="dllhost.exe" OR process_name="dllhst3g.exe" OR process_name="dmcertinst.exe" OR process_name="dmcfghost.exe" OR process_name="dmclient.exe" OR process_name="dnscacheugc.exe" OR process_name="doskey.exe" OR process_name="dpapimig.exe" OR process_name="dpnsvr.exe" OR process_name="driverquery.exe" OR process_name="drvcfg.exe" OR process_name="drvinst.exe" OR process_name="dsregcmd.exe" OR process_name="dstokenclean.exe" OR process_name="dvdplay.exe" OR process_name="dvdupgrd.exe" OR process_name="dwm.exe" OR process_name="dxdiag.exe" OR process_name="easinvoker.exe" OR process_name="efsui.exe"; -$cond_4 = -| from $ssa_input +$cond_4 = +| from $ssa_input | where process_name="embeddedapplauncher.exe" OR process_name="esentutl.exe" OR process_name="eudcedit.exe" OR process_name="eventcreate.exe" OR process_name="eventvwr.exe" OR process_name="expand.exe" OR process_name="extrac32.exe" OR process_name="fc.exe" OR process_name="fhmanagew.exe" OR process_name="find.exe" OR process_name="findstr.exe" OR process_name="finger.exe" OR process_name="fixmapi.exe" OR process_name="fltmc.exe" OR process_name="fodhelper.exe" OR process_name="fontdrvhost.exe" OR process_name="fontview.exe" OR process_name="forfiles.exe" OR process_name="fsavailux.exe" OR process_name="fsquirt.exe" OR process_name="fsutil.exe" OR process_name="ftp.exe" OR process_name="fvenotify.exe" OR process_name="fveprompt.exe" OR process_name="getmac.exe" OR process_name="gpresult.exe" OR process_name="gpscript.exe" OR process_name="gpupdate.exe" OR process_name="grpconv.exe" OR process_name="hdwwiz.exe" OR process_name="help.exe" OR process_name="hwrcomp.exe" OR process_name="hwrreg.exe" OR process_name="icacls.exe" OR process_name="icardagt.exe" OR process_name="icsunattend.exe" OR process_name="ie4uinit.exe" OR process_name="ieunatt.exe" OR process_name="ieetwcollector.exe" OR process_name="iexpress.exe" OR process_name="immersivetpmvscmgrsvr.exe" OR process_name="ipconfig.exe" OR process_name="irftp.exe" OR process_name="iscsicli.exe" OR process_name="iscsicpl.exe" OR process_name="isoburn.exe" OR process_name="klist.exe" OR process_name="ksetup.exe" OR process_name="ktmutil.exe" OR process_name="label.exe" OR process_name="licensingdiag.exe" OR process_name="lodctr.exe" OR process_name="logagent.exe" OR process_name="logman.exe" OR process_name="logoff.exe" OR process_name="lpkinstall.exe" OR process_name="lpksetup.exe" OR process_name="lpremove.exe" OR process_name="lsass.exe" OR process_name="lsm.exe" OR process_name="makecab.exe" OR process_name="manage-bde.exe" OR process_name="mblctr.exe" OR process_name="mcbuilder.exe" OR process_name="mctadmin.exe" OR process_name="mfpmp.exe" OR process_name="mmc.exe" OR process_name="mobsync.exe" OR process_name="mountvol.exe" OR process_name="mpnotify.exe" OR process_name="msconfig.exe" OR process_name="msdt.exe" OR process_name="msdtc.exe" OR process_name="msfeedssync.exe" OR process_name="msg.exe" OR process_name="mshta.exe" OR process_name="msiexec.exe" OR process_name="msinfo32.exe" OR process_name="mspaint.exe" OR process_name="msra.exe" OR process_name="mstsc.exe" OR process_name="mtstocom.exe" OR process_name="nbtstat.exe" OR process_name="ndadmin.exe" OR process_name="net.exe" OR process_name="net1.exe" OR process_name="netbtugc.exe" OR process_name="netcfg.exe" OR process_name="netiougc.exe" OR process_name="netsh.exe" OR process_name="newdev.exe" OR process_name="nltest.exe" OR process_name="notepad.exe" OR process_name="nslookup.exe" OR process_name="ntoskrnl.exe" OR process_name="ntprint.exe" OR process_name="ocsetup.exe" OR process_name="odbcad32.exe" OR process_name="odbcconf.exe" OR process_name="omadmclient.exe" OR process_name="omadmprc.exe"; -$cond_5 = -| from $ssa_input +$cond_5 = +| from $ssa_input | where process_name="openfiles.exe" OR process_name="osk.exe" OR process_name="p2phost.exe" OR process_name="pcalua.exe" OR process_name="pcaui.exe" OR process_name="pcawrk.exe" OR process_name="pcwrun.exe" OR process_name="perfmon.exe" OR process_name="phoneactivate.exe" OR process_name="plasrv.exe" OR process_name="poqexec.exe" OR process_name="powercfg.exe" OR process_name="prevhost.exe" OR process_name="print.exe" OR process_name="printfilterpipelinesvc.exe" OR process_name="printui.exe" OR process_name="proquota.exe" OR process_name="provtool.exe" OR process_name="psr.exe" OR process_name="pwlauncher.exe" OR process_name="qappsrv.exe" OR process_name="qprocess.exe" OR process_name="query.exe" OR process_name="quser.exe" OR process_name="qwinsta.exe" OR process_name="rasautou.exe" OR process_name="rasdial.exe" OR process_name="raserver.exe" OR process_name="rasphone.exe" OR process_name="rdpclip.exe" OR process_name="rdpinput.exe" OR process_name="rdrleakdiag.exe" OR process_name="recdisc.exe" OR process_name="recover.exe" OR process_name="reg.exe" OR process_name="regedt32.exe" OR process_name="regini.exe" OR process_name="regsvr32.exe" OR process_name="rekeywiz.exe" OR process_name="relog.exe" OR process_name="repair-bde.exe" OR process_name="replace.exe" OR process_name="reset.exe" OR process_name="resmon.exe" OR process_name="rmttpmvscmgrsvr.exe" OR process_name="rrinstaller.exe" OR process_name="rstrui.exe" OR process_name="runas.exe" OR process_name="rundll32.exe" OR process_name="runonce.exe" OR process_name="rwinsta.exe" OR process_name="sbunattend.exe" OR process_name="sc.exe" OR process_name="schtasks.exe" OR process_name="sdbinst.exe" OR process_name="sdchange.exe" OR process_name="sdclt.exe" OR process_name="sdiagnhost.exe" OR process_name="secinit.exe" OR process_name="services.exe" OR process_name="sessionmsg.exe" OR process_name="sethc.exe" OR process_name="setspn.exe" OR process_name="setupcl.exe" OR process_name="setupugc.exe" OR process_name="setx.exe" OR process_name="sfc.exe" OR process_name="shadow.exe" OR process_name="shrpubw.exe" OR process_name="shutdown.exe" OR process_name="sigverif.exe" OR process_name="sihost.exe" OR process_name="slui.exe" OR process_name="smss.exe" OR process_name="snmptrap.exe" OR process_name="sort.exe" OR process_name="spinstall.exe" OR process_name="spoolsv.exe" OR process_name="sppsvc.exe" OR process_name="spreview.exe" OR process_name="srdelayed.exe" OR process_name="subst.exe" OR process_name="svchost.exe" OR process_name="sxstrace.exe" OR process_name="syskey.exe" OR process_name="systeminfo.exe" OR process_name="systemreset.exe" OR process_name="systray.exe" OR process_name="tabcal.exe" OR process_name="takeown.exe" OR process_name="taskeng.exe" OR process_name="taskhost.exe" OR process_name="taskhostw.exe" OR process_name="taskkill.exe" OR process_name="tasklist.exe" OR process_name="taskmgr.exe" OR process_name="tcmsetup.exe" OR process_name="timeout.exe" OR process_name="tpmvscmgr.exe" OR process_name="tpmvscmgrsvr.exe"; -$cond_6 = -| from $ssa_input +$cond_6 = +| from $ssa_input | where process_name="tracerpt.exe" OR process_name="tscon.exe" OR process_name="tsdiscon.exe" OR process_name="tskill.exe" OR process_name="typeperf.exe" OR process_name="tzsync.exe" OR process_name="tzutil.exe" OR process_name="ucsvc.exe" OR process_name="unlodctr.exe" OR process_name="unregmp2.exe" OR process_name="upnpcont.exe" OR process_name="userinit.exe" OR process_name="vds.exe" OR process_name="vdsldr.exe" OR process_name="verclsid.exe" OR process_name="verifier.exe" OR process_name="verifiergui.exe" OR process_name="vmicsvc.exe" OR process_name="vssadmin.exe" OR process_name="w32tm.exe" OR process_name="waitfor.exe" OR process_name="wbadmin.exe" OR process_name="wbengine.exe" OR process_name="wecutil.exe" OR process_name="wermgr.exe" OR process_name="wevtutil.exe" OR process_name="wextract.exe" OR process_name="where.exe" OR process_name="whoami.exe" OR process_name="wiaacmgr.exe" OR process_name="wiawow64.exe" OR process_name="wifitask.exe" OR process_name="wimserv.exe" OR process_name="wininit.exe" OR process_name="winload.exe" OR process_name="winlogon.exe" OR process_name="winresume.exe" OR process_name="winrs.exe" OR process_name="winrshost.exe" OR process_name="winver.exe" OR process_name="wisptis.exe" OR process_name="wkspbroker.exe" OR process_name="wksprt.exe" OR process_name="wlanext.exe" OR process_name="wlrmdr.exe" OR process_name="wowreg32.exe" OR process_name="wpnpinst.exe" OR process_name="wpr.exe" OR process_name="write.exe" OR process_name="wscript.exe" OR process_name="wsmprovhost.exe" OR process_name="wsqmcons.exe" OR process_name="wuapihost.exe" OR process_name="wuapp.exe" OR process_name="wuauclt.exe" OR process_name="wusa.exe" OR process_name="xcopy.exe" OR process_name="xpsrchvw.exe" OR process_name="xwizard.exe"; -| from $cond_1 -| union $cond_2 -| union $cond_3 -| union $cond_4 -| union $cond_5 -| union $cond_6 -| where process_path!="c:\\windows\\system32" AND process_path!="c:\\windows\\syswow64" -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(device, user), body = "TBD" +| from $cond_1 +| union $cond_2 +| union $cond_3 +| union $cond_4 +| union $cond_5 +| union $cond_6 +| where process_path!="c:\\windows\\system32" AND process_path!="c:\\windows\\syswow64" +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(device, user), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -14574,7 +14574,7 @@ None This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2020-12-08 @@ -14584,19 +14584,19 @@ This search looks for system processes that normally run out of C:\Windows\Syste ====Search==== | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name Processes.process_hash -| `drop_dm_object_name("Processes")` +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| `is_windows_system_file` +| `is_windows_system_file` | `system_processes_run_from_unexpected_locations_filter` ====Associated Analytic Story==== -* Suspicious Command-Line Executions +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Command-Line_Executions|Suspicious Command-Line Executions]] -* Unusual Processes +* [[Documentation:ESSOC:stories:UseCase#Unusual_Processes|Unusual Processes]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] ====How To Implement==== @@ -14651,18 +14651,18 @@ The fsutil.exe application is a legitimate Windows utility used to perform tasks ====Search==== -| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| search process="*deletejournal*" AND process="*usn*" +| `security_content_ctime(lastTime)` +| search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter` ====Associated Analytic Story==== -* Windows Log Manipulation +* [[Documentation:ESSOC:stories:UseCase#Windows_Log_Manipulation|Windows Log Manipulation]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] ====How To Implement==== @@ -14717,16 +14717,16 @@ Attackers often disable security tools to avoid detection. This search looks for ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user -| `drop_dm_object_name("Processes")` +| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +|`security_content_ctime(lastTime)` |`unload_sysmon_filter_driver_filter` | table firstTime lastTime dest user count process_name process_id parent_process_name process ====Associated Analytic Story==== -* Disabling Security Tools +* [[Documentation:ESSOC:stories:UseCase#Disabling_Security_Tools|Disabling Security Tools]] ====How To Implement==== @@ -14772,27 +14772,27 @@ You must be ingesting data that records process activity from your hosts to popu Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez. * '''Product''': UEBA for Security Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2020-10-06
====Search==== - -| from read_ssa_enriched_events() -| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) -| eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) -| where cmd_line!=null and dest_user_id!=null + +| from read_ssa_enriched_events() +| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) +| eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) +| where cmd_line!=null and dest_user_id!=null | eval cmd_line_norm=replace(cast(cmd_line, "string"), /\s(--?\w+) -|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) -| select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input -| adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 -| where label AND quantile>0.99 -| first_time_event input_columns=["dest_device_id", "cmd_line"] -| where first_time_dest_device_id_cmd_line -| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" +|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) +| select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input +| adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 +| where label AND quantile>0.99 +| first_time_event input_columns=["dest_device_id", "cmd_line"] +| where first_time_dest_device_id_cmd_line +| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" | into write_ssa_detected_events(); ====Associated Analytic Story==== @@ -14845,8 +14845,8 @@ This detection may flag suspiciously long command lines when there is not suffic Command lines that are extremely long may be indicative of malicious activity on your hosts. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2020-12-08
@@ -14854,26 +14854,26 @@ Command lines that are extremely long may be indicative of malicious activity on ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process -| `drop_dm_object_name("Processes")` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process +| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| eval processlen=len(process) -| eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest -| stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process -| `unusually_long_command_line_filter` -|eval threshold = 3 +| eval processlen=len(process) +| eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest +| stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process +| `unusually_long_command_line_filter` +|eval threshold = 3 | where maxlen > ((threshold*stdevperhost) + avgperhost) ====Associated Analytic Story==== -* Suspicious Command-Line Executions +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Command-Line_Executions|Suspicious Command-Line Executions]] -* Unusual Processes +* [[Documentation:ESSOC:stories:UseCase#Unusual_Processes|Unusual Processes]] -* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns +* [[Documentation:ESSOC:stories:UseCase#Possible_Backdoor_Activity_Associated_With_MUDCARP_Espionage_Campaigns|Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] ====How To Implement==== @@ -14915,8 +14915,8 @@ Some legitimate applications start with long command lines. Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2019-05-08
@@ -14924,27 +14924,27 @@ Command lines that are extremely long may be indicative of malicious activity on ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process -| `drop_dm_object_name(Processes)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process +| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -| eval processlen=len(process) -| search user!=unknown -| apply cmdline_pdfmodel threshold=0.01 -| rename "IsOutlier(processlen)" as isOutlier -| search isOutlier > 0 -| table firstTime lastTime user dest process_name process processlen count +| eval processlen=len(process) +| search user!=unknown +| apply cmdline_pdfmodel threshold=0.01 +| rename "IsOutlier(processlen)" as isOutlier +| search isOutlier > 0 +| table firstTime lastTime user dest process_name process processlen count | `unusually_long_command_line___mltk_filter` ====Associated Analytic Story==== -* Suspicious Command-Line Executions +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Command-Line_Executions|Suspicious Command-Line Executions]] -* Unusual Processes +* [[Documentation:ESSOC:stories:UseCase#Unusual_Processes|Unusual Processes]] -* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns +* [[Documentation:ESSOC:stories:UseCase#Possible_Backdoor_Activity_Associated_With_MUDCARP_Espionage_Campaigns|Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] ====How To Implement==== @@ -14993,17 +14993,17 @@ This search looks for flags passed to wbadmin.exe (Windows Backup Administrator ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wbadmin_delete_system_backups_filter` ====Associated Analytic Story==== -* Ryuk Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] ====How To Implement==== @@ -15057,7 +15057,7 @@ Administrators may modify the boot configuration. This search looks for the creation of WMI permanent event subscriptions. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.003/ T1546.003] * '''Last Updated''': 2020-12-08 @@ -15065,14 +15065,14 @@ This search looks for the creation of WMI permanent event subscriptions.
====Search==== -`sysmon` EventCode=21 -| rename host as dest -| table _time, dest, user, Operation, EventType, Query, Consumer, Filter +`sysmon` EventCode=21 +| rename host as dest +| table _time, dest, user, Operation, EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter` ====Associated Analytic Story==== -* Suspicious WMI Use +* [[Documentation:ESSOC:stories:UseCase#Suspicious_WMI_Use|Suspicious WMI Use]] ====How To Implement==== @@ -15126,16 +15126,16 @@ This search looks for the execution of `adfind.exe` with command-line arguments
====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `windows_adfind_exe_filter` ====Associated Analytic Story==== -* Sunburst Malware +* [[Documentation:ESSOC:stories:UseCase#Sunburst_Malware|Sunburst Malware]] ====How To Implement==== @@ -15185,7 +15185,7 @@ administrators rarely use adfind, usually not used for legitimate reasons This search looks for Windows events that indicate one of the Windows event logs has been purged. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1070.001/ T1070.001] * '''Last Updated''': 2020-07-06 @@ -15193,17 +15193,17 @@ This search looks for Windows events that indicate one of the Windows event logs
====Search==== -(`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) -| stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +(`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) +| stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `windows_event_log_cleared_filter` ====Associated Analytic Story==== -* Windows Log Manipulation +* [[Documentation:ESSOC:stories:UseCase#Windows_Log_Manipulation|Windows Log Manipulation]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] ====How To Implement==== @@ -15251,7 +15251,7 @@ It is possible that these logs may be legitimately cleared by Administrators. The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1489/ T1489] * '''Last Updated''': 2020-11-06 @@ -15260,15 +15260,15 @@ The search looks for a Windows Security Account Manager (SAM) was stopped via co ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" -| `drop_dm_object_name(Processes)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" +| `drop_dm_object_name(Processes)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` | `windows_security_account_manager_stopped_filter` ====Associated Analytic Story==== -* Ryuk Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]] ====How To Implement==== @@ -15328,26 +15328,26 @@ This search allows you to identify DNS requests that are unusually large for the ====Search==== -| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type -| search DNS.record_type=* -| `drop_dm_object_name(DNS)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| eval query_length = len(query) -| apply dns_query_pdfmodel threshold=0.01 -| rename "IsOutlier(query_length)" as isOutlier -| search isOutlier > 0 -| sort -query_length -| table start_time end_time query record_type count src dest query_length +| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type +| search DNS.record_type=* +| `drop_dm_object_name(DNS)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval query_length = len(query) +| apply dns_query_pdfmodel threshold=0.01 +| rename "IsOutlier(query_length)" as isOutlier +| search isOutlier > 0 +| sort -query_length +| table start_time end_time query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` ====Associated Analytic Story==== -* Hidden Cobra Malware +* [[Documentation:ESSOC:stories:UseCase#Hidden_Cobra_Malware|Hidden Cobra Malware]] -* Suspicious DNS Traffic +* [[Documentation:ESSOC:stories:UseCase#Suspicious_DNS_Traffic|Suspicious DNS Traffic]] -* Command and Control +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] ====How To Implement==== @@ -15406,22 +15406,22 @@ This search allows you to identify DNS requests and compute the standard deviati ====Search==== -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query -| `drop_dm_object_name("DNS")` -| eval query_length = len(query) -| table query query_length record_type count +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query +| `drop_dm_object_name("DNS")` +| eval query_length = len(query) +| table query query_length record_type count | eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50 -| where query_length>(avg+stdev*2) -| eval z_score=(query_length-avg)/stdev +| where query_length>(avg+stdev*2) +| eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter` ====Associated Analytic Story==== -* Hidden Cobra Malware +* [[Documentation:ESSOC:stories:UseCase#Hidden_Cobra_Malware|Hidden Cobra Malware]] -* Suspicious DNS Traffic +* [[Documentation:ESSOC:stories:UseCase#Suspicious_DNS_Traffic|Suspicious DNS Traffic]] -* Command and Control +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] ====How To Implement==== @@ -15467,7 +15467,7 @@ It's possible there can be long domain names that are legitimate. By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557.002/ T1557.002] * '''Last Updated''': 2020-10-28 @@ -15475,18 +15475,18 @@ By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organiz
====Search==== -`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") -| eval src_interface=src_int_prefix_long+src_int_suffix -| eval dest_interface=dest_int_prefix_long+dest_int_suffix -| stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface -| table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count -| `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") +| eval src_interface=src_int_prefix_long+src_int_suffix +| eval dest_interface=dest_int_prefix_long+dest_int_suffix +| stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface +| table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` | `detect_ipv6_network_infrastructure_threats_filter` ====Associated Analytic Story==== -* Router and Infrastructure Security +* [[Documentation:ESSOC:stories:UseCase#Router_and_Infrastructure_Security|Router and Infrastructure Security]] ====How To Implement==== @@ -15567,16 +15567,16 @@ This search looks for outbound ICMP packets with a packet size larger than 1,000 ====Search==== -| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip -| `drop_dm_object_name("All_Traffic")` -| search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) +| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip +| `drop_dm_object_name("All_Traffic")` +| search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter` ====Associated Analytic Story==== -* Command and Control +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] ====How To Implement==== @@ -15629,19 +15629,19 @@ This search looks for outbound SMB connections made by hosts within your network ====Search==== -| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip -| `drop_dm_object_name("All_Traffic")` -| `security_content_ctime(start_time)` -| `security_content_ctime(end_time)` +| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip +| `drop_dm_object_name("All_Traffic")` +| `security_content_ctime(start_time)` +| `security_content_ctime(end_time)` | `detect_outbound_smb_traffic_filter` ====Associated Analytic Story==== -* Hidden Cobra Malware +* [[Documentation:ESSOC:stories:UseCase#Hidden_Cobra_Malware|Hidden Cobra Malware]] -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] -* Sunburst Malware +* [[Documentation:ESSOC:stories:UseCase#Sunburst_Malware|Sunburst Malware]] ====How To Implement==== @@ -15687,7 +15687,7 @@ It is likely that the outbound Server Message Block (SMB) traffic is legitimate, By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557.002/ T1557.002] * '''Last Updated''': 2020-10-28 @@ -15695,16 +15695,16 @@ By enabling Port Security on a Cisco switch you can restrict input to an interfa
====Search==== -`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") -| eval src_interface=src_int_prefix_long+src_int_suffix -| stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") +| eval src_interface=src_int_prefix_long+src_int_suffix +| stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` | `detect_port_security_violation_filter` ====Associated Analytic Story==== -* Router and Infrastructure Security +* [[Documentation:ESSOC:stories:UseCase#Router_and_Infrastructure_Security|Router and Infrastructure Security]] ====How To Implement==== @@ -15762,7 +15762,7 @@ This search might be prone to high false positives if you have malfunctioning de By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557/ T1557] * '''Last Updated''': 2020-08-11 @@ -15770,15 +15770,15 @@ By enabling DHCP Snooping as a Layer 2 Security measure on the organization's ne
====Search==== -`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" -| stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host +`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" +| stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detect_rogue_dhcp_server_filter` ====Associated Analytic Story==== -* Router and Infrastructure Security +* [[Documentation:ESSOC:stories:UseCase#Router_and_Infrastructure_Security|Router and Infrastructure Security]] ====How To Implement==== @@ -15834,7 +15834,7 @@ This search might be prone to high false positives if DHCP Snooping has been inc This search looks for commands that the SNICat tool uses in the TLS SNI field. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1041/ T1041] * '''Last Updated''': 2020-10-21 @@ -15842,7 +15842,7 @@ This search looks for commands that the SNICat tool uses in the TLS SNI field.
====Search==== -`zeek_ssl` +`zeek_ssl` | rex field=server_name "(?(LIST |LS |SIZE @@ -15853,15 +15853,15 @@ This search looks for commands that the SNICat tool uses in the TLS SNI field. |ALIVE |EXIT |WHERE -|finito)-[A-Za-z0-9]{16}\.)" -| stats count by src_ip dest_ip server_name snicat -| where count>0 -| table src_ip dest_ip server_name snicat +|finito)-[A-Za-z0-9]{16}\.)" +| stats count by src_ip dest_ip server_name snicat +| where count>0 +| table src_ip dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter` ====Associated Analytic Story==== -* Data Exfiltration +* [[Documentation:ESSOC:stories:UseCase#Data_Exfiltration|Data Exfiltration]] ====How To Implement==== @@ -15920,15 +15920,15 @@ Adversaries may abuse netbooting to load an unauthorized network device operatin ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port -| `drop_dm_object_name("All_Traffic")` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter` ====Associated Analytic Story==== -* Router and Infrastructure Security +* [[Documentation:ESSOC:stories:UseCase#Router_and_Infrastructure_Security|Router and Infrastructure Security]] ====How To Implement==== @@ -15972,7 +15972,7 @@ This search will also report any legitimate attempts of software downloads to ne Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1020.001/ T1020.001] * '''Last Updated''': 2020-10-28 @@ -15980,15 +15980,15 @@ Adversaries may leverage traffic mirroring in order to automate data exfiltratio
====Search==== -`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") -| stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic +`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") +| stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic | `security_content_ctime(firstTime)` -|`security_content_ctime(lastTime)` +|`security_content_ctime(lastTime)` | `detect_traffic_mirroring_filter` ====Associated Analytic Story==== -* Router and Infrastructure Security +* [[Documentation:ESSOC:stories:UseCase#Router_and_Infrastructure_Security|Router and Infrastructure Security]] ====How To Implement==== @@ -16043,7 +16043,7 @@ By populating the organization's assets within the assets_by_str.csv, we will be * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Network_Sessions -* '''ATT&CK''': +* '''ATT&CK''': * '''Last Updated''': 2017-09-13
@@ -16051,19 +16051,19 @@ By populating the organization's assets within the assets_by_str.csv, we will be ====Search==== -| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac +| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac | dedup All_Sessions.dest_mac | `drop_dm_object_name("Network_Sessions")` -|`drop_dm_object_name("All_Sessions")` +|`drop_dm_object_name("All_Sessions")` | search NOT [ -| inputlookup asset_lookup_by_str -|rename mac as dest_mac -| fields + dest_mac] +| inputlookup asset_lookup_by_str +|rename mac as dest_mac +| fields + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter` ====Associated Analytic Story==== -* Asset Tracking +* [[Documentation:ESSOC:stories:UseCase#Asset_Tracking|Asset Tracking]] ====How To Implement==== @@ -16107,7 +16107,7 @@ This search might be prone to high false positives. Please consider this when co This search detects SIGRed via Splunk Stream. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203] * '''Last Updated''': 2020-07-28 @@ -16115,20 +16115,20 @@ This search detects SIGRed via Splunk Stream.
====Search==== -`stream_dns` -| spath "query_type{}" -| search "query_type{}" IN (SIG,KEY) -| spath protocol_stack -| search protocol_stack="ip:tcp:dns" -| append [search `stream_tcp` bytes_out>65000] -| `detect_windows_dns_sigred_via_splunk_stream_filter` -| stats count by flow_id -| where count>1 +`stream_dns` +| spath "query_type{}" +| search "query_type{}" IN (SIG,KEY) +| spath protocol_stack +| search protocol_stack="ip:tcp:dns" +| append [search `stream_tcp` bytes_out>65000] +| `detect_windows_dns_sigred_via_splunk_stream_filter` +| stats count by flow_id +| where count>1 | fields - count ====Associated Analytic Story==== -* Windows DNS SIGRed CVE-2020-1350 +* [[Documentation:ESSOC:stories:UseCase#Windows_DNS_SIGRed_CVE-2020-1350|Windows DNS SIGRed CVE-2020-1350]] ====How To Implement==== @@ -16183,19 +16183,19 @@ This search detects SIGRed via Zeek DNS and Zeek Conn data. ====Search==== -| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id -| rename DNS.flow_id as flow_id +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id +| rename DNS.flow_id as flow_id | append [ -| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id -| rename All_Traffic.flow_id as flow_id] -| `detect_windows_dns_sigred_via_zeek_filter` -| stats count by flow_id -| where count>1 +| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id +| rename All_Traffic.flow_id as flow_id] +| `detect_windows_dns_sigred_via_zeek_filter` +| stats count by flow_id +| where count>1 | fields - count ====Associated Analytic Story==== -* Windows DNS SIGRed CVE-2020-1350 +* [[Documentation:ESSOC:stories:UseCase#Windows_DNS_SIGRed_CVE-2020-1350|Windows DNS SIGRed CVE-2020-1350]] ====How To Implement==== @@ -16241,7 +16241,7 @@ unknown This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1190/ T1190] * '''Last Updated''': 2020-09-15 @@ -16249,15 +16249,15 @@ This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vul
====Search==== -`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) -| bin span=5m _time -| stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip -| search opscount=3 authcount>4 passcount>0 +`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) +| bin span=5m _time +| stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip +| search opscount=3 authcount>4 passcount>0 | search `detect_zerologon_via_zeek_filter` ====Associated Analytic Story==== -* Detect Zerologon Attack +* [[Documentation:ESSOC:stories:UseCase#Detect_Zerologon_Attack|Detect Zerologon Attack]] ====How To Implement==== @@ -16316,25 +16316,25 @@ Malicious actors often abuse legitimate Dynamic DNS services to host malicious p ====Search==== -| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host -| `drop_dm_object_name("DNS")` -| `security_content_ctime(firstTime)` -| `dynamic_dns_providers` +| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host +| `drop_dm_object_name("DNS")` +| `security_content_ctime(firstTime)` +| `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter` ====Associated Analytic Story==== -* Data Protection +* [[Documentation:ESSOC:stories:UseCase#Data_Protection|Data Protection]] -* Prohibited Traffic Allowed or Protocol Mismatch +* [[Documentation:ESSOC:stories:UseCase#Prohibited_Traffic_Allowed_or_Protocol_Mismatch|Prohibited Traffic Allowed or Protocol Mismatch]] -* DNS Hijacking +* [[Documentation:ESSOC:stories:UseCase#DNS_Hijacking|DNS Hijacking]] -* Suspicious DNS Traffic +* [[Documentation:ESSOC:stories:UseCase#Suspicious_DNS_Traffic|Suspicious DNS Traffic]] -* Dynamic DNS +* [[Documentation:ESSOC:stories:UseCase#Dynamic_DNS|Dynamic DNS]] -* Command and Control +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] ====How To Implement==== @@ -16397,18 +16397,18 @@ This search looks for RDP application network traffic and filters any source/des ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port -| eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 -| where count>(avg + stdev*2) -| rename All_Traffic.src AS src All_Traffic.dest AS dest -| table firstTime lastTime src dest count avg p50 stdev +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 +| where count>(avg + stdev*2) +| rename All_Traffic.src AS src All_Traffic.dest AS dest +| table firstTime lastTime src dest count avg p50 stdev | `remote_desktop_network_bruteforce_filter` ====Associated Analytic Story==== -* SamSam Ransomware +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] -* Ryuk Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]] ====How To Implement==== @@ -16463,21 +16463,21 @@ This search looks for network traffic on TCP/3389, the default port used by remo ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port -| `drop_dm_object_name("All_Traffic")` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` +| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` ====Associated Analytic Story==== -* SamSam Ransomware +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] -* Ryuk Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]] -* Hidden Cobra Malware +* [[Documentation:ESSOC:stories:UseCase#Hidden_Cobra_Malware|Hidden Cobra Malware]] -* Lateral Movement +* [[Documentation:ESSOC:stories:UseCase#Lateral_Movement|Lateral Movement]] ====How To Implement==== @@ -16530,24 +16530,24 @@ This search looks for spikes in the number of Server Message Block (SMB) traffic ====Search==== -| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src -| `drop_dm_object_name("All_Traffic")` -| eventstats max(_time) as maxtime -| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) -| where isOutlier=1 -| table src count +| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src +| `drop_dm_object_name("All_Traffic")` +| eventstats max(_time) as maxtime +| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) +| where isOutlier=1 +| table src count | `smb_traffic_spike_filter` ====Associated Analytic Story==== -* Emotet Malware DHS Report TA18-201A +* [[Documentation:ESSOC:stories:UseCase#Emotet_Malware__DHS_Report_TA18-201A_|Emotet Malware DHS Report TA18-201A ]] -* Hidden Cobra Malware +* [[Documentation:ESSOC:stories:UseCase#Hidden_Cobra_Malware|Hidden Cobra Malware]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] ====How To Implement==== @@ -16600,26 +16600,26 @@ This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the n ====Search==== -| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src -| eval HourOfDay=strftime(_time, "%H") -| eval DayOfWeek=strftime(_time, "%A") -| `drop_dm_object_name(All_Traffic)` -| apply smb_pdfmodel threshold=0.001 -| rename "IsOutlier(count)" as isOutlier -| search isOutlier > 0 -| sort -count -| table _time src dest port count +| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src +| eval HourOfDay=strftime(_time, "%H") +| eval DayOfWeek=strftime(_time, "%A") +| `drop_dm_object_name(All_Traffic)` +| apply smb_pdfmodel threshold=0.001 +| rename "IsOutlier(count)" as isOutlier +| search isOutlier > 0 +| sort -count +| table _time src dest port count | `smb_traffic_spike___mltk_filter` ====Associated Analytic Story==== -* Emotet Malware DHS Report TA18-201A +* [[Documentation:ESSOC:stories:UseCase#Emotet_Malware__DHS_Report_TA18-201A_|Emotet Malware DHS Report TA18-201A ]] -* Hidden Cobra Malware +* [[Documentation:ESSOC:stories:UseCase#Hidden_Cobra_Malware|Hidden Cobra Malware]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] -* DHS Report TA18-074A +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] ====How To Implement==== @@ -16675,21 +16675,21 @@ This search looks for network traffic identified as The Onion Router (TOR), a be ====Search==== -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `drop_dm_object_name("All_Traffic")` +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` | `tor_traffic_filter` ====Associated Analytic Story==== -* Prohibited Traffic Allowed or Protocol Mismatch +* [[Documentation:ESSOC:stories:UseCase#Prohibited_Traffic_Allowed_or_Protocol_Mismatch|Prohibited Traffic Allowed or Protocol Mismatch]] -* Ransomware +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] -* Command and Control +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] -* Sunburst Malware +* [[Documentation:ESSOC:stories:UseCase#Sunburst_Malware|Sunburst Malware]] ====How To Implement==== @@ -16733,23 +16733,23 @@ None at this time This search looks for unusually long strings in the Content-Type http header that the client sends the server. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2017-10-13
====Search==== -`stream_http` -| eval cs_content_type_length = len(cs_content_type) -| where cs_content_type_length > 100 -| table endtime src_ip dest_ip cs_content_type_length cs_content_type url +`stream_http` +| eval cs_content_type_length = len(cs_content_type) +| where cs_content_type_length > 100 +| table endtime src_ip dest_ip cs_content_type_length cs_content_type url | `unusually_long_content_type_length_filter` ====Associated Analytic Story==== -* Apache Struts Vulnerability +* [[Documentation:ESSOC:stories:UseCase#Apache_Struts_Vulnerability|Apache Struts Vulnerability]] ====How To Implement==== @@ -16794,7 +16794,7 @@ Very few legitimate Content-Type fields will have a length greater than 100 char This search is used to identify the creation of multiple user accounts using the same email domain name. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1136/ T1136] * '''Last Updated''': 2018-10-08 @@ -16802,19 +16802,19 @@ This search is used to identify the creation of multiple user accounts using the
====Search==== -`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" -| rex field=cookie "form_key=(?\w+)" +`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" +| rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^& -|^$]+)" -| search Username=* -| rex field=Username "@(?.*)" -| stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain -| where UniqueUsernames> 25 +|^$]+)" +| search Username=* +| rex field=Username "@(?.*)" +| stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain +| where UniqueUsernames> 25 | `web_fraud___account_harvesting_filter` ====Associated Analytic Story==== -* Web Fraud Detection +* [[Documentation:ESSOC:stories:UseCase#Web_Fraud_Detection|Web Fraud Detection]] ====How To Implement==== @@ -16862,7 +16862,7 @@ As is common with many fraud-related searches, we are usually looking to attribu This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': +* '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] * '''Last Updated''': 2018-10-08 @@ -16870,17 +16870,17 @@ This search is used to examine web sessions to identify those where the clicks a
====Search==== -`stream_http` http_content_type=text* -| rex field=cookie "form_key=(?\w+)" -| streamstats window=2 current=1 range(_time) as TimeDelta by session_id -| where TimeDelta>0 -|stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id -| where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) +`stream_http` http_content_type=text* +| rex field=cookie "form_key=(?\w+)" +| streamstats window=2 current=1 range(_time) as TimeDelta by session_id +| where TimeDelta>0 +|stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id +| where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) | `web_fraud___anomalous_user_clickspeed_filter` ====Associated Analytic Story==== -* Web Fraud Detection +* [[Documentation:ESSOC:stories:UseCase#Web_Fraud_Detection|Web Fraud Detection]] ====How To Implement==== @@ -16932,26 +16932,26 @@ As is common with many fraud-related searches, we are usually looking to attribu This search is used to identify user accounts that share a common password. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': -* '''ATT&CK''': +* '''Datamodel''': +* '''ATT&CK''': * '''Last Updated''': 2018-10-08
====Search==== -`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* +`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data "login\[username\]=(?[^& -|^$]+)" +|^$]+)" | rex field=form_data "login\[password\]=(?[^& -|^$]+)" +|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password -|where UniqueUsernames>5 +|where UniqueUsernames>5 | `web_fraud___password_sharing_across_accounts_filter` ====Associated Analytic Story==== -* Web Fraud Detection +* [[Documentation:ESSOC:stories:UseCase#Web_Fraud_Detection|Web Fraud Detection]] ====How To Implement==== @@ -16996,4 +16996,4 @@ As is common with many fraud-related searches, we are usually looking to attribu -[[Category:V:ESSOC:3.15.0]] +[[Category:V:ESSOC:draft]] \ No newline at end of file From 14915ca789aef56c7eefb6a6a41adf8eee710fcf Mon Sep 17 00:00:00 2001 From: divious1 Date: Tue, 2 Mar 2021 15:37:50 -0500 Subject: [PATCH 10/62] adding pyattck to libs --- requirements.txt | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/requirements.txt b/requirements.txt index 8919522ce6..89550cd921 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,32 +3,51 @@ appdirs==1.4.4 aspy.yaml==1.3.0 attackcti==0.3.4.3 attrs==20.3.0 +CacheControl==0.12.6 certifi==2020.12.5 cfgv==3.2.0 chardet==4.0.0 +colorama==0.4.3 coloredlogs==14.0 configparser==5.0.1 contextlib2==0.6.0.post1 distlib==0.3.1 +distro==1.4.0 filelock==3.0.12 +fire==0.3.1 gitdb==4.0.5 +html5lib==1.0.1 humanfriendly==9.1 identify==1.5.13 idna==2.10 importlib-metadata==3.4.0 importlib-resources==5.1.0 +ipaddr==2.2.0 Jinja2==2.11.3 jsonschema==3.2.0 +lockfile==0.12.2 MarkupSafe==1.1.1 more-itertools==8.6.0 +msgpack==0.6.2 nodeenv==1.5.0 +packaging==20.3 pathlib2==2.3.5 +pendulum==1.2.5 +pep517==0.8.2 +Pillow==8.1.1 pre-commit==2.9.3 +progress==1.5 +pyattck==2.1.3 +pyfiglet==0.8.post1 +pyparsing==2.4.6 pyrsistent==0.17.3 python-dateutil==2.8.1 +pytoml==0.1.21 pytz==2021.1 +pytzdata==2020.1 PyYAML==5.4.1 requests==2.25.1 +retrying==1.3.3 scandir==1.10.0 semantic-version==2.8.5 simplejson==3.17.2 @@ -36,9 +55,13 @@ six==1.15.0 sly==0.4 smmap==3.0.5 stix2==2.1.0 +stix2-patterns==1.3.2 taxii2-client==2.2.2 +termcolor==1.1.0 toml==0.10.2 typing==3.7.4.3 +tzlocal==2.1 urllib3==1.26.3 virtualenv==20.4.2 +webencodings==0.5.1 zipp==3.4.0 From 329a1ef56204d1308997d35181c4bb50db81a135 Mon Sep 17 00:00:00 2001 From: divious1 Date: Wed, 3 Mar 2021 11:40:39 -0500 Subject: [PATCH 11/62] started stories --- bin/doc_gen.py | 130 +- bin/jinja2_templates/doc_stories_markdown.j2 | 50 + docs/detections.wiki | 2 +- docs/stories.md | 3147 ++++++++++++++++++ requirements.txt | 14 + 5 files changed, 3335 insertions(+), 8 deletions(-) create mode 100644 bin/jinja2_templates/doc_stories_markdown.j2 create mode 100644 docs/stories.md diff --git a/bin/doc_gen.py b/bin/doc_gen.py index baec9d7542..60474c8751 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -36,8 +36,117 @@ def get_mitre_enrichment_new(attack, mitre_attack_id): mitre_attack = mitre_attack_object(technique, attack) return mitre_attack -def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): +def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE): + manifest_files = [] + for root, dirs, files in walk(REPO_PATH + '/stories'): + for file in files: + if file.endswith(".yml"): + manifest_files.append((path.join(root, file))) + stories = [] + for manifest_file in manifest_files: + story_yaml = dict() + if VERBOSE: + print("processing manifest {0}".format(manifest_file)) + + with open(manifest_file, 'r') as stream: + try: + object = list(yaml.safe_load_all(stream))[0] + except yaml.YAMLError as exc: + print(exc) + print("Error reading {0}".format(manifest_file)) + error = True + continue + story_yaml = object + + # enrich the mitre object + mitre_attacks = [] + if 'mitre_attack_id' in story_yaml['tags']: + for mitre_technique_id in story_yaml['tags']['mitre_attack_id']: + mitre_attack = get_mitre_enrichment_new(attack, mitre_technique_id) + mitre_attacks.append(mitre_attack) + story_yaml['mitre_attacks'] = mitre_attacks + stories.append(story_yaml) + + sorted_stories = sorted(stories, key=lambda i: i['name']) + + # enrich stories with information from detections: data_models, mitre_ids, kill_chain_phases + sto_to_data_models = {} + sto_to_mitre_attack_ids = {} + sto_to_kill_chain_phases = {} + sto_to_det = {} + for detection in sorted_detections: + if 'analytic_story' in detection['tags']: + for story in detection['tags']['analytic_story']: + if story in sto_to_det.keys(): + sto_to_det[story].add(detection['name']) + else: + sto_to_det[story] = {detection['name']} + + data_model = detection['datamodel'] + if data_model: + for d in data_model: + if story in sto_to_data_models.keys(): + sto_to_data_models[story].add(d) + else: + sto_to_data_models[story] = {d} + + if 'mitre_attack_id' in detection['tags']: + if story in sto_to_mitre_attack_ids.keys(): + for mitre_attack_id in detection['tags']['mitre_attack_id']: + sto_to_mitre_attack_ids[story].add(mitre_attack_id) + else: + sto_to_mitre_attack_ids[story] = set(detection['tags']['mitre_attack_id']) + + if 'kill_chain_phases' in detection['tags']: + if story in sto_to_kill_chain_phases.keys(): + for kill_chain in detection['tags']['kill_chain_phases']: + sto_to_kill_chain_phases[story].add(kill_chain) + else: + sto_to_kill_chain_phases[story] = set(detection['tags']['kill_chain_phases']) + + for story in sorted_stories: + story['detections'] = sorted(sto_to_det[story['name']]) + if story['name'] in sto_to_data_models: + story['data_models'] = sorted(sto_to_data_models[story['name']]) + if story['name'] in sto_to_mitre_attack_ids: + story['mitre_attack_ids'] = sorted(sto_to_mitre_attack_ids[story['name']]) + if story['name'] in sto_to_kill_chain_phases: + story['kill_chain_phases'] = sorted(sto_to_kill_chain_phases[story['name']]) + if story['name'] in sto_to_ciss: + story['ciss'] = sorted(sto_to_ciss[story['name']]) + if story['name'] in sto_to_nists: + story['nists'] = sorted(sto_to_nists[story['name']]) + #sort stories into categories + categories = [] + category_names = set() + for story in sorted_stories: + if 'category' in story['tags']: + category_names.add(story['tags']['category'][0]) + + for category_name in sorted(category_names): + new_category = {} + new_category['name'] = category_name + new_category['stories'] = [] + categories.append(new_category) + + for story in sorted_stories: + for category in categories: + if category['name'] == story['tags']['category'][0]: + category['stories'].append(story) + + j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), + trim_blocks=False) + + # write markdown + template = j2_env.get_template('doc_stories_markdown.j2') + output_path = path.join(OUTPUT_DIR + '/stories.md') + output = template.render(categories=categories) + with open(output_path, 'w', encoding="utf-8") as f: + f.write(output) + messages.append("doc_gen.py wrote {0} stories documentation in markdown to: {1}".format(len(stories),output_path)) + +def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE): types = ["endpoint", "application", "cloud", "network", "web"] manifest_files = [] for t in types: @@ -46,9 +155,6 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): if file.endswith(".yml"): manifest_files.append((path.join(root, file))) - if VERBOSE: - print("getting mitre enrichment data from cti") - attack = Attck() detections = [] for manifest_file in manifest_files: detection_yaml = dict() @@ -86,7 +192,7 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): output = template.render(detections=sorted_detections) with open(output_path, 'w', encoding="utf-8") as f: f.write(output) - print("doc_gen.py wrote {0} detections documentation in markdown to: {1}".format(len(detections),output_path)) + messages.append("doc_gen.py wrote {0} detections documentation in markdown to: {1}".format(len(detections),output_path)) #sort detections by kind into categories kinds = [] @@ -111,8 +217,9 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE): output = template.render(kinds=kinds) with open(output_path, 'w', encoding="utf-8") as f: f.write(output) - print("doc_gen.py wrote {0} detections documentation in mediawiki to: {1}".format(len(detections),output_path)) + messages.append("doc_gen.py wrote {0} detections documentation in mediawiki to: {1}".format(len(detections),output_path)) + return sorted_detections, messages if __name__ == "__main__": # grab arguments @@ -138,10 +245,19 @@ if __name__ == "__main__": TEMPLATE_PATH = path.join(REPO_PATH, 'bin/jinja2_templates') + if VERBOSE: + print("getting mitre enrichment data from cti") + attack = Attck() + + messages = [] if type == 'all': - generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, VERBOSE) + sorted_detections, messages = generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE) + generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE) + # print all the messages from generation + for m in messages: + print(m) print("finished successfully!") # stories = load_objects("stories/*.yml") diff --git a/bin/jinja2_templates/doc_stories_markdown.j2 b/bin/jinja2_templates/doc_stories_markdown.j2 new file mode 100644 index 0000000000..2c8ea192ae --- /dev/null +++ b/bin/jinja2_templates/doc_stories_markdown.j2 @@ -0,0 +1,50 @@ +# Splunk Security Content Analytic Stories +![security_content](static/logo.png) +===== +All the Analytic Stories shipped to different Splunk products. Below is a breakdown by kind. + +{% for category in categories %} +## {{ category.name }} +
+ details +{% for story in category.stories %} +### {{ story.name }} +{{ story.description }} + + +- **Product**: {{ story.tags.product|join(', ') }} +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: {{ story.date }} + +
+ details + +#### Detection Profile +{% for detection in story.detections %} +* {{ detection.name }} +{% endfor %} + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase +{% for phase in story.tags.kill_chain_phases %} +* {{ phase }} +{% endfor %} + +#### Reference +{% for reference in story.references %} +* {{ reference }} +{% endfor %} + +_version_: {{story.version}} +
+ +--- +{% endfor %} +
+{% endfor %} diff --git a/docs/detections.wiki b/docs/detections.wiki index 0844928868..78ffda0338 100644 --- a/docs/detections.wiki +++ b/docs/detections.wiki @@ -16996,4 +16996,4 @@ As is common with many fraud-related searches, we are usually looking to attribu -[[Category:V:ESSOC:draft]] \ No newline at end of file +[[Category:V:ESSOC:drafts]] \ No newline at end of file diff --git a/docs/stories.md b/docs/stories.md new file mode 100644 index 0000000000..c2c167dfc9 --- /dev/null +++ b/docs/stories.md @@ -0,0 +1,3147 @@ +# Splunk Security Content Analytic Stories +![security_content](static/logo.png) +===== +All the Analytic Stories shipped to different Splunk products. Below is a breakdown by kind. + + +## Abuse +
+ details + +### Brand Monitoring +Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-12-19 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.zerofox.com/blog/what-is-digital-risk-monitoring/ + +* https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/ + +* https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/ + + +_version_: 1 +
+ +--- + +### DNS Amplification Attacks +DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2016-09-13 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.us-cert.gov/ncas/alerts/TA13-088A + +* https://www.imperva.com/learn/application-security/dns-amplification/ + + +_version_: 1 +
+ +--- + +### Data Protection +Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-14 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.cisecurity.org/controls/data-protection/ + +* https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022 + +* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ + + +_version_: 1 +
+ +--- + +### Host Redirection +Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-14 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/ + + +_version_: 1 +
+ +--- + +### Netsh Abuse +Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-01-05 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://docs.microsoft.com/en-us/previous-versions/tn-archive/bb490939(v=technet.10) + +* https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html + +* http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html + + +_version_: 1 +
+ +--- + +### Web Fraud Detection +Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-10-08 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud + +* https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718 + + +_version_: 1 +
+ +--- + +
+ +## Adversary Tactics +
+ details + +### Baron Samedit CVE-2021-3156 +Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Research Team, this vulnerability has been found to affect sudo across multiple Linux distributions (Ubuntu 20.04 and prior, Debian 10 and prior, Fedora 33 and prior). As this vulnerability was committed to code in July 2011, there will be many distributions affected. Successful exploitation of this vulnerability allows any unprivileged user to gain root privileges on the vulnerable host. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2021-01-27 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + + +_version_: 1 +
+ +--- + +### Cobalt Strike +Cobalt Strike is threat emulation software. Red teams and penetration testers use Cobalt Strike to demonstrate the risk of a breach and evaluate mature security programs. Most recently, Cobalt Strike has become the choice tool by threat groups due to its ease of use and extensibility. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2021-02-16 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.cobaltstrike.com/ + +* https://www.infocyte.com/blog/2020/09/02/cobalt-strike-the-new-favorite-among-thieves/ + +* https://bluescreenofjeff.com/2017-01-24-how-to-write-malleable-c2-profiles-for-cobalt-strike/ + +* https://blog.talosintelligence.com/2020/09/coverage-strikes-back-cobalt-strike-paper.html + +* https://www.fireeye.com/blog/threat-research/2020/12/unauthorized-access-of-fireeye-red-team-tools.html + + +_version_: 1 +
+ +--- + +### Collection and Staging +Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-02-03 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/wiki/Collection + +* https://attack.mitre.org/wiki/Technique/T1074 + + +_version_: 1 +
+ +--- + +### Command and Control +Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-06-01 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/wiki/Command_and_Control + +* https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware + + +_version_: 1 +
+ +--- + +### Common Phishing Frameworks +Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2019-04-29 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://github.com/kgretzky/evilginx2 + +* https://attack.mitre.org/techniques/T1192/ + +* https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/ + + +_version_: 1 +
+ +--- + +### Credential Dumping +Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-02-04 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/wiki/Technique/T1003 + +* https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html + + +_version_: 3 +
+ +--- + +### DNS Hijacking +Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-02-04 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html + +* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ + +* http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ + +* https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html + + +_version_: 1 +
+ +--- + +### Data Exfiltration +The stealing of data by an adversary. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-10-21 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/tactics/TA0010/ + + +_version_: 1 +
+ +--- + +### Detect Zerologon Attack +Uncover activity related to the execution of Zerologon CVE-2020-11472, a technique wherein attackers target a Microsoft Windows Domain Controller to reset its computer account password. The result from this attack is attackers can now provide themselves high privileges and take over Domain Controller. The included searches in this Analytic Story are designed to identify attempts to reset Domain Controller Computer Account via exploit code remotely or via the use of tool Mimikatz as payload carrier. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-09-18 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/wiki/Technique/T1003 + +* https://github.com/SecuraBV/CVE-2020-1472 + +* https://www.secura.com/blog/zero-logon + +* https://nvd.nist.gov/vuln/detail/CVE-2020-1472 + + +_version_: 1 +
+ +--- + +### Disabling Security Tools +Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-02-04 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/wiki/Technique/T1089 + +* https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/ + +* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf + + +_version_: 2 +
+ +--- + +### F5 TMUI RCE CVE-2020-5902 +Uncover activity consistent with CVE-2020-5902. Discovered by Positive Technologies researchers, this vulnerability affects F5 BIG-IP, BIG-IQ. and Traffix SDC devices (vulnerable versions in F5 support link below). This vulnerability allows unauthenticated users, along with authenticated users, who have access to the configuration utility to execute system commands, create/delete files, disable services, and/or execute Java code. This vulnerability can result in full system compromise. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-08-02 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/ + +* https://support.f5.com/csp/article/K52145254 + +* https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/ + + +_version_: 1 +
+ +--- + +### Lateral Movement +Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-02-04 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html + + +_version_: 2 +
+ +--- + +### Malicious PowerShell +Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-08-23 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/ + +* https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ + + +_version_: 4 +
+ +--- + +### Phishing Payloads +Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2019-04-29 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html + + +_version_: 1 +
+ +--- + +### Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns +Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-01-22 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/ + +* http://blog.amossys.fr/badflick-is-not-so-bad.html + + +_version_: 1 +
+ +--- + +### SQL Injection +Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-19 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://capec.mitre.org/data/definitions/66.html + +* https://www.incapsula.com/web-application-security/sql-injection.html + + +_version_: 1 +
+ +--- + +### Sunburst Malware +Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and management software. It was discovered by FireEye in December 2020. The actors behind this campaign gained access to numerous public and private organizations around the world. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-12-14 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html + +* https://msrc-blog.microsoft.com/2020/12/13/customer-guidance-on-recent-nation-state-cyber-attacks/ + + +_version_: 1 +
+ +--- + +### Suspicious Command-Line Executions +Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-02-03 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/wiki/Technique/T1059 + +* https://www.microsoft.com/en-us/wdsi/threats/macro-malware + +* https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf + + +_version_: 2 +
+ +--- + +### Suspicious Compiled HTML Activity +Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2021-02-11 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +* https://attack.mitre.org/techniques/T1218/001/ + +* https://docs.microsoft.com/en-us/windows/win32/api/htmlhelp/nf-htmlhelp-htmlhelpa + + +_version_: 1 +
+ +--- + +### Suspicious DNS Traffic +Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-18 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/ + +* http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680 + +* https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454 + + +_version_: 1 +
+ +--- + +### Suspicious Emails +Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-01-27 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/ + + +_version_: 1 +
+ +--- + +### Suspicious MSHTA Activity +Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2021-01-20 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +* https://redcanary.com/blog/windows-registry-attacks-threat-detection/ + +* https://attack.mitre.org/techniques/T1218/005/ + +* https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5 + + +_version_: 2 +
+ +--- + +### Suspicious Okta Activity +Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-04-02 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/wiki/Technique/T1078 + +* https://owasp.org/www-community/attacks/Credential_stuffing + +* https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work + + +_version_: 1 +
+ +--- + +### Suspicious Regsvcs Regasm Activity +Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2021-02-11 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/techniques/T1218/009/ + +* https://github.com/rapid7/metasploit-framework/blob/master/documentation/modules/evasion/windows/applocker_evasion_regasm_regsvcs.md + +* https://oddvar.moe/2017/12/13/applocker-case-study-how-insecure-is-it-really-part-1/ + + +_version_: 1 +
+ +--- + +### Suspicious Regsvr32 Activity +Monitor and detect techniques used by attackers who leverage the regsvr32.exe process to execute malicious code. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2021-01-29 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/techniques/T1218/010/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md + +* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ + + +_version_: 1 +
+ +--- + +### Suspicious Rundll32 Activity +Monitor and detect techniques used by attackers who leverage rundll32.exe to execute arbitrary malicious code. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2021-02-03 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/techniques/T1218/011/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + + +_version_: 1 +
+ +--- + +### Suspicious WMI Use +Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-10-23 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf + +* https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html + + +_version_: 2 +
+ +--- + +### Suspicious Windows Registry Activities +Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-05-31 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://redcanary.com/blog/windows-registry-attacks-threat-detection/ + +* https://attack.mitre.org/wiki/Technique/T1112 + + +_version_: 1 +
+ +--- + +### Suspicious Zoom Child Processes +Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-04-13 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/ + +* https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/ + + +_version_: 1 +
+ +--- + +### Trusted Developer Utilities Proxy Execution +Monitor and detect behaviors used by attackers who leverage trusted developer utilities to execute malicious code. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2021-01-12 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/techniques/T1127/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md + +* https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/ + + +_version_: 1 +
+ +--- + +### Trusted Developer Utilities Proxy Execution MSBuild +Monitor and detect techniques used by attackers who leverage the msbuild.exe process to execute malicious code. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2021-01-21 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/techniques/T1127/001/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + +* https://github.com/infosecn1nja/MaliciousMacroMSBuild + +* https://github.com/xorrior/RandomPS-Scripts/blob/master/Invoke-ExecuteMSBuild.ps1 + +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + +* https://github.com/MHaggis/CBR-Queries/blob/master/msbuild.md + + +_version_: 1 +
+ +--- + +### Windows DNS SIGRed CVE-2020-1350 +Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpoint researchers, this vulnerability affects Windows 2003 to 2019, and is triggered by a malicious DNS response (only affects DNS over TCP). An attacker can use the malicious payload to cause a buffer overflow on the vulnerable system, leading to compromise. The included searches in this Analytic Story are designed to identify the large response payload for SIG and KEY DNS records which can be used for the exploit. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-07-28 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + +* https://support.microsoft.com/en-au/help/4569509/windows-dns-server-remote-code-execution-vulnerability + + +_version_: 1 +
+ +--- + +### Windows Defense Evasion Tactics +Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-05-31 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/wiki/Defense_Evasion + + +_version_: 1 +
+ +--- + +### Windows Log Manipulation +Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-12 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ + +* https://zeltser.com/security-incident-log-review-checklist/ + +* http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html + + +_version_: 2 +
+ +--- + +### Windows Persistence Techniques +Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-05-31 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* http://www.fuzzysecurity.com/tutorials/19.html + +* https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html + +* http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/ + +* https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html + +* https://www.youtube.com/watch?v=dq2Hv7J9fvk + + +_version_: 2 +
+ +--- + +### Windows Privilege Escalation +Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-02-04 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/tactics/TA0004/ + + +_version_: 2 +
+ +--- + +
+ +## Best Practices +
+ details + +### Asset Tracking +Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-13 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/ + + +_version_: 1 +
+ +--- + +### Monitor Backup Solution +Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-12 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/ + + +_version_: 1 +
+ +--- + +### Monitor for Unauthorized Software +Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-15 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ + + +_version_: 1 +
+ +--- + +### Monitor for Updates +Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-15 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://learn.cisecurity.org/20-controls-download + + +_version_: 1 +
+ +--- + +### Prohibited Traffic Allowed or Protocol Mismatch +Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-11 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/ + + +_version_: 1 +
+ +--- + +### Router and Infrastructure Security +Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-12 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html + +* https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html + + +_version_: 1 +
+ +--- + +### Use of Cleartext Protocols +Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-15 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.monkey.org/~dugsong/dsniff/ + + +_version_: 1 +
+ +--- + +
+ +## Cloud Security +
+ details + +### AWS Cross Account Activity +Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-06-04 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ + + +_version_: 1 +
+ +--- + +### AWS Cryptomining +Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-03-08 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +_version_: 1 +
+ +--- + +### AWS Network ACL Activity +Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-05-21 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html + +* https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/ + + +_version_: 2 +
+ +--- + +### AWS Security Hub Alerts +This story is focused around detecting Security Hub alerts generated from AWS + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-08-04 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://aws.amazon.com/security-hub/features/ + + +_version_: 1 +
+ +--- + +### AWS Suspicious Provisioning Activities +Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-03-16 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +_version_: 1 +
+ +--- + +### AWS User Monitoring +Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-03-12 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +* https://redlock.io/blog/cryptojacking-tesla + + +_version_: 1 +
+ +--- + +### Cloud Cryptomining +Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2019-10-02 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +_version_: 1 +
+ +--- + +### Cloud Federated Credential Abuse +This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2021-01-26 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + + +_version_: 1 +
+ +--- + +### Container Implantation Monitoring and Investigation +Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-02-20 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://github.com/splunk/cloud-datamodel-security-research + + +_version_: 1 +
+ +--- + +### GCP Cross Account Activity +Track when a user assumes an IAM role in another GCP account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-09-01 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://cloud.google.com/iam/docs/understanding-service-accounts + + +_version_: 1 +
+ +--- + +### Kubernetes Scanning Activity +This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-04-15 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://github.com/splunk/cloud-datamodel-security-research + + +_version_: 1 +
+ +--- + +### Kubernetes Sensitive Object Access Activity +This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + + +_version_: 1 +
+ +--- + +### Kubernetes Sensitive Role Activity +This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + + +_version_: 1 +
+ +--- + +### Office 365 Detections +This story is focused around detecting Office 365 Attacks. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-12-16 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf + + +_version_: 1 +
+ +--- + +### Suspicious AWS EC2 Activities +Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-02-09 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +_version_: 1 +
+ +--- + +### Suspicious AWS Login Activities +Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2019-05-01 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html + + +_version_: 1 +
+ +--- + +### Suspicious AWS S3 Activities +Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-07-24 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +* https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/ + + +_version_: 2 +
+ +--- + +### Suspicious AWS Traffic +Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-05-07 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/ + + +_version_: 1 +
+ +--- + +### Suspicious Cloud Authentication Activities +Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-06-04 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ + +* https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html + + +_version_: 1 +
+ +--- + +### Suspicious Cloud Instance Activities +Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-08-25 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +_version_: 1 +
+ +--- + +### Suspicious Cloud Provisioning Activities +Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-08-20 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +_version_: 1 +
+ +--- + +### Suspicious Cloud User Activities +Detect and investigate suspicious activities by users and roles in your cloud environments. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-09-04 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +* https://redlock.io/blog/cryptojacking-tesla + + +_version_: 1 +
+ +--- + +### Suspicious GCP Storage Activities +Use the searches in this Analytic Story to monitor your GCP Storage buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open storage buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-08-05 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://cloud.google.com/blog/product/gcp/4-steps-for-hardening-your-cloud-storage-buckets-taking-charge-of-your-security + +* https://rhinosecuritylabs.com/gcp/google-cloud-platform-gcp-bucket-enumeration/ + + +_version_: 1 +
+ +--- + +### Unusual AWS EC2 Modifications +Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-04-09 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +_version_: 1 +
+ +--- + +
+ +## Malware +
+ details + +### ColdRoot MacOS RAT +Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2019-01-09 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/ + +* https://objective-see.com/blog/blog_0x2A.html + +* https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/ + + +_version_: 1 +
+ +--- + +### DHS Report TA18-074A +Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-01-22 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.us-cert.gov/ncas/alerts/TA18-074A + + +_version_: 2 +
+ +--- + +### Dynamic DNS +Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and deny lists. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-09-06 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html + +* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ + +* http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ + +* https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html + + +_version_: 2 +
+ +--- + +### Emotet Malware DHS Report TA18-201A +Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-01-27 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.us-cert.gov/ncas/alerts/TA18-201A + +* https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf + +* https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html + + +_version_: 1 +
+ +--- + +### Hidden Cobra Malware +Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-01-22 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity + +* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf + + +_version_: 2 +
+ +--- + +### Orangeworm Attack Group +Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-01-22 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia + +* https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/ + + +_version_: 2 +
+ +--- + +### Ransomware +Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-02-04 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/ + +* https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html + + +_version_: 1 +
+ +--- + +### Ransomware Cloud +Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware. These searches include cloud related objects that may be targeted by malicious actors via cloud providers own encryption features. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-10-27 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ + +* https://github.com/d1vious/git-wild-hunt + +* https://www.youtube.com/watch?v=PgzNib37g0M + + +_version_: 1 +
+ +--- + +### Ryuk Ransomware +Leverage searches that allow you to detect and investigate unusual activities that might relate to the Ryuk ransomware, including looking for file writes associated with Ryuk, Stopping Security Access Manager, DisableAntiSpyware registry key modification, suspicious psexec use, and more. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-11-06 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.splunk.com/en_us/blog/security/detecting-ryuk-using-splunk-attack-range.html + +* https://www.crowdstrike.com/blog/big-game-hunting-with-ryuk-another-lucrative-targeted-ransomware/ + +* https://us-cert.cisa.gov/ncas/alerts/aa20-302a + + +_version_: 1 +
+ +--- + +### SamSam Ransomware +Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-12-13 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/ + +* https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/ + +* https://thehackernews.com/2018/07/samsam-ransomware-attacks.html + + +_version_: 1 +
+ +--- + +### Unusual Processes +Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-02-04 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html + +* https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf + +* https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262 + + +_version_: 2 +
+ +--- + +### Windows File Extension and Association Abuse +Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-01-26 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/ + +* https://attack.mitre.org/wiki/Technique/T1042 + + +_version_: 1 +
+ +--- + +### Windows Service Abuse +Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-11-02 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://attack.mitre.org/wiki/Technique/T1050 + +* https://attack.mitre.org/wiki/Technique/T1031 + + +_version_: 3 +
+ +--- + +
+ +## Vulnerability +
+ details + +### Apache Struts Vulnerability +Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-12-06 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf + + +_version_: 1 +
+ +--- + +### JBoss Vulnerability +In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-14 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html + + +_version_: 1 +
+ +--- + +### Spectre And Meltdown Vulnerabilities +Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-01-08 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://meltdownattack.com/ + + +_version_: 1 +
+ +--- + +### Splunk Enterprise Vulnerability +Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-19 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* http://www.splunk.com/view/SP-CAAAPQ6#announce + +* https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859 + + +_version_: 1 +
+ +--- + +### Splunk Enterprise Vulnerability CVE-2018-11409 +Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-06-14 + +
+ details + +#### Detection Profile + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + + +#### Kill Chain Phase + + +#### Reference + +* https://nvd.nist.gov/vuln/detail/CVE-2018-11409 + +* https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings + +* https://www.exploit-db.com/exploits/44865/ + + +_version_: 1 +
+ +--- + +
diff --git a/requirements.txt b/requirements.txt index 8919522ce6..5a2e738c53 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,6 +11,7 @@ configparser==5.0.1 contextlib2==0.6.0.post1 distlib==0.3.1 filelock==3.0.12 +fire==0.3.1 gitdb==4.0.5 humanfriendly==9.1 identify==1.5.13 @@ -23,11 +24,20 @@ MarkupSafe==1.1.1 more-itertools==8.6.0 nodeenv==1.5.0 pathlib2==2.3.5 +pendulum==1.2.5 +Pillow==8.1.0 pre-commit==2.9.3 +prompt-toolkit==1.0.14 +pyattck==2.1.3 +pyfiglet==0.8.post1 +Pygments==2.8.0 +PyInquirer==1.0.3 pyrsistent==0.17.3 python-dateutil==2.8.1 pytz==2021.1 +pytzdata==2020.1 PyYAML==5.4.1 +regex==2020.11.13 requests==2.25.1 scandir==1.10.0 semantic-version==2.8.5 @@ -36,9 +46,13 @@ six==1.15.0 sly==0.4 smmap==3.0.5 stix2==2.1.0 +stix2-patterns==1.2.1 taxii2-client==2.2.2 +termcolor==1.1.0 toml==0.10.2 typing==3.7.4.3 +tzlocal==2.1 urllib3==1.26.3 virtualenv==20.4.2 +wcwidth==0.2.5 zipp==3.4.0 From 113512d8fd5cb728d2ea709677bd04a73739d6f2 Mon Sep 17 00:00:00 2001 From: divious1 Date: Wed, 3 Mar 2021 14:12:43 -0500 Subject: [PATCH 12/62] working stories --- bin/doc_gen.py | 9 +- docs/detections.md | 8796 ++++++++++++++++++++++++++++++++++++++++++++ docs/stories.md | 1250 ++++++- 3 files changed, 9958 insertions(+), 97 deletions(-) diff --git a/bin/doc_gen.py b/bin/doc_gen.py index 60474c8751..97c3d1969d 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -82,7 +82,6 @@ def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_de sto_to_det[story].add(detection['name']) else: sto_to_det[story] = {detection['name']} - data_model = detection['datamodel'] if data_model: for d in data_model: @@ -106,6 +105,7 @@ def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_de sto_to_kill_chain_phases[story] = set(detection['tags']['kill_chain_phases']) for story in sorted_stories: + print(story) story['detections'] = sorted(sto_to_det[story['name']]) if story['name'] in sto_to_data_models: story['data_models'] = sorted(sto_to_data_models[story['name']]) @@ -113,10 +113,7 @@ def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_de story['mitre_attack_ids'] = sorted(sto_to_mitre_attack_ids[story['name']]) if story['name'] in sto_to_kill_chain_phases: story['kill_chain_phases'] = sorted(sto_to_kill_chain_phases[story['name']]) - if story['name'] in sto_to_ciss: - story['ciss'] = sorted(sto_to_ciss[story['name']]) - if story['name'] in sto_to_nists: - story['nists'] = sorted(sto_to_nists[story['name']]) + #sort stories into categories categories = [] category_names = set() @@ -147,7 +144,7 @@ def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_de messages.append("doc_gen.py wrote {0} stories documentation in markdown to: {1}".format(len(stories),output_path)) def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE): - types = ["endpoint", "application", "cloud", "network", "web"] + types = ["endpoint", "application", "cloud", "network", "web", "experimental", "deprecated"] manifest_files = [] for t in types: for root, dirs, files in walk(REPO_PATH + '/detections/' + t): diff --git a/docs/detections.md b/docs/detections.md index c9b7a290b5..494ab90ce8 100644 --- a/docs/detections.md +++ b/docs/detections.md @@ -9,6 +9,14 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + - [AWS Cross Account Activity From Previously Unseen Account](#aws-cross-account-activity-from-previously-unseen-account) @@ -21,6 +29,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [AWS EKS Kubernetes cluster sensitive object access](#aws-eks-kubernetes-cluster-sensitive-object-access) + + + - [AWS Network Access Control List Created with All Open Ports](#aws-network-access-control-list-created-with-all-open-ports) @@ -37,6 +49,14 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + - [Abnormally High Number Of Cloud Infrastructure API Calls](#abnormally-high-number-of-cloud-infrastructure-api-calls) @@ -55,6 +75,18 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [Amazon EKS Kubernetes Pod scan detection](#amazon-eks-kubernetes-pod-scan-detection) + + + +- [Amazon EKS Kubernetes cluster scan detection](#amazon-eks-kubernetes-cluster-scan-detection) + + + + + + + @@ -101,6 +133,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Cloud Provisioning Activity From Previously Unseen City](#cloud-provisioning-activity-from-previously-unseen-city) @@ -152,6 +186,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + @@ -186,6 +230,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + @@ -204,6 +258,14 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + @@ -262,6 +324,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + @@ -273,6 +337,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Detect Spike in AWS Security Hub Alerts for EC2 Instance](#detect-spike-in-aws-security-hub-alerts-for-ec2-instance) @@ -281,10 +347,14 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Detect Spike in S3 Bucket deletion](#detect-spike-in-s3-bucket-deletion) + + - [Detect Spike in blocked Outbound Traffic from your AWS](#detect-spike-in-blocked-outbound-traffic-from-your-aws) @@ -329,6 +399,68 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +- [GCP Detect accounts with high risk roles by project](#gcp-detect-accounts-with-high-risk-roles-by-project) + + + +- [GCP Detect gcploit framework](#gcp-detect-gcploit-framework) + + + +- [GCP Detect high risk permissions by resource and account](#gcp-detect-high-risk-permissions-by-resource-and-account) + + + + + +- [GCP Kubernetes cluster pod scan detection](#gcp-kubernetes-cluster-pod-scan-detection) + + + +- [GCP Kubernetes cluster scan detection](#gcp-kubernetes-cluster-scan-detection) + + + + + - [High Number of Login Failures from a single source](#high-number-of-login-failures-from-a-single-source) @@ -350,6 +482,98 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + +- [Kubernetes AWS detect RBAC authorization by account](#kubernetes-aws-detect-rbac-authorization-by-account) + + + +- [Kubernetes AWS detect most active service accounts by pod](#kubernetes-aws-detect-most-active-service-accounts-by-pod) + + + +- [Kubernetes AWS detect sensitive role access](#kubernetes-aws-detect-sensitive-role-access) + + + +- [Kubernetes AWS detect service accounts forbidden failure access](#kubernetes-aws-detect-service-accounts-forbidden-failure-access) + + + +- [Kubernetes AWS detect suspicious kubectl calls](#kubernetes-aws-detect-suspicious-kubectl-calls) + + + +- [Kubernetes Azure detect RBAC authorization by account](#kubernetes-azure-detect-rbac-authorization-by-account) + + + +- [Kubernetes Azure detect most active service accounts by pod namespace](#kubernetes-azure-detect-most-active-service-accounts-by-pod-namespace) + + + +- [Kubernetes Azure detect sensitive object access](#kubernetes-azure-detect-sensitive-object-access) + + + +- [Kubernetes Azure detect sensitive role access](#kubernetes-azure-detect-sensitive-role-access) + + + +- [Kubernetes Azure detect service accounts forbidden failure access](#kubernetes-azure-detect-service-accounts-forbidden-failure-access) + + + +- [Kubernetes Azure detect suspicious kubectl calls](#kubernetes-azure-detect-suspicious-kubectl-calls) + + + +- [Kubernetes Azure pod scan fingerprint](#kubernetes-azure-pod-scan-fingerprint) + + + +- [Kubernetes Azure scan fingerprint](#kubernetes-azure-scan-fingerprint) + + + +- [Kubernetes GCP detect RBAC authorizations by account](#kubernetes-gcp-detect-rbac-authorizations-by-account) + + + +- [Kubernetes GCP detect most active service accounts by pod](#kubernetes-gcp-detect-most-active-service-accounts-by-pod) + + + +- [Kubernetes GCP detect sensitive object access](#kubernetes-gcp-detect-sensitive-object-access) + + + +- [Kubernetes GCP detect sensitive role access](#kubernetes-gcp-detect-sensitive-role-access) + + + +- [Kubernetes GCP detect service accounts forbidden failure access](#kubernetes-gcp-detect-service-accounts-forbidden-failure-access) + + + +- [Kubernetes GCP detect suspicious kubectl calls](#kubernetes-gcp-detect-suspicious-kubectl-calls) + + + + + + + + + + @@ -377,6 +601,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [O365 Add App Role Assignment Grant User](#o365-add-app-role-assignment-grant-user) @@ -584,6 +810,98 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +- [aws detect attach to role policy](#aws-detect-attach-to-role-policy) + + + +- [aws detect permanent key creation](#aws-detect-permanent-key-creation) + + + +- [aws detect role creation](#aws-detect-role-creation) + + + +- [aws detect sts assume role abuse](#aws-detect-sts-assume-role-abuse) + + + +- [aws detect sts get session token abuse](#aws-detect-sts-get-session-token-abuse) + + + +- [gcp detect oauth token abuse](#gcp-detect-oauth-token-abuse) + +
## Endpoint @@ -609,6 +927,24 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + @@ -618,6 +954,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + - [Applying Stolen Credentials via Mimikatz modules](#applying-stolen-credentials-via-mimikatz-modules) @@ -662,6 +1002,14 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [Child Processes of Spoolsv exe](#child-processes-of-spoolsv-exe) + + + + + + + @@ -766,6 +1114,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + - [Deleting Shadow Copies](#deleting-shadow-copies) @@ -778,10 +1130,28 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + - [Detect Activity Related to Pass the Hash Attacks](#detect-activity-related-to-pass-the-hash-attacks) +- [Detect Baron Samedit CVE-2021-3156](#detect-baron-samedit-cve-2021-3156) + + + +- [Detect Baron Samedit CVE-2021-3156 Segfault](#detect-baron-samedit-cve-2021-3156-segfault) + + + +- [Detect Baron Samedit CVE-2021-3156 via OSQuery](#detect-baron-samedit-cve-2021-3156-via-osquery) + + + - [Detect Computer Changed with Anonymous Account](#detect-computer-changed-with-anonymous-account) @@ -790,6 +1160,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Detect Dump LSASS Memory using comsvcs](#detect-dump-lsass-memory-using-comsvcs) @@ -804,6 +1176,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Detect HTML Help Renamed](#detect-html-help-renamed) @@ -828,10 +1202,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Detect MSHTA Url in Command Line](#detect-mshta-url-in-command-line) + + + + - [Detect New Local Admin account](#detect-new-local-admin-account) @@ -844,6 +1224,12 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [Detect Oulook exe writing a zip file](#detect-oulook-exe-writing-a--zip-file) + + + + + - [Detect Pass the Hash](#detect-pass-the-hash) @@ -925,6 +1311,14 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + @@ -946,6 +1340,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + - [Detect mshta inline hta execution](#detect-mshta-inline-hta-execution) @@ -954,10 +1352,22 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + - [Detect processes used for System Network Configuration Discovery](#detect-processes-used-for-system-network-configuration-discovery) + + + + +- [Detection of tools built by NirSoft](#detection-of-tools-built-by-nirsoft) + + + - [Disabling Remote User Account Control](#disabling-remote-user-account-control) @@ -973,6 +1383,24 @@ All the detections shipped to different Splunk products. Below is a breakdown by - [Dump LSASS via procdump Rename](#dump-lsass-via-procdump-rename) + + + + + + + + + + + + + + + + + + @@ -980,6 +1408,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [File with Samsam Extension](#file-with-samsam-extension) @@ -988,16 +1418,38 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [First Time Seen Running Windows Service](#first-time-seen-running-windows-service) + + + - [First time seen command line argument](#first-time-seen-command-line-argument) + + + + + + + + + + + + + + - [Hiding Files And Directories With Attrib exe](#hiding-files-and-directories-with-attrib-exe) + + + + - [Illegal Access To User Content via PowerSploit modules](#illegal-access-to-user-content-via-powersploit-modules) @@ -1042,6 +1494,50 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +- [MacOS - Re-opened Applications](#macos---re-opened-applications) + + + - [Malicious PowerShell Process - Connect To Internet With Hidden Window](#malicious-powershell-process---connect-to-internet-with-hidden-window) @@ -1054,14 +1550,22 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Malicious PowerShell Process With Obfuscation Techniques](#malicious-powershell-process-with-obfuscation-techniques) + + + + - [Monitor Registry Keys for Print Monitors](#monitor-registry-keys-for-print-monitors) + + - [More than usual number of LOLBAS applications in short time period](#more-than-usual-number-of-lolbas-applications-in-short-time-period) @@ -1074,6 +1578,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Ntdsutil export ntds](#ntdsutil-export-ntds) @@ -1101,6 +1607,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + @@ -1110,6 +1620,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Probing Access with Stolen Credentials via PowerSploit modules](#probing-access-with-stolen-credentials-via-powersploit-modules) @@ -1122,10 +1634,24 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [Processes Tapping Keyboard Events](#processes-tapping-keyboard-events) + + + + + - [Processes launching netsh](#processes-launching-netsh) + + + + + + + + - [Rare Parent-Child Process Relationship](#rare-parent-child-process-relationship) @@ -1194,6 +1720,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Registry Keys Used For Persistence](#registry-keys-used-for-persistence) @@ -1210,10 +1738,18 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [Remote Desktop Process Running On System](#remote-desktop-process-running-on-system) + + + - [Remote Process Instantiation via WMI](#remote-process-instantiation-via-wmi) + + + + - [RunDLL Loading DLL By Ordinal](#rundll-loading-dll-by-ordinal) @@ -1226,6 +1762,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Samsam Test File Write](#samsam-test-file-write) @@ -1238,6 +1776,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Schtasks scheduling job on remote system](#schtasks-scheduling-job-on-remote-system) @@ -1278,6 +1818,30 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + +- [Spike in File Writes](#spike-in-file-writes) + + + + + +- [Sunburst Correlation DLL and Network Event](#sunburst-correlation-dll-and-network-event) + + + + + + + + + + + + + + + - [Suspicious MSBuild Rename](#suspicious-msbuild-rename) @@ -1334,6 +1898,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Suspicious writes to windows Recycle Bin](#suspicious-writes-to-windows-recycle-bin) @@ -1356,10 +1922,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Unload Sysmon Filter Driver](#unload-sysmon-filter-driver) + + + + - [Unusually Long Command Line](#unusually-long-command-line) @@ -1378,10 +1950,18 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [WMI Permanent Event Subscription](#wmi-permanent-event-subscription) + + + - [WMI Permanent Event Subscription - Sysmon](#wmi-permanent-event-subscription---sysmon) +- [WMI Temporary Event Subscription](#wmi-temporary-event-subscription) + + + @@ -1394,6 +1974,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Windows Event Log Cleared](#windows-event-log-cleared) @@ -1401,6 +1983,22 @@ All the detections shipped to different Splunk products. Below is a breakdown by - [Windows Security Account Manager Stopped](#windows-security-account-manager-stopped) + + + + + + + + + + + + + + + +
## Network @@ -1494,6 +2092,34 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -1525,6 +2151,30 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [DNS record changed](#dns-record-changed) + + + + + + + +- [Detect ARP Poisoning](#detect-arp-poisoning) + + + + + + + + + + + + + + + @@ -1572,6 +2222,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + @@ -1639,10 +2299,18 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + - [Detect Traffic Mirroring](#detect-traffic-mirroring) + + - [Detect Unauthorized Assets by MAC address](#detect-unauthorized-assets-by-mac-address) @@ -1661,6 +2329,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + - [Detect hosts connecting to dynamic domain providers](#detect-hosts-connecting-to-dynamic-domain-providers) @@ -1707,6 +2377,43 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [Excessive DNS Failures](#excessive-dns-failures) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +- [Hosts receiving high volume of network traffic from email server](#hosts-receiving-high-volume-of-network-traffic-from-email-server) @@ -1772,6 +2479,105 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [Large Volume of DNS ANY Queries](#large-volume-of-dns-any-queries) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +- [Prohibited Network Traffic Allowed](#prohibited-network-traffic-allowed) + + + + + +- [Protocol or Port Mismatch](#protocol-or-port-mismatch) + + + +- [Protocols passing authentication in cleartext](#protocols-passing-authentication-in-cleartext) + + + + @@ -1827,6 +2633,12 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + - [SMB Traffic Spike](#smb-traffic-spike) @@ -1876,6 +2688,32 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -1911,6 +2749,12 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + - [Unusually Long Content-Type Length](#unusually-long-content-type-length) @@ -1932,6 +2776,28 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + + + + +
## Application @@ -2146,6 +3012,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [Detect New Login Attempts to Routers](#detect-new-login-attempts-to-routers) @@ -2180,10 +3047,106 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +- [Email Attachments With Lots Of Spaces](#email-attachments-with-lots-of-spaces) + + + - [Email files written outside of the Outlook directory](#email-files-written-outside-of-the-outlook-directory) +- [Email servers sending high volume traffic to hosts](#email-servers-sending-high-volume-traffic-to-hosts) @@ -2230,6 +3193,87 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +- [Monitor Email For Brand Abuse](#monitor-email-for-brand-abuse) + + + + + + + + + - [Multiple Okta Users With Invalid Credentials From The Same IP](#multiple-okta-users-with-invalid-credentials-from-the-same-ip) @@ -2238,6 +3282,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [No Windows Updates in a time frame](#no-windows-updates-in-a-time-frame) + + + @@ -2280,6 +3328,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [Phishing Email Detection by Machine Learning Method - SSA](#phishing-email-detection-by-machine-learning-method---ssa) @@ -2374,6 +3423,77 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + + + + +- [Spectre and Meltdown Vulnerable Systems](#spectre-and-meltdown-vulnerable-systems) + + + + + + + + + + + + + +- [Suspicious Email - UBA Anomaly](#suspicious-email---uba-anomaly) + + + +- [Suspicious Email Attachment Extensions](#suspicious-email-attachment-extensions) + + + + + +- [Suspicious Java Classes](#suspicious-java-classes) + + + + + + + + + + + + + + + + + + + + + + + + + @@ -2430,6 +3550,24 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + @@ -2619,6 +3757,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [Detect F5 TMUI RCE CVE-2020-5902](#detect-f5-tmui-rce-cve-2020-5902) @@ -2732,11 +3871,13 @@ All the detections shipped to different Splunk products. Below is a breakdown by +- [Detect attackers scanning for vulnerable JBoss servers](#detect-attackers-scanning-for-vulnerable-jboss-servers) +- [Detect malicious requests to exploit JBoss servers](#detect-malicious-requests-to-exploit-jboss-servers) @@ -2877,6 +4018,249 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + + + + +- [Monitor Web Traffic For Brand Abuse](#monitor-web-traffic-for-brand-abuse) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +- [SQL Injection with Long URLs](#sql-injection-with-long-urls) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +- [Supernova Webshell](#supernova-webshell) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -2925,6 +4309,24 @@ All the detections shipped to different Splunk products. Below is a breakdown by + + + + + + + + + + + + + + + + + + @@ -2933,6 +4335,267 @@ All the detections shipped to different Splunk products. Below is a breakdown by +### AWS Cloud Provisioning From Previously Unseen City +This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) +- **Last Updated**: 2018-03-16 + +
+ details + +#### Search +``` +`cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search City=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by City +| eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newCity=1 +| table City] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, City, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_city_filter` +``` +#### Associated Analytic Story + +* AWS Suspicious Provisioning Activities + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + +#### Kill Chain Phase + + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### AWS Cloud Provisioning From Previously Unseen Country +This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) +- **Last Updated**: 2018-03-16 + +
+ details + +#### Search +``` +`cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Country=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by Country +| eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newCountry=1 +| table Country] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, Country, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_country_filter` +``` +#### Associated Analytic Story + +* AWS Suspicious Provisioning Activities + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + +#### Kill Chain Phase + + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### AWS Cloud Provisioning From Previously Unseen IP Address +This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-03-16 + +
+ details + +#### Search +``` +`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Country=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress +| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newIP=1 +| table sourceIPAddress] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_ip_address_filter` +``` +#### Associated Analytic Story + +* AWS Suspicious Provisioning Activities + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### AWS Cloud Provisioning From Previously Unseen Region +This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) +- **Last Updated**: 2018-03-16 + +
+ details + +#### Search +``` +`cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Region=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by Region +| eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newRegion=1 +| table Region] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, Region, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_region_filter` +``` +#### Associated Analytic Story + +* AWS Suspicious Provisioning Activities + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + +#### Kill Chain Phase + + +#### Known False Positives +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### AWS Cross Account Activity From Previously Unseen Account This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. This search is deprecated and have been translated to use the latest Authentication Datamodel. @@ -3121,6 +4784,59 @@ bucket with S3 encryption * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json +_version_: 1 +
+ +--- + +### AWS EKS Kubernetes cluster sensitive object access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 +|table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason +|dedup user.username user.groups{} +|`aws_eks_kubernetes_cluster_sensitive_object_access_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Object Access Activity + + +#### How To Implement +You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. This search works with cloudwatch logs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. + +#### Reference + + +#### Test Dataset + + _version_: 1
@@ -3371,6 +5087,242 @@ _version_: 1 --- +### Abnormally High AWS Instances Launched by User +This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` +`cloudtrail` eventName=RunInstances errorCode=success +| bucket span=10m _time +| stats count AS instances_launched by _time userName +| eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev +| eval threshold_value = 4 +| eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) +| search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") +| eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) +| table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev +| `abnormally_high_aws_instances_launched_by_user_filter` +``` +#### Associated Analytic Story + +* AWS Cryptomining + +* Suspicious AWS EC2 Activities + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Abnormally High AWS Instances Launched by User - MLTK +This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` +`cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` +| bucket span=10m _time +| stats count as instances_launched by _time src_user +| apply ec2_excessive_runinstances_v1 +| rename "IsOutlier(instances_launched)" as isOutlier +| where isOutlier=1 +``` +#### Associated Analytic Story + +* AWS Cryptomining + +* Suspicious AWS EC2 Activities + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Abnormally High AWS Instances Terminated by User +This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` +`cloudtrail` eventName=TerminateInstances errorCode=success +| bucket span=10m _time +| stats count AS instances_terminated by _time userName +| eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev +| eval threshold_value = 4 +| eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) +| search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") +| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) +|table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev +| `abnormally_high_aws_instances_terminated_by_user_filter` +``` +#### Associated Analytic Story + +* Suspicious AWS EC2 Activities + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Abnormally High AWS Instances Terminated by User - MLTK +This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` +`cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` +| bucket span=10m _time +| stats count as instances_terminated by _time src_user +| apply ec2_excessive_terminateinstances_v1 +| rename "IsOutlier(instances_terminated)" as isOutlier +| where isOutlier=1 +``` +#### Associated Analytic Story + +* Suspicious AWS EC2 Activities + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Abnormally High Number Of Cloud Infrastructure API Calls This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user. @@ -3703,6 +5655,118 @@ _version_: 2 --- +### Amazon EKS Kubernetes Pod scan detection +This search provides detection information on unauthenticated requests against Kubernetes' Pods API + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) +- **Last Updated**: 2020-04-15 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" +| rename source as cluster_name sourceIPs{} as src_ip +| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `amazon_eks_kubernetes_pod_scan_detection_filter` +``` +#### Associated Analytic Story + +* Kubernetes Scanning Activity + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1526 | Cloud Service Discovery | Discovery | + +#### Kill Chain Phase + +* Reconnaissance + + +#### Known False Positives +Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Amazon EKS Kubernetes cluster scan detection +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) +- **Last Updated**: 2020-04-15 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" +| rename sourceIPs{} as src_ip +| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +|`amazon_eks_kubernetes_cluster_scan_detection_filter` +``` +#### Associated Analytic Story + +* Kubernetes Scanning Activity + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1526 | Cloud Service Discovery | Discovery | + +#### Kill Chain Phase + +* Reconnaissance + + +#### Known False Positives +Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Applying Stolen Credentials via Mimikatz modules This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack. @@ -4421,6 +6485,125 @@ _version_: 1 --- +### Child Processes of Spoolsv exe +This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) +- **Last Updated**: 2020-03-16 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `child_processes_of_spoolsv_exe_filter` +``` +#### Associated Analytic Story + +* Windows Privilege Escalation + + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | + +#### Kill Chain Phase + +* Exploitation + + +#### Known False Positives +Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + +### Clients Connecting to Multiple DNS Servers +This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src +| `drop_dm_object_name("Network_Resolution")` +|where dest_count > 5 +| `clients_connecting_to_multiple_dns_servers_filter` +``` +#### Associated Analytic Story + +* DNS Hijacking + +* Command and Control + +* Suspicious DNS Traffic + +* Host Redirection + + +#### How To Implement +This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ +This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | + +#### Kill Chain Phase + +* Command and Control + + +#### Known False Positives +It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + ### Cloud API Calls From Previously Unseen User Roles This search looks for new commands from each user role. @@ -4787,6 +6970,61 @@ It's possible that a new user will start to modify EC2 instances when they haven * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json +_version_: 1 +
+ +--- + +### Cloud Network Access Control List Deleted +Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-09-08 + +
+ details + +#### Search +``` +`cloudtrail` eventName=DeleteNetworkAcl +|rename userIdentity.arn as arn +| stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `cloud_network_access_control_list_deleted_filter` +``` +#### Associated Analytic Story + +* Cloud Network ACL Activity + + +#### How To Implement +You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the `cloud_network_access_control_list_deleted_filter` macro. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +It's possible that a user has legitimately deleted a network ACL. + +#### Reference + + +#### Test Dataset + + _version_: 1
@@ -6477,6 +8715,139 @@ It's possible there can be long domain names that are legitimate. * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log +_version_: 3 +
+ +--- + +### DNS Query Requests Resolved by Unauthorized DNS Servers +This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest +| `drop_dm_object_name("DNS")` +| `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` +``` +#### Associated Analytic Story + +* DNS Hijacking + +* Command and Control + +* Suspicious DNS Traffic + +* Host Redirection + + +#### How To Implement +To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1071.004 | DNS | Command and Control | + +#### Kill Chain Phase + +* Command and Control + + +#### Known False Positives +Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + +### DNS record changed +The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| inputlookup discovered_dns_records +| rename answer as discovered_answer +| join domain[ +|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query +| rename DNS.query as query +| where query!="unknown" +| rex field=query "(?\w+\.\w+?)(?:$ +|/)"] +| makemv delim=" " answer +| makemv delim=" " type +| sort -count +| table count,src,domain,type,query,current_answer,discovered_answer +| makemv current_answer +| mvexpand current_answer +| makemv discovered_answer +| eval n=mvfind(discovered_answer, current_answer) +| where isnull(n) +| `dns_record_changed_filter` +``` +#### Associated Analytic Story + +* DNS Hijacking + + +#### How To Implement +To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ + + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1071.004 | DNS | Command and Control | + +#### Kill Chain Phase + +* Command and Control + + +#### Known False Positives +Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. + +#### Reference + + +#### Test Dataset + + _version_: 3
@@ -6544,6 +8915,199 @@ _version_: 4 --- +### Detect API activity from users without MFA +This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-05-17 + +
+ details + +#### Search +``` +`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false +| search NOT [ +| inputlookup aws_service_accounts +| fields identity +| rename identity as user] +| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_api_activity_from_users_without_mfa_filter` +``` +#### Associated Analytic Story + +* AWS User Monitoring + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ +This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** AWS User ARN, **Field:** userIdentity.arn\ +1. \ +1. **Label:** AWS User Type, **Field:** userIdentity.type\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + + +#### Known False Positives +Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Detect ARP Poisoning +By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557.002](https://attack.mitre.org/techniques/T1557.002/) +- **Last Updated**: 2020-08-11 + +
+ details + +#### Search +``` +`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" +| eval src_interface=src_int_prefix_long+src_int_suffix +| stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_arp_poisoning_filter` +``` +#### Associated Analytic Story + +* Router and Infrastructure Security + + +#### How To Implement +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1200 | Hardware Additions | Initial Access | +| T1498 | Network Denial of Service | Impact | +| T1557.002 | ARP Cache Poisoning | Collection, Credential Access | + +#### Kill Chain Phase + +* Reconnaissance + +* Delivery + +* Actions on Objectives + + +#### Known False Positives +This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely). + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Detect AWS API Activities From Unapproved Accounts +This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` +`cloudtrail` errorCode=success +| rename userName as identity +| search NOT [ +| inputlookup identity_lookup_expanded +| fields identity] +| search NOT [ +| inputlookup aws_service_accounts +| fields identity] +| rename identity as user +| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_aws_api_activities_from_unapproved_accounts_filter` +``` +#### Associated Analytic Story + +* AWS User Monitoring + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ +This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** First Time, **Field:** firstTime\ +1. \ +1. **Label:** Last Time, **Field:** lastTime\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Detect AWS Console Login by New User This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour @@ -6873,6 +9437,173 @@ _version_: 5 --- +### Detect Baron Samedit CVE-2021-3156 +This search detects the heap-based buffer overflow of sudoedit + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) +- **Last Updated**: 2021-01-27 + +
+ details + +#### Search +``` +`linux_hosts` +| search "sudoedit -s \\" +| `detect_baron_samedit_cve_2021_3156_filter` +``` +#### Associated Analytic Story + +* Baron Samedit CVE-2021-3156 + + +#### How To Implement +Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | + +#### Kill Chain Phase + +* Exploitation + + +#### Known False Positives +unknown + +#### Reference + +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Detect Baron Samedit CVE-2021-3156 Segfault +This search detects the heap-based buffer overflow of sudoedit + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) +- **Last Updated**: 2021-01-29 + +
+ details + +#### Search +``` +`linux_hosts` +| search sudoedit segfault +| stats count min(_time) as firstTime max(_time) as lastTime by host +| search count > 5 +| `detect_baron_samedit_cve_2021_3156_segfault_filter` +``` +#### Associated Analytic Story + +* Baron Samedit CVE-2021-3156 + + +#### How To Implement +Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged. This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | + +#### Kill Chain Phase + +* Exploitation + + +#### Known False Positives +If sudoedit is throwing segfaults for other reasons this will pick those up too. + +#### Reference + +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Detect Baron Samedit CVE-2021-3156 via OSQuery +This search detects the heap-based buffer overflow of sudoedit + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) +- **Last Updated**: 2021-01-28 + +
+ details + +#### Search +``` +`osquery_process` +| search "columns.cmdline"="sudoedit -s \\*" +| `detect_baron_samedit_cve_2021_3156_via_osquery_filter` +``` +#### Associated Analytic Story + +* Baron Samedit CVE-2021-3156 + + +#### How To Implement +OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | + +#### Kill Chain Phase + +* Exploitation + + +#### Known False Positives +unknown + +#### Reference + +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Detect Computer Changed with Anonymous Account This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. @@ -6988,6 +9719,79 @@ _version_: 3 --- +### Detect DNS requests to Phishing Sites leveraging EvilGinx2 +This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1566.003](https://attack.mitre.org/techniques/T1566.003/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host +| `drop_dm_object_name(DNS)` +| rex field=query ".*?(?[^./:]+\.(\S{2,3} +|\S{2,3}.\S{2,3}))$" +| stats count values(query) as query by domain dest src answer +| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` +| search NOT [ inputlookup legit_domains.csv +| fields domain] +| join domain type=outer [ +| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site +| rename "Web.*" as * +| rex field=site ".*?(?[^./:]+\.(\S{2,3} +|\S{2,3}.\S{2,3}))$" +| table dest domain url] +| table count src dest query answer domain url +| `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter` +``` +#### Associated Analytic Story + +* Common Phishing Frameworks + + +#### How To Implement +You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\ + + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1566.003 | Spearphishing via Service | Initial Access | + +#### Kill Chain Phase + +* Delivery + +* Command and Control + + +#### Known False Positives +If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Detect Dump LSASS Memory using comsvcs This search detects the memory of lsass.exe being dumped for offline credential theft attack. @@ -7180,6 +9984,66 @@ _version_: 3 --- +### Detect F5 TMUI RCE CVE-2020-5902 +This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/) +- **Last Updated**: 2020-08-02 + +
+ details + +#### Search +``` +`f5_bigip_rogue` +| regex _raw="(hsqldb; +|.*\\.\\.;.*)" +| search `detect_f5_tmui_rce_cve_2020_5902_filter` +``` +#### Associated Analytic Story + +* F5 TMUI RCE CVE-2020-5902 + + +#### How To Implement +To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog. As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it. I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;). + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1190 | Exploit Public-Facing Application | Initial Access | + +#### Kill Chain Phase + +* Exploitation + + +#### Known False Positives +unknown + +#### Reference + +* https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/ + +* https://support.f5.com/csp/article/K52145254 + +* https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/ + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Detect GCP Storage access from a new IP This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. @@ -7724,6 +10588,68 @@ ICMP packets are used in a variety of ways to help troubleshoot networking issue #### Test Dataset +_version_: 2 +
+ +--- + +### Detect Long DNS TXT Record Response +This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type +| `drop_dm_object_name("DNS")` +| eval anslen=len(answer) +| search anslen>100 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count +| table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" +| `detect_long_dns_txt_record_response_filter` +``` +#### Associated Analytic Story + +* Suspicious DNS Traffic + +* Command and Control + + +#### How To Implement +To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | + +#### Kill Chain Phase + +* Command and Control + + +#### Known False Positives +It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. + +#### Reference + + +#### Test Dataset + + _version_: 2
@@ -7793,6 +10719,128 @@ _version_: 1 --- +### Detect Mimikatz Using Loaded Images +This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) +- **Last Updated**: 2019-12-03 + +
+ details + +#### Search +``` +`sysmon` EventCode=7 +| stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image +| search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mimikatz_using_loaded_images_filter` +``` +#### Associated Analytic Story + +* Credential Dumping + +* Detect Zerologon Attack + +* Cloud Federated Credential Abuse + + +#### How To Implement +This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.001 | LSASS Memory | Credential Access | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Other tools can import the same DLLs. These tools should be part of a whitelist. + +#### Reference + +* https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Detect Mimikatz Via PowerShell And EventCode 4703 +This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) +- **Last Updated**: 2019-02-27 + +
+ details + +#### Search +``` +`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe +| rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" +| where privs="SeDebugPrivilege" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message +| rename privs as "Enabled Privilege" +| rename Process_Name as process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mimikatz_via_powershell_and_eventcode_4703_filter` +``` +#### Associated Analytic Story + +* Cloud Federated Credential Abuse + + +#### How To Implement +You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.001 | LSASS Memory | Credential Access | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Detect New Local Admin account This search looks for newly created accounts that have been elevated to local administrators. @@ -7858,6 +10906,63 @@ _version_: 2 --- +### Detect New Login Attempts to Routers +The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Authentication +- **ATT&CK**: +- **Last Updated**: 2017-09-12 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user +| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) +| where isOutlier=1 +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| `drop_dm_object_name("Authentication")` +| `detect_new_login_attempts_to_routers_filter` +``` +#### Associated Analytic Story + +* Router and Infrastructure Security + + +#### How To Implement +To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Legitimate router connections may appear as new connections + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Detect New Open GCP Storage Buckets This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. @@ -8044,6 +11149,75 @@ _version_: 2 --- +### Detect Oulook exe writing a zip file +This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename process_id as malicious_id +| rename parent_process_id as outlook_id +| join malicious_id type=inner[ +| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename process_id as malicious_id +| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] +| table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path +| where file_name != "" +| `detect_oulook_exe_writing_a__zip_file_filter` +``` +#### Associated Analytic Story + +* Phishing Payloads + + +#### How To Implement +You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1566.001 | Spearphishing Attachment | Initial Access | + +#### Kill Chain Phase + +* Installation + +* Actions on Objectives + + +#### Known False Positives +It is not uncommon for outlook to write legitimate zip files to the disk. + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + ### Detect Outbound SMB Traffic This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. @@ -9549,6 +12723,82 @@ _version_: 1 --- +### Detect Spike in AWS API Activity +This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` +`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup api_call_by_user_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup api_call_by_user_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_aws_api_activity_filter` +``` +#### Associated Analytic Story + +* AWS User Monitoring + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ +This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\ +1. \ +1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives + + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Detect Spike in AWS Security Hub Alerts for EC2 Instance This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals @@ -9664,6 +12914,76 @@ _version_: 3 --- +### Detect Spike in Network ACL Activity +This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562.007/) +- **Last Updated**: 2018-05-21 + +
+ details + +#### Search +``` +`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup network_acl_activity_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup network_acl_activity_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_network_acl_activity_filter` +``` +#### Associated Analytic Story + +* AWS Network ACL Activity + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Detect Spike in S3 Bucket deletion This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. @@ -9730,6 +13050,76 @@ Based on the values of`dataPointThreshold` and `deviationThreshold`, the false p #### Test Dataset +_version_: 1 +
+ +--- + +### Detect Spike in Security Group Activity +This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) +- **Last Updated**: 2018-04-18 + +
+ details + +#### Search +``` +`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup security_group_activity_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup security_group_activity_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_security_group_activity_filter` +``` +#### Associated Analytic Story + +* AWS User Monitoring + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. + +#### Reference + + +#### Test Dataset + + _version_: 1
@@ -9861,6 +13251,63 @@ This search will return false positives for any legitimate traffic captures by n #### Test Dataset +_version_: 1 +
+ +--- + +### Detect USB device insertion +The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Change_Analysis +- **ATT&CK**: +- **Last Updated**: 2017-11-27 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest +| `drop_dm_object_name("All_Changes")` +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| `detect_usb_device_insertion_filter` +``` +#### Associated Analytic Story + +* Data Protection + + +#### How To Implement +To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Installation + +* Actions on Objectives + + +#### Known False Positives +Legitimate USB activity will also be detected. Please verify and investigate as appropriate. + +#### Reference + + +#### Test Dataset + + _version_: 1
@@ -10169,6 +13616,64 @@ unknown #### Test Dataset +_version_: 1 +
+ +--- + +### Detect attackers scanning for vulnerable JBoss servers +This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Web +- **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/) +- **Last Updated**: 2017-09-23 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` +``` +#### Associated Analytic Story + +* JBoss Vulnerability + +* SamSam Ransomware + + +#### How To Implement +You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1082 | System Information Discovery | Discovery | + +#### Kill Chain Phase + +* Reconnaissance + + +#### Known False Positives +It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. + +#### Reference + + +#### Test Dataset + + _version_: 1
@@ -10250,6 +13755,65 @@ _version_: 3 --- +### Detect malicious requests to exploit JBoss servers +This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Web +- **ATT&CK**: +- **Last Updated**: 2017-09-23 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest +| search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table src, dest_ip, http_method, url, firstTime, lastTime +| `detect_malicious_requests_to_exploit_jboss_servers_filter` +``` +#### Associated Analytic Story + +* JBoss Vulnerability + +* SamSam Ransomware + + +#### How To Implement +You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Delivery + + +#### Known False Positives +No known false positives for this detection. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Detect mshta inline hta execution The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. @@ -10376,6 +13940,129 @@ _version_: 1 --- +### Detect new API calls from user roles +This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) +- **Last Updated**: 2018-04-16 + +
+ details + +#### Search +``` +`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole +| stats earliest(_time) as earliest latest(_time) as latest by userName eventName +| inputlookup append=t previously_seen_api_calls_from_user_roles +| stats min(earliest) as earliest, max(latest) as latest by userName eventName +| outputlookup previously_seen_api_calls_from_user_roles +| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) +| where newApiCallfromUserRole=1 +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| table eventName userName] +|rename userName as user +| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| `detect_new_api_calls_from_user_roles_filter` +``` +#### Associated Analytic Story + +* AWS User Monitoring + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + + +#### Known False Positives +It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Detect new user AWS Console Login +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` +`cloudtrail` eventName=ConsoleLogin +| rename userIdentity.arn as user +| stats earliest(_time) as firstTime latest(_time) as lastTime by user +| inputlookup append=t previously_seen_users_console_logins_cloudtrail +| stats min(firstTime) as firstTime max(lastTime) as lastTime by user +| eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| where userStatus ="First Time Logging into AWS Console" +| `detect_new_user_aws_console_login_filter` +``` +#### Associated Analytic Story + +* Suspicious AWS Login Activities + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Detect processes used for System Network Configuration Discovery This search looks for fast execution of processes used for system network configuration discovery on the endpoint. @@ -10442,6 +14129,193 @@ _version_: 2 --- +### Detect web traffic to dynamic domain providers +This search looks for web connections to dynamic DNS providers. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Web +- **ATT&CK**: [T1071.001](https://attack.mitre.org/techniques/T1071.001/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `dynamic_dns_web_traffic` +| `detect_web_traffic_to_dynamic_domain_providers_filter` +``` +#### Associated Analytic Story + +* Dynamic DNS + + +#### How To Implement +This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ +This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` Deprecated because duplicate. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1071.001 | Web Protocols | Command and Control | + +#### Kill Chain Phase + +* Command and Control + +* Actions on Objectives + + +#### Known False Positives +It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Detection of DNS Tunnels +This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) +- **Last Updated**: 2017-09-18 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" +| rename "DNS.src" as src "DNS.query" as message +| eval length=len(message) +| stats sum(length) as length by src +| append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" +| rename "DNS.src" as src "DNS.answer" as message +| eval message=if(message=="unknown","", message) +| eval length=len(message) +| stats sum(length) as length by src ] +| stats sum(length) as length by src +| where length > 10000 +| `detection_of_dns_tunnels_filter` +``` +#### Associated Analytic Story + +* Data Protection + +* Suspicious DNS Traffic + +* Command and Control + + +#### How To Implement +To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | + +#### Kill Chain Phase + +* Command and Control + +* Actions on Objectives + + +#### Known False Positives +It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Detection of tools built by NirSoft +This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1072](https://attack.mitre.org/techniques/T1072/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detection_of_tools_built_by_nirsoft_filter` +``` +#### Associated Analytic Story + +* Emotet Malware DHS Report TA18-201A + + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1072 | Software Deployment Tools | Execution, Lateral Movement | + +#### Kill Chain Phase + +* Installation + +* Actions on Objectives + + +#### Known False Positives +While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + ### Disabling Remote User Account Control The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). @@ -10694,6 +14568,385 @@ _version_: 1 --- +### EC2 Instance Modified With Previously Unseen User +This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` +`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success +| stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn +| rename userIdentity.arn as arn +| inputlookup append=t previously_seen_ec2_modifications_by_user +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn +| outputlookup previously_seen_ec2_modifications_by_user +| eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newUser=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=dest responseElements.instancesSet.items{}.instanceId +| spath output=user userIdentity.arn +| table _time, user, dest +| `ec2_instance_modified_with_previously_unseen_user_filter` +``` +#### Associated Analytic Story + +* Unusual AWS EC2 Modifications + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + + +#### Known False Positives +It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + +### EC2 Instance Started In Previously Unseen Region +This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) +- **Last Updated**: 2018-02-23 + +
+ details + +#### Search +``` +`cloudtrail` earliest=-1h StartInstances +| stats earliest(_time) as earliest latest(_time) as latest by awsRegion +| inputlookup append=t previously_seen_aws_regions.csv +| stats min(earliest) as earliest max(latest) as latest by awsRegion +| outputlookup previously_seen_aws_regions.csv +| eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| where regionStatus="Instance Started in a New Region" +| `ec2_instance_started_in_previously_unseen_region_filter` +``` +#### Associated Analytic Story + +* AWS Cryptomining + +* Suspicious AWS EC2 Activities + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### EC2 Instance Started With Previously Unseen AMI +This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-03-12 + +
+ details + +#### Search +``` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success +| stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId +| rename requestParameters.instancesSet.items{}.imageId as amiID +| inputlookup append=t previously_seen_ec2_amis.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID +| outputlookup previously_seen_ec2_amis.csv +| eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| where newAMI=1 +| rename amiID as requestParameters.instancesSet.items{}.imageId +| table requestParameters.instancesSet.items{}.imageId] +| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID +| table firstTime, lastTime, arn, amiID, dest, instanceType +| `ec2_instance_started_with_previously_unseen_ami_filter` +``` +#### Associated Analytic Story + +* AWS Cryptomining + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + + +#### Known False Positives +After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### EC2 Instance Started With Previously Unseen Instance Type +This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-02-07 + +
+ details + +#### Search +``` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success +| fillnull value="m1.small" requestParameters.instanceType +| stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType +| rename requestParameters.instanceType as instanceType +| inputlookup append=t previously_seen_ec2_instance_types.csv +| stats min(earliest) as earliest max(latest) as latest by instanceType +| outputlookup previously_seen_ec2_instance_types.csv +| eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| where newType=1 +| rename instanceType as requestParameters.instanceType +| table requestParameters.instanceType] +| spath output=user userIdentity.arn +| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest +| table _time, user, dest, instanceType +| `ec2_instance_started_with_previously_unseen_instance_type_filter` +``` +#### Associated Analytic Story + +* AWS Cryptomining + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + + +#### Known False Positives +It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### EC2 Instance Started With Previously Unseen User +This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success +| stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn +| rename userIdentity.arn as arn +| inputlookup append=t previously_seen_ec2_launches_by_user.csv +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn +| outputlookup previously_seen_ec2_launches_by_user.csv +| eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newUser=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename arn as userIdentity.arn +| table userIdentity.arn] +| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user +| table _time, user, dest, instanceType +| `ec2_instance_started_with_previously_unseen_user_filter` +``` +#### Associated Analytic Story + +* AWS Cryptomining + +* Suspicious AWS EC2 Activities + + +#### How To Implement +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + + +#### Known False Positives +It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Email Attachments With Lots Of Spaces +Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Email +- **ATT&CK**: +- **Last Updated**: 2017-09-19 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Email")` +| eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) +| search space_ratio >= 0.1 +| rex field=recipient_address "(?.*)@" +| `email_attachments_with_lots_of_spaces_filter` +``` +#### Associated Analytic Story + +* Emotet Malware DHS Report TA18-201A + +* Suspicious Emails + + +#### How To Implement +You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ + **Splunk Phantom Playbook Integration**\ +If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Delivery + + +#### Known False Positives +None at this time + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Email files written outside of the Outlook directory The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. @@ -10745,6 +14998,185 @@ Administrators and users sometimes prefer backing up their email data by moving #### Test Dataset +_version_: 3 +
+ +--- + +### Email servers sending high volume traffic to hosts +This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Traffic +- **ATT&CK**: [T1114.002](https://attack.mitre.org/techniques/T1114.002/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d +| `drop_dm_object_name("All_Traffic")` +| eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out +| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip +| eval minimum_data_samples = 4, deviation_threshold = 3 +| where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") +| eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) +| table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average +| `email_servers_sending_high_volume_traffic_to_hosts_filter` +``` +#### Associated Analytic Story + +* Collection and Staging + + +#### How To Implement +This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1114.002 | Remote Email Collection | Collection | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Excessive DNS Failures +This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071.004/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query" +| `drop_dm_object_name("DNS")` +| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain +| where isnull(domain) +| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank +| where isnull(rank) +| stats sum(count) as count mode(queries) as queries by src +| `get_asset(src)` +| where count>50 +| `excessive_dns_failures_filter` +``` +#### Associated Analytic Story + +* Suspicious DNS Traffic + +* Command and Control + + +#### How To Implement +To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1071.004 | DNS | Command and Control | + +#### Kill Chain Phase + +* Command and Control + + +#### Known False Positives +It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Execution of File With Spaces Before Extension +This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036.003/) +- **Last Updated**: 2020-11-19 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `execution_of_file_with_spaces_before_extension_filter` +``` +#### Associated Analytic Story + +* Windows File Extension and Association Abuse + + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1036.003 | Rename System Utilities | Defense Evasion | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +None identified. + +#### Reference + + +#### Test Dataset + + _version_: 3
@@ -10808,6 +15240,61 @@ _version_: 3 --- +### Extended Period Without Successful Netbackup Backups +This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-12 + +
+ details + +#### Search +``` +`netbackup` MESSAGE="Disk/Partition backup completed successfully." +| stats latest(_time) as latestTime by COMPUTERNAME +| `security_content_ctime(latestTime)` +| rename COMPUTERNAME as dest +| eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) +| search isOutlier=1 +| table latestTime, dest +| `extended_period_without_successful_netbackup_backups_filter` +``` +#### Associated Analytic Story + +* Monitor Backup Solution + + +#### How To Implement +To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + + +#### Known False Positives +None identified + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### File with Samsam Extension The search looks for file writes with extensions consistent with a SamSam ransomware attack. @@ -10927,6 +15414,69 @@ _version_: 1 --- +### First Time Seen Running Windows Service +This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1569.002](https://attack.mitre.org/techniques/T1569.002/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` +`wineventlog_system` EventCode=7036 +| rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" +| where state="running" +| lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen +| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) +| table _time dest service +| `first_time_seen_running_windows_service_filter` +``` +#### Associated Analytic Story + +* Windows Service Abuse + +* Orangeworm Attack Group + +* Sunburst Malware + + +#### How To Implement +While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1569.002 | Service Execution | Execution | + +#### Kill Chain Phase + +* Installation + +* Actions on Objectives + + +#### Known False Positives +A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. + +#### Reference + + +#### Test Dataset + + +_version_: 4 +
+ +--- + ### First time seen command line argument This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel. @@ -11001,6 +15551,423 @@ _version_: 2 --- +### First time seen command line argument +This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/), [T1059.003](https://attack.mitre.org/techniques/T1059.003/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search [ +| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process +| `drop_dm_object_name(Processes)` +| inputlookup append=t previously_seen_cmd_line_arguments +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by process +| outputlookup previously_seen_cmd_line_arguments +| eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newCmdLineArgument=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table process] +| `first_time_seen_command_line_argument_filter` +``` +#### Associated Analytic Story + +* DHS Report TA18-074A + +* Suspicious Command-Line Executions + +* Orangeworm Attack Group + +* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + +* Hidden Cobra Malware + + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059.001 | PowerShell | Execution | +| T1059.003 | Windows Command Shell | Execution | + +#### Kill Chain Phase + +* Command and Control + +* Actions on Objectives + + +#### Known False Positives +Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name + +#### Reference + + +#### Test Dataset + + +_version_: 5 +
+ +--- + +### GCP Detect accounts with high risk roles by project +This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) +- **Last Updated**: 2020-10-09 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}.role=roles/owner OR roles/editor OR roles/iam.serviceAccountUser OR roles/iam.serviceAccountAdmin OR roles/iam.serviceAccountTokenCreator OR roles/dataflow.developer OR roles/dataflow.admin OR roles/composer.admin OR roles/dataproc.admin OR roles/dataproc.editor +| table data.resource.type data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.authorizationInfo{}.resource data.protoPayload.response.bindings{}.role data.protoPayload.response.bindings{}.members{} +| `gcp_detect_accounts_with_high_risk_roles_by_project_filter` +``` +#### Associated Analytic Story + +* GCP Cross Account Activity + + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Accounts with high risk roles should be reduced to the minimum number needed, however specific tasks and setups may be simply expected behavior within organization + +#### Reference + +* https://github.com/dxa4481/gcploit + +* https://www.youtube.com/watch?v=Ml09R38jpok + +* https://cloud.google.com/iam/docs/understanding-roles + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### GCP Detect gcploit framework +This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) +- **Last Updated**: 2020-10-08 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s +| table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent +| `gcp_detect_gcploit_framework_filter` +``` +#### Associated Analytic Story + +* GCP Cross Account Activity + + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects + +#### Reference + +* https://github.com/dxa4481/gcploit + +* https://www.youtube.com/watch?v=Ml09R38jpok + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### GCP Detect high risk permissions by resource and account +This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) +- **Last Updated**: 2020-10-09 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.permission=iam.serviceAccounts.getaccesstoken OR iam.serviceAccounts.setIamPolicy OR iam.serviceAccounts.actas OR dataflow.jobs.create OR composer.environments.create OR dataproc.clusters.create +|table data.protoPayload.requestMetadata.callerIp data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.response.bindings{}.members{} data.resource.labels.project_id +| `gcp_detect_high_risk_permissions_by_resource_and_account_filter` +``` +#### Associated Analytic Story + +* GCP Cross Account Activity + + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +High risk permissions are part of any GCP environment, however it is important to track resource and accounts usage, this search may produce false positives. + +#### Reference + +* https://github.com/dxa4481/gcploit + +* https://www.youtube.com/watch?v=Ml09R38jpok + +* https://cloud.google.com/iam/docs/permissions-reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### GCP GCR container uploaded +This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1525](https://attack.mitre.org/techniques/T1525/) +- **Last Updated**: 2020-02-20 + +
+ details + +#### Search +``` + +|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path +| `drop_dm_object_name("Storage")` +| `gcp_gcr_container_uploaded_filter` +``` +#### Associated Analytic Story + +* Container Implantation Monitoring and Investigation + + +#### How To Implement +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1525 | Implant Container Image | Persistence | + +#### Kill Chain Phase + + +#### Known False Positives +Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### GCP Kubernetes cluster pod scan detection +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) +- **Last Updated**: 2020-07-17 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` category=kube-audit +|spath input=properties.log +|search responseStatus.code=401 +|table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod +| `gcp_kubernetes_cluster_pod_scan_detection_filter` +``` +#### Associated Analytic Story + +* Kubernetes Scanning Activity + + +#### How To Implement +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1526 | Cloud Service Discovery | Discovery | + +#### Kill Chain Phase + +* Reconnaissance + + +#### Known False Positives +Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### GCP Kubernetes cluster scan detection +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) +- **Last Updated**: 2020-04-15 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" +| rename data.protoPayload.requestMetadata.callerIp as src_ip +| stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name +| rename data.resource.labels.cluster_name as cluster_name +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `gcp_kubernetes_cluster_scan_detection_filter` +``` +#### Associated Analytic Story + +* Kubernetes Scanning Activity + + +#### How To Implement +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1526 | Cloud Service Discovery | Discovery | + +#### Kill Chain Phase + +* Reconnaissance + + +#### Known False Positives +Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Hiding Files And Directories With Attrib exe Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. @@ -11110,6 +16077,122 @@ unknown #### Test Dataset +_version_: 1 +
+ +--- + +### Hosts receiving high volume of network traffic from email server +This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Traffic +- **ATT&CK**: [T1114.002](https://attack.mitre.org/techniques/T1114.002/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d +| `drop_dm_object_name("All_Traffic")` +| eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in +| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip +| eval minimum_data_samples = 4, deviation_threshold = 3 +| where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") +| eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) +| table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average +| `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` +``` +#### Associated Analytic Story + +* Collection and Staging + + +#### How To Implement +This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1114.002 | Remote Email Collection | Collection | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Identify New User Accounts +This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078.002](https://attack.mitre.org/techniques/T1078.002/) +- **Last Updated**: 2017-09-12 + +
+ details + +#### Search +``` + +| from datamodel Identity_Management.All_Identities +| eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") +| search empStatus="Accounts created in last week" +| `security_content_ctime(endDate)` +| `security_content_ctime(startDate)` +| table identity empStatus endDate startDate +| `identify_new_user_accounts_filter` +``` +#### Associated Analytic Story + +* Account Monitoring and Controls + + +#### How To Implement +To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078.002 | Domain Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + + +#### Known False Positives +If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. + +#### Reference + + +#### Test Dataset + + _version_: 1
@@ -11853,6 +16936,1139 @@ _version_: 3 --- +### Kubernetes AWS detect RBAC authorization by account +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* +| table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason +| stats count by user.username annotations.authorization.k8s.io/reason +| rare user.username annotations.authorization.k8s.io/reason +|`kubernetes_aws_detect_rbac_authorization_by_account_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Role Activity + + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes AWS detect most active service accounts by pod +This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts objectRef.resource=pods +| table sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision +| top sourceIPs{} user.username verb annotations.authorization.k8s.io/decision +|`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Role Activity + + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes AWS detect sensitive role access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 +| table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason +| dedup user.username user.groups{} +|`kubernetes_aws_detect_sensitive_role_access_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Role Activity + + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes AWS detect service accounts forbidden failure access +This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure +| table sourceIPs{} user.username userAgent verb responseStatus.status requestURI +| `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Object Access Activity + + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes AWS detect suspicious kubectl calls +This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous +| table src_ip src_user verb userAgent requestURI +| stats count by src_ip src_user verb userAgent requestURI +|`kubernetes_aws_detect_suspicious_kubectl_calls_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Object Access Activity + + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes Azure detect RBAC authorization by account +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-05-26 + +
+ details + +#### Search +``` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search annotations.authorization.k8s.io/reason=* +| table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason +|stats count by user.username annotations.authorization.k8s.io/reason +| rare user.username annotations.authorization.k8s.io/reason +|`kubernetes_azure_detect_rbac_authorization_by_account_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Role Activity + + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes Azure detect most active service accounts by pod namespace +This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-05-26 + +
+ details + +#### Search +``` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow +| table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace +| top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace +|`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Role Activity + + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes Azure detect sensitive object access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ details + +#### Search +``` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow +|table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason +|dedup user.username user.groups{} +|`kubernetes_azure_detect_sensitive_object_access_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Object Access Activity + + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes Azure detect sensitive role access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ details + +#### Search +``` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search objectRef.resource=clusterroles OR clusterrolebindings +| table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason +| dedup user.username user.groups{} +|`kubernetes_azure_detect_sensitive_role_access_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Role Activity + + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes Azure detect service accounts forbidden failure access +This search provides information on Kubernetes service accounts with failure or forbidden access status + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ details + +#### Search +``` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden +| table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace +|`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Object Access Activity + + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes Azure detect suspicious kubectl calls +This search provides information on rare Kubectl calls with IP, verb namespace and object access context + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-05-26 + +
+ details + +#### Search +``` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration +| search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 +| table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI +| rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI +|`kubernetes_azure_detect_suspicious_kubectl_calls_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Object Access Activity + + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes Azure pod scan fingerprint +This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-05-20 + +
+ details + +#### Search +``` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search responseStatus.code=401 +| table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod +|`kubernetes_azure_pod_scan_fingerprint_filter` +``` +#### Associated Analytic Story + +* Kubernetes Scanning Activity + + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Reconnaissance + + +#### Known False Positives +Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes Azure scan fingerprint +This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) +- **Last Updated**: 2020-05-19 + +
+ details + +#### Search +``` +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search responseStatus.code=401 +| table sourceIPs{} userAgent verb requestURI responseStatus.reason +|`kubernetes_azure_scan_fingerprint_filter` +``` +#### Associated Analytic Story + +* Kubernetes Scanning Activity + + +#### How To Implement +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1526 | Cloud Service Discovery | Discovery | + +#### Kill Chain Phase + +* Reconnaissance + + +#### Known False Positives +Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes GCP detect RBAC authorizations by account +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole +| table src_ip src_user data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason +| rare src_user data.labels.authorization.k8s.io/reason +|`kubernetes_gcp_detect_rbac_authorizations_by_account_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Role Activity + + +#### How To Implement +You must install splunk AWS add on for GCP. This search works with pubsub messaging service logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes GCP detect most active service accounts by pod +This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-07-10 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.request.spec.group{}=system:serviceaccounts +| table src_ip src_user http_user_agent data.protoPayload.request.spec.nonResourceAttributes.verb data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource +| top src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource +|`kubernetes_gcp_detect_most_active_service_accounts_by_pod_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Role Activity + + +#### How To Implement +You must install splunk GCP add on. This search works with pubsub messaging service logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes GCP detect sensitive object access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.resource=configmaps OR secrets +| table data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name data.protoPayload.request.metadata.namespace data.labels.authorization.k8s.io/decision +| dedup data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name +|`kubernetes_gcp_detect_sensitive_object_access_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Object Access Activity + + +#### How To Implement +You must install splunk add on for GCP . This search works with pubsub messaging service logs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes GCP detect sensitive role access +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole dest=apis/rbac.authorization.k8s.io/v1 src_ip!=::1 +| table src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason +| dedup src_ip src_user +|`kubernetes_gcp_detect_sensitive_role_access_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Role Activity + + +#### How To Implement +You must install splunk add on for GCP. This search works with pubsub messaging servicelogs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Sensitive role resource access is necessary for cluster operation, however source IP, user agent, decision and reason may indicate possible malicious use. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes GCP detect service accounts forbidden failure access +This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-06-23 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` system:serviceaccounts data.protoPayload.response.status.allowed!=* +| table src_ip src_user http_user_agent data.protoPayload.response.spec.resourceAttributes.namespace data.resource.labels.cluster_name data.protoPayload.response.spec.resourceAttributes.verb data.protoPayload.request.status.allowed data.protoPayload.response.status.reason data.labels.authorization.k8s.io/decision +| dedup src_ip src_user +| `kubernetes_gcp_detect_service_accounts_forbidden_failure_access_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Object Access Activity + + +#### How To Implement +You must install splunk add on for GCP. This search works with pubsub messaging service logs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Kubernetes GCP detect suspicious kubectl calls +This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-07-11 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous +| table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path +|dedup src_ip src_user +|`kubernetes_gcp_detect_suspicious_kubectl_calls_filter` +``` +#### Associated Analytic Story + +* Kubernetes Sensitive Object Access Activity + + +#### How To Implement +You must install splunk add on for GCP. This search works with pubsub messaging logs. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Large Volume of DNS ANY Queries +The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1498.002](https://attack.mitre.org/techniques/T1498.002/) +- **Last Updated**: 2017-09-20 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" +| `drop_dm_object_name("DNS")` +| where count>200 +| `large_volume_of_dns_any_queries_filter` +``` +#### Associated Analytic Story + +* DNS Amplification Attacks + + +#### How To Implement +To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1498.002 | Reflection Amplification | Impact | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### MacOS - Re-opened Applications +This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: +- **Last Updated**: 2020-02-07 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `macos___re_opened_applications_filter` +``` +#### Associated Analytic Story + + +#### How To Implement +In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Installation + +* Command and Control + + +#### Known False Positives +At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Malicious PowerShell Process - Connect To Internet With Hidden Window This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. @@ -12037,6 +18253,65 @@ _version_: 4 --- +### Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments +This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/) +- **Last Updated**: 2021-01-19 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* +| `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter` +``` +#### Associated Analytic Story + +* Malicious PowerShell + + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059.001 | PowerShell | Execution | + +#### Kill Chain Phase + +* Command and Control + +* Actions on Objectives + + +#### Known False Positives +Legitimate process can have this combination of command-line options, but it's not common. + +#### Reference + + +#### Test Dataset + + +_version_: 6 +
+ +--- + ### Malicious PowerShell Process With Obfuscation Techniques This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. @@ -12099,6 +18374,125 @@ _version_: 4 --- +### Monitor DNS For Brand Abuse +This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Resolution +- **ATT&CK**: +- **Last Updated**: 2017-09-23 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query +| `drop_dm_object_name("DNS")` +| `security_content_ctime(firstTime)` +| `brand_abuse_dns` +| `monitor_dns_for_brand_abuse_filter` +``` +#### Associated Analytic Story + +* Brand Monitoring + + +#### How To Implement +You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Delivery + +* Actions on Objectives + + +#### Known False Positives +None at this time + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Monitor Email For Brand Abuse +This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Email +- **ATT&CK**: +- **Last Updated**: 2018-01-05 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id +| `drop_dm_object_name("All_Email")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval temp=split(src_user, "@") +| eval email_domain=mvindex(temp, 1) +| lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse +| search domain_abuse=true +| table message_id, src_user, email_domain, recipients, firstTime, lastTime +| `monitor_email_for_brand_abuse_filter` +``` +#### Associated Analytic Story + +* Brand Monitoring + +* Suspicious Emails + + +#### How To Implement +You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Delivery + + +#### Known False Positives +None at this time + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Monitor Registry Keys for Print Monitors This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. @@ -12157,6 +18551,61 @@ _version_: 2 --- +### Monitor Web Traffic For Brand Abuse +This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Web +- **ATT&CK**: +- **Last Updated**: 2017-09-23 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `brand_abuse_web` +| `monitor_web_traffic_for_brand_abuse_filter` +``` +#### Associated Analytic Story + +* Brand Monitoring + + +#### How To Implement +You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Delivery + + +#### Known False Positives +None at this time + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### More than usual number of LOLBAS applications in short time period Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine. @@ -12400,6 +18849,64 @@ Uploading container is a normal behavior from developers or users with access to #### Test Dataset +_version_: 1 +
+ +--- + +### No Windows Updates in a time frame +This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Updates +- **ATT&CK**: +- **Last Updated**: 2017-09-15 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product +| rename Updates.dest as Host +| rename Updates.status as "Update Status" +| rename Updates.vendor_product as Product +| eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) +| `security_content_ctime(lastTime)` +| search isOutlier=1 +| rename lastTime as "Last Update Time", +| table Host, "Update Status", Product, "Last Update Time" +| `no_windows_updates_in_a_time_frame_filter` +``` +#### Associated Analytic Story + +* Monitor for Updates + + +#### How To Implement +To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + + +#### Known False Positives +None identified + +#### Reference + + +#### Test Dataset + + _version_: 1
@@ -13319,6 +19826,115 @@ _version_: 2 --- +### Open Redirect in Splunk Web +This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-19 + +
+ details + +#### Search +``` +index=_internal sourcetype=splunk_web_access return_to="/%09/*" +| `open_redirect_in_splunk_web_filter` +``` +#### Associated Analytic Story + +* Splunk Enterprise Vulnerability + + +#### How To Implement +No extra steps needed to implement this search. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Delivery + + +#### Known False Positives +None identified + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Osquery pack - ColdRoot detection +This search looks for ColdRoot events from the osx-attacks osquery pack. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2019-01-29 + +
+ details + +#### Search +``` + +| from datamodel Alerts.Alerts +| search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) +| rename columns.path as path +| bucket _time span=30s +| stats count(path) by _time, host, user, path +| `osquery_pack___coldroot_detection_filter` +``` +#### Associated Analytic Story + +* ColdRoot MacOS RAT + + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Installation + +* Command and Control + + +#### Known False Positives +There are no known false positives. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Overwriting Accessibility Binaries Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. @@ -13377,6 +19993,66 @@ _version_: 4 --- +### Phishing Email Detection by Machine Learning Method - SSA +Malicious mails can conduct phishing that induces readers to open attachment, click links or trigger third party service. This detect uses Natural Language Processing (NLP) approach to analyze an email message's content (Sender, Subject and Body) and judge whether it is a phishing email. The detection adopts a deep learning (neural network) model that employs character level embeddings plus LSTM layers to perform classification. The model is pre-trained and then published as ONNX format. Current sample model is trained using the dataset published at https://github.com/splunk/attack_data/tree/master/datasets/T1566_Phishing_Email/splunk_train.json User are expected to re-train the model by combining with their own training data for better accuracy using the provided model file (SMLE notebook). DSP pipeline then processes the email message and passes it as an event to Apply ML Models function, which returns the probability of a phishing email. Current implementation assumes the email is fed to DSP in JSON format contains at least email's sender, subject and its message body, including reply content, if any. + +- **Product**: UEBA for Security Cloud +- **Datamodel**: +- **ATT&CK**: [T1566](https://attack.mitre.org/techniques/T1566/) +- **Last Updated**: 2020-08-25 + +
+ details + +#### Search +``` + +| from read_ssa_enriched_events() +| eval eventLine=concat(ucast(map_get(input_event, "From"), "string", " "), " ", ucast(map_get(input_event, "Subject"), "string", " "), " ", ucast(map_get(input_event, "Content"), "string", " "), " "), _time=map_get(input_event, "_time") +| where eventLine IS NOT NULL +| eval mapC={" ": 32, "!": 33, "\"": 34, "#": 35, "$": 36, "%": 37, "&": 38, "`": 39, "(": 40, ")": 41, "*": 42, "+": 43, ",": 44, "-": 45, ".": 46, "/": 47, "0": 48, "1": 49, "2": 50, "3": 51, "4": 52, "5": 53, "6": 54, "7": 55, "8": 56, "9": 57, ":": 58, ";": 59, "<": 60, "=": 61, ">": 62, "?": 63, "@": 64, "A": 65, "B": 66, "C": 67, "D": 68, "E": 69, "F": 70, "G": 71, "H": 72, "I": 73, "J": 74, "K": 75, "L": 76, "M": 77, "N": 78, "O": 79, "P": 80, "Q": 81, "R": 82, "S": 83, "T": 84, "U": 85, "V": 86, "W": 87, "X": 88, "Y": 89, "Z": 90, "[": 91, "\\": 92, "]": 93, "^": 94, "_": 95, "`": 96, "a": 97, "b": 98, "c": 99, "d": 100, "e": 101, "f": 102, "g": 103, "h": 104, "i": 105, "j": 106, "k": 107, "l": 108, "m": 109, "n": 110, "o": 111, "p": 112, "q": 113, "r": 114, "s": 115, "t": 116, "u": 117, "v": 118, "w": 119, "x": 120, "y": 121, "z": 122, "{": 123, " +|": 124, "}": 125, "~": 126}, ml_in = for_each(iterator(mvrange(1,129), "i"), cast(map_get(mapC, substr(eventLine, i, 1)), "float") ) +| apply_model connection_id="YOUR_S3_ONNX_CONNECTOR_ID" name="phishing_email_v8" path="s3://smle-experiments/models/phishing_email" +| eval probability = mvindex(ml_out, 0) +| where probability > 0.5 +| eval start_time=_time, end_time=_time, entities="TBD", body="TBD" +| select probability, body, entities, start_time, end_time +| into write_ssa_detected_events(); +``` +#### Associated Analytic Story + + +#### How To Implement +Events are fed to DSP contains at least email's sender, subject and its message body. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1566 | Phishing | Initial Access | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Because of imbalance of anomaly data in training, the model will less likely report false positive. Instead, the model is more prone to false negative. Current best recall score is ~85% + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Probing Access with Stolen Credentials via PowerSploit modules This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services. @@ -13573,6 +20249,118 @@ _version_: 3 --- +### Processes Tapping Keyboard Events +This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2019-01-25 + +
+ details + +#### Search +``` + +| from datamodel Alerts.Alerts +| search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps +| rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id +| dedup host,process_name +| table host,process_name, cmd, process_id +| `processes_tapping_keyboard_events_filter` +``` +#### Associated Analytic Story + +* ColdRoot MacOS RAT + + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Command and Control + + +#### Known False Positives +There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Processes created by netsh +This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1562.004](https://attack.mitre.org/techniques/T1562.004/) +- **Last Updated**: 2020-11-23 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `processes_created_by_netsh_filter` +``` +#### Associated Analytic Story + +* Netsh Abuse + + +#### How To Implement +To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1562.004 | Disable or Modify System Firewall | Defense Evasion | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft. + +#### Reference + + +#### Test Dataset + + +_version_: 5 +
+ +--- + ### Processes launching netsh This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. @@ -13635,6 +20423,249 @@ _version_: 3 --- +### Prohibited Network Traffic Allowed +This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Traffic +- **ATT&CK**: [T1048](https://attack.mitre.org/techniques/T1048/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action +| lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport +| search is_prohibited=true +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `prohibited_network_traffic_allowed_filter` +``` +#### Associated Analytic Story + +* Prohibited Traffic Allowed or Protocol Mismatch + +* Ransomware + +* Command and Control + + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1048 | Exfiltration Over Alternative Protocol | Exfiltration | + +#### Kill Chain Phase + +* Delivery + +* Command and Control + + +#### Known False Positives +None identified + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Prohibited Software On Endpoint +This search looks for applications on the endpoint that you have marked as prohibited. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: +- **Last Updated**: 2019-10-11 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `prohibited_softwares` +| `prohibited_software_on_endpoint_filter` +``` +#### Associated Analytic Story + +* Monitor for Unauthorized Software + +* Emotet Malware DHS Report TA18-201A + +* SamSam Ransomware + + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Installation + +* Command and Control + +* Actions on Objectives + + +#### Known False Positives +None identified + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Protocol or Port Mismatch +This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Traffic +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port +|`security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `protocol_or_port_mismatch_filter` +``` +#### Associated Analytic Story + +* Prohibited Traffic Allowed or Protocol Mismatch + +* Command and Control + + +#### How To Implement +Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | + +#### Kill Chain Phase + +* Command and Control + + +#### Known False Positives +None identified + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + +### Protocols passing authentication in cleartext +This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Network_Traffic +- **ATT&CK**: +- **Last Updated**: 2020-11-04 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `protocols_passing_authentication_in_cleartext_filter` +``` +#### Associated Analytic Story + +* Use of Cleartext Protocols + + +#### How To Implement +This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Reconnaissance + +* Actions on Objectives + + +#### Known False Positives +Some networks may use kerberized FTP or telnet servers, however, this is rare. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Rare Parent-Child Process Relationship An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries @@ -14803,6 +21834,67 @@ _version_: 5 --- +### Reg exe used to hide files directories via registry keys +The search looks for command-line arguments used to hide a file or directory using the reg add command. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1564.001](https://attack.mitre.org/techniques/T1564.001/) +- **Last Updated**: 2019-02-27 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| regex process = "(/d\s+2)" +| `reg_exe_used_to_hide_files_directories_via_registry_keys_filter` +``` +#### Associated Analytic Story + +* Windows Defense Evasion Tactics + +* Suspicious Windows Registry Activities + +* Windows Persistence Techniques + + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1564.001 | Hidden Files and Directories | Defense Evasion | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +None at the moment + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Registry Keys Used For Persistence The search looks for modifications to registry keys that can be used to launch an application or service at system startup. @@ -15120,6 +22212,64 @@ _version_: 3 --- +### Remote Desktop Process Running On System +This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021.001/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `remote_desktop_process_running_on_system_filter` +``` +#### Associated Analytic Story + +* Hidden Cobra Malware + +* Lateral Movement + + +#### How To Implement +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1021.001 | Remote Desktop Protocol | Lateral Movement | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Remote Desktop may be used legitimately by users on the network. + +#### Reference + + +#### Test Dataset + + +_version_: 5 +
+ +--- + ### Remote Process Instantiation via WMI This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. @@ -15180,6 +22330,121 @@ _version_: 5 --- +### Remote Registry Key modifications +This search monitors for remote modifications to registry keys. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-03-02 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `remote_registry_key_modifications_filter` +``` +#### Associated Analytic Story + +* Windows Defense Evasion Tactics + +* Suspicious Windows Registry Activities + +* Windows Persistence Techniques + + +#### How To Implement +To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + +### Remote WMI Command Attempt +This search looks for wmic.exe being launched with parameters to operate on remote systems. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) +- **Last Updated**: 2018-12-03 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `remote_wmi_command_attempt_filter` +``` +#### Associated Analytic Story + +* Suspicious WMI Use + + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1047 | Windows Management Instrumentation | Execution | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Administrators may use this legitimately to gather info from remote systems. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### RunDLL Loading DLL By Ordinal This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. @@ -15431,6 +22696,62 @@ _version_: 3 --- +### SQL Injection with Long URLs +This search looks for long URLs that have several SQL commands visible within them. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Web +- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent +| `drop_dm_object_name("Web")` +| eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 +| where num_sql_cmds > 3 +| `sql_injection_with_long_urls_filter` +``` +#### Associated Analytic Story + +* SQL Injection + + +#### How To Implement +To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1190 | Exploit Public-Facing Application | Initial Access | + +#### Kill Chain Phase + +* Delivery + + +#### Known False Positives +It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Samsam Test File Write The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. @@ -15617,6 +22938,63 @@ _version_: 5 --- +### Scheduled tasks used in BadRabbit ransomware +This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/) +- **Last Updated**: 2020-07-21 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| search (process=*rhaegal* OR process=*drogon* OR *viserion_*) +| `scheduled_tasks_used_in_badrabbit_ransomware_filter` +``` +#### Associated Analytic Story + +* Ransomware + + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +No known false positives + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + ### Schtasks scheduling job on remote system This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. @@ -16245,6 +23623,586 @@ _version_: 3 --- +### Spectre and Meltdown Vulnerable Systems +The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Vulnerabilities +- **ATT&CK**: +- **Last Updated**: 2017-01-07 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest +| `drop_dm_object_name(Vulnerabilities)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `spectre_and_meltdown_vulnerable_systems_filter` +``` +#### Associated Analytic Story + +* Spectre And Meltdown Vulnerabilities + + +#### How To Implement +The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + + +#### Known False Positives +It is possible that your vulnerability scanner is not detecting that the patches have been applied. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Spike in File Writes +The search looks for a sharp increase in the number of files written to a particular host + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2020-03-16 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest +| `drop_dm_object_name(Filesystem)` +| eventstats max(_time) as maxtime +| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) +| search isOutlier=1 +| `spike_in_file_writes_filter` +``` +#### Associated Analytic Story + +* SamSam Ransomware + +* Ryuk Ransomware + +* Ransomware + + +#### How To Implement +In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + +### Splunk Enterprise Information Disclosure +This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-06-14 + +
+ details + +#### Search +``` +index=_internal sourcetype=splunkd_ui_access server-info +| search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" +| rename clientip as src_ip, splunk_server as dest +| stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `splunk_enterprise_information_disclosure_filter` +``` +#### Associated Analytic Story + +* Splunk Enterprise Vulnerability CVE-2018-11409 + + +#### How To Implement +The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Delivery + + +#### Known False Positives +Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Sunburst Correlation DLL and Network Event +The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/) +- **Last Updated**: 2020-12-14 + +
+ details + +#### Search +``` +(`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) +| eventstats dc(EventCode) AS dc_events +| where dc_events=2 +| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host +| rename host as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `sunburst_correlation_dll_and_network_event_filter` +``` +#### Associated Analytic Story + +* Sunburst Malware + + +#### How To Implement +This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1203 | Exploitation for Client Execution | Execution | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +unknown + +#### Reference + +* https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Supernova Webshell +This search aims to detect the Supernova webshell used in the SUNBURST attack. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Web +- **ATT&CK**: [T1505.003](https://attack.mitre.org/techniques/T1505.003/) +- **Last Updated**: 2021-01-06 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count from datamodel=Web.Web where web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s +| `supernova_webshell_filter` +``` +#### Associated Analytic Story + +* Sunburst Malware + + +#### How To Implement +To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1505.003 | Web Shell | Persistence | + +#### Kill Chain Phase + +* Exfiltration + + +#### Known False Positives +There might be false positives associted with this detection since items like args as a web argument is pretty generic. + +#### Reference + +* https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html + +* https://www.guidepointsecurity.com/supernova-solarwinds-net-webshell-analysis/ + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Suspicious Changes to File Associations +This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1546.001](https://attack.mitre.org/techniques/T1546.001/) +- **Last Updated**: 2020-07-22 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| join [ +| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest +| `drop_dm_object_name("Registry")` +| table process_id dest registry_path] +| `suspicious_changes_to_file_associations_filter` +``` +#### Associated Analytic Story + +* Suspicious Windows Registry Activities + +* Windows File Extension and Association Abuse + + +#### How To Implement +To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1546.001 | Change Default File Association | Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. + +#### Reference + + +#### Test Dataset + + +_version_: 4 +
+ +--- + +### Suspicious Email - UBA Anomaly +This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: UEBA +- **ATT&CK**: [T1566](https://attack.mitre.org/techniques/T1566/) +- **Last Updated**: 2020-07-22 + +
+ details + +#### Search +``` + +|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model +| `drop_dm_object_name(All_UEBA_Events)` +| `drop_dm_object_name(UEBA_Anomalies)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_email___uba_anomaly_filter` +``` +#### Associated Analytic Story + +* Suspicious Emails + + +#### How To Implement +You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1566 | Phishing | Initial Access | + +#### Kill Chain Phase + +* Delivery + + +#### Known False Positives +This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and add the URL to an allow list if you determine that it is a legitimate sender. + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + +### Suspicious Email Attachment Extensions +This search looks for emails that have attachments with suspicious file extensions. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Email +- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/) +- **Last Updated**: 2020-07-22 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Email")` +| `suspicious_email_attachments` +| `suspicious_email_attachment_extensions_filter` +``` +#### Associated Analytic Story + +* Emotet Malware DHS Report TA18-201A + +* Suspicious Emails + + +#### How To Implement +You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ + **Splunk Phantom Playbook Integration**\ +If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1566.001 | Spearphishing Attachment | Initial Access | + +#### Kill Chain Phase + +* Delivery + + +#### Known False Positives +None identified + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + +### Suspicious File Write +The search looks for files created with names that have been linked to malicious activity. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2019-04-25 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Filesystem)` +| `suspicious_writes` +| `suspicious_file_write_filter` +``` +#### Associated Analytic Story + +* Hidden Cobra Malware + + +#### How To Implement +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. + +#### Reference + + +#### Test Dataset + + +_version_: 3 +
+ +--- + +### Suspicious Java Classes +This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-12-06 + +
+ details + +#### Search +``` +`stream_http` http_method=POST http_content_length>1 +| regex form_data="(?i)java\.lang\.(?:runtime +|processbuilder)" +| rename src_ip as src +| stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_java_classes_filter` +``` +#### Associated Analytic Story + +* Apache Struts Vulnerability + + +#### How To Implement +In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Exploitation + + +#### Known False Positives +There are no known false positives. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Suspicious MSBuild Rename The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. @@ -17161,6 +25119,59 @@ _version_: 3 --- +### Suspicious writes to System Volume Information +This search detects writes to the 'System Volume Information' folder by something other than the System process. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1036](https://attack.mitre.org/techniques/T1036/) +- **Last Updated**: 2020-07-22 + +
+ details + +#### Search +``` +(`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* +| stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_writes_to_system_volume_information_filter` +``` +#### Associated Analytic Story + +* Collection and Staging + + +#### How To Implement +You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1036 | Masquerading | Defense Evasion | + +#### Kill Chain Phase + + +#### Known False Positives +It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. + +#### Reference + + +#### Test Dataset + + +_version_: 2 +
+ +--- + ### Suspicious writes to windows Recycle Bin This search detects writes to the recycle bin by a process other than explorer.exe. @@ -17557,6 +25568,67 @@ _version_: 2 --- +### Uncommon Processes On Endpoint +This search looks for applications on the endpoint that you have marked as uncommon. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1204.002](https://attack.mitre.org/techniques/T1204.002/) +- **Last Updated**: 2020-07-22 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `uncommon_processes` +|`uncommon_processes_on_endpoint_filter` +``` +#### Associated Analytic Story + +* Windows Privilege Escalation + +* Unusual Processes + +* Cloud Federated Credential Abuse + + +#### How To Implement +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1204.002 | Malicious File | Execution | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +None identified + +#### Reference + + +#### Test Dataset + + +_version_: 4 +
+ +--- + ### Unload Sysmon Filter Driver Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. @@ -17616,6 +25688,118 @@ _version_: 3 --- +### Unsigned Image Loaded by LSASS +This search detects loading of unsigned images by LSASS. Deprecated because too noisy. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/) +- **Last Updated**: 2019-12-06 + +
+ details + +#### Search +``` +`sysmon` EventID=7 Image=*lsass.exe Signed=false +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `unsigned_image_loaded_by_lsass_filter` +``` +#### Associated Analytic Story + +* Credential Dumping + + +#### How To Implement +This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.001 | LSASS Memory | Credential Access | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. + +#### Reference + +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Unsuccessful Netbackup backups +This search gives you the hosts where a backup was attempted and then failed. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2017-09-12 + +
+ details + +#### Search +``` +`netbackup` +| stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE +| search MESSAGE="An error occurred, failed to backup." +| `security_content_ctime(latestTime)` +| rename COMPUTERNAME as dest, MESSAGE as signature +| table latestTime, dest, signature +| `unsuccessful_netbackup_backups_filter` +``` +#### Associated Analytic Story + +* Monitor Backup Solution + + +#### How To Implement +To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + + +#### Known False Positives +None identified + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Unusually Long Command Line Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez. @@ -17939,6 +26123,65 @@ Administrators may modify the boot configuration. * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log +_version_: 1 +
+ +--- + +### WMI Permanent Event Subscription +This search looks for the creation of WMI permanent event subscriptions. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) +- **Last Updated**: 2018-10-23 + +
+ details + +#### Search +``` +`wmi` EventCode=5861 Binding +| rex field=Message "Consumer =\s+(?[^; +|^$]+)" +| search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" +| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename ComputerName as dest +| `wmi_permanent_event_subscription_filter` +``` +#### Associated Analytic Story + +* Suspicious WMI Use + + +#### How To Implement +To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1047 | Windows Management Instrumentation | Execution | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Although unlikely, administrators may use event subscriptions for legitimate purposes. + +#### Reference + + +#### Test Dataset + + _version_: 1
@@ -18000,6 +26243,64 @@ _version_: 2 --- +### WMI Temporary Event Subscription +This search looks for the creation of WMI temporary event subscriptions. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/) +- **Last Updated**: 2018-10-23 + +
+ details + +#### Search +``` +`wmi` EventCode=5860 Temporary +| rex field=Message "NotificationQuery =\s+(?[^; +|^$]+)" +| search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" +| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `wmi_temporary_event_subscription_filter` +``` +#### Associated Analytic Story + +* Suspicious WMI Use + + +#### How To Implement +To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1047 | Windows Management Instrumentation | Execution | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + ### Web Fraud - Account Harvesting This search is used to identify the creation of multiple user accounts using the same email domain name. @@ -18304,6 +26605,62 @@ administrators rarely use adfind, usually not used for legitimate reasons * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log +_version_: 1 +
+ +--- + +### Windows DisableAntiSpyware Registry +The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562.001/) +- **Last Updated**: 2020-11-06 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name +| `drop_dm_object_name(Registry)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `windows_disableantispyware_registry_filter` +``` +#### Associated Analytic Story + +* Ryuk Ransomware + + +#### How To Implement +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1562.001 | Disable or Modify Tools | Defense Evasion | + +#### Kill Chain Phase + +* Delivery + + +#### Known False Positives +It is unusual to turn this feature on a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. + +#### Reference + + +#### Test Dataset + + _version_: 1
@@ -18423,6 +26780,445 @@ SAM is a critical windows service, stopping it would cause major issues on an en * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log +_version_: 1 +
+ +--- + +### Windows connhost exe started forcefully +The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059.003/) +- **Last Updated**: 2020-11-06 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `windows_connhost_exe_started_forcefully_filter` +``` +#### Associated Analytic Story + +* Ryuk Ransomware + + +#### How To Implement +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1059.003 | Windows Command Shell | Execution | + +#### Kill Chain Phase + +* Delivery + + +#### Known False Positives +This process should not be ran forcefully, we have not see any false positives for this detection + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### Windows hosts file modification +The search looks for modifications to the hosts file on all Windows endpoints across your environment. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: +- **Last Updated**: 2018-11-02 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* +| `drop_dm_object_name(Filesystem)` +| `windows_hosts_file_modification_filter` +``` +#### Associated Analytic Story + +* Host Redirection + + +#### How To Implement +To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| + +#### Kill Chain Phase + +* Command and Control + + +#### Known False Positives +There may be legitimate reasons for system administrators to add entries to this file. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### aws detect attach to role policy +This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) +- **Last Updated**: 2020-07-27 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` attach policy +| spath requestParameters.policyArn +| table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate +| `aws_detect_attach_to_role_policy_filter` +``` +#### Associated Analytic Story + +* AWS Cross Account Activity + + +#### How To Implement +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### aws detect permanent key creation +This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) +- **Last Updated**: 2020-07-27 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` CreateAccessKey +| spath eventName +| search eventName=CreateAccessKey "userIdentity.type"=IAMUser +| table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId +|`aws_detect_permanent_key_creation_filter` +``` +#### Associated Analytic Story + +* AWS Cross Account Activity + + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### aws detect role creation +This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) +- **Last Updated**: 2020-07-27 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* +| table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate +| `aws_detect_role_creation_filter` +``` +#### Associated Analytic Story + +* AWS Cross Account Activity + + +#### How To Implement +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### aws detect sts assume role abuse +This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) +- **Last Updated**: 2020-07-27 + +
+ details + +#### Search +``` +`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role +| table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate +| `aws_detect_sts_assume_role_abuse_filter` +``` +#### Associated Analytic Story + +* AWS Cross Account Activity + + +#### How To Implement +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### aws detect sts get session token abuse +This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1550](https://attack.mitre.org/techniques/T1550/) +- **Last Updated**: 2020-07-27 + +
+ details + +#### Search +``` +`aws_cloudwatchlogs_eks` ASIA userIdentity.type=IAMUser +| spath eventName +| search eventName=GetSessionToken +| table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region +| `aws_detect_sts_get_session_token_abuse_filter` +``` +#### Associated Analytic Story + +* AWS Cross Account Activity + + +#### How To Implement +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1550 | Use Alternate Authentication Material | Defense Evasion, Lateral Movement | + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used. + +#### Reference + + +#### Test Dataset + + +_version_: 1 +
+ +--- + +### gcp detect oauth token abuse +This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) +- **Last Updated**: 2020-09-01 + +
+ details + +#### Search +``` +`google_gcp_pubsub_message` type.googleapis.com/google.cloud.audit.AuditLog +|table protoPayload.@type protoPayload.status.details{}.@type protoPayload.status.details{}.violations{}.callerIp protoPayload.status.details{}.violations{}.type protoPayload.status.message +| `gcp_detect_oauth_token_abuse_filter` +``` +#### Associated Analytic Story + +* GCP Cross Account Activity + + +#### How To Implement +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + +#### Kill Chain Phase + +* Lateral Movement + + +#### Known False Positives +GCP Oauth token abuse detection will only work if there are access policies in place along with audit logs. + +#### Reference + +* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-1 + +* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-2 + + +#### Test Dataset + + _version_: 1
diff --git a/docs/stories.md b/docs/stories.md index c2c167dfc9..c0e95b87d1 100644 --- a/docs/stories.md +++ b/docs/stories.md @@ -11,9 +11,10 @@ All the Analytic Stories shipped to different Splunk products. Below is a breakd ### Brand Monitoring Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-12-19
@@ -21,6 +22,13 @@ Detect and investigate activity that may indicate that an adversary is using fau #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -47,9 +55,10 @@ _version_: 1 ### DNS Amplification Attacks DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2016-09-13
@@ -57,6 +66,9 @@ DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it respond #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -81,9 +93,10 @@ _version_: 1 ### Data Protection Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-14
@@ -91,6 +104,13 @@ Fortify your data-protection arsenal--while continuing to ensure data confidenti #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -117,9 +137,10 @@ _version_: 1 ### Host Redirection Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-14
@@ -127,6 +148,13 @@ Detect evidence of tactics used to redirect traffic from a host to a destination #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -149,9 +177,10 @@ _version_: 1 ### Netsh Abuse Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-01-05
@@ -159,6 +188,11 @@ Detect activities and various techniques associated with the abuse of `netsh.exe #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -185,9 +219,10 @@ _version_: 1 ### Web Fraud Detection Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-10-08
@@ -195,6 +230,13 @@ Monitor your environment for activity consistent with common attack techniques b #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -225,9 +267,10 @@ _version_: 1 ### Baron Samedit CVE-2021-3156 Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Research Team, this vulnerability has been found to affect sudo across multiple Linux distributions (Ubuntu 20.04 and prior, Debian 10 and prior, Fedora 33 and prior). As this vulnerability was committed to code in July 2011, there will be many distributions affected. Successful exploitation of this vulnerability allows any unprivileged user to gain root privileges on the vulnerable host. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2021-01-27
@@ -235,6 +278,13 @@ Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Researc #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -257,9 +307,10 @@ _version_: 1 ### Cobalt Strike Cobalt Strike is threat emulation software. Red teams and penetration testers use Cobalt Strike to demonstrate the risk of a breach and evaluate mature security programs. Most recently, Cobalt Strike has become the choice tool by threat groups due to its ease of use and extensibility. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2021-02-16
@@ -267,6 +318,11 @@ Cobalt Strike is threat emulation software. Red teams and penetration testers us #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -297,9 +353,10 @@ _version_: 1 ### Collection and Staging Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-02-03
@@ -307,6 +364,17 @@ Monitor for and investigate activities--such as suspicious writes to the Windows #### Detection Profile +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -331,9 +399,10 @@ _version_: 1 ### Command and Control Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-06-01
@@ -341,6 +410,33 @@ Detect and investigate tactics, techniques, and procedures leveraged by attacker #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -365,9 +461,10 @@ _version_: 1 ### Common Phishing Frameworks Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2019-04-29
@@ -375,6 +472,9 @@ Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -401,9 +501,10 @@ _version_: 1 ### Credential Dumping Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-02-04
@@ -411,6 +512,41 @@ Uncover activity consistent with credential dumping, a technique wherein attacke #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -435,9 +571,10 @@ _version_: 3 ### DNS Hijacking Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-02-04
@@ -445,6 +582,15 @@ Secure your environment against DNS hijacks with searches that help you detect a #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -473,9 +619,10 @@ _version_: 1 ### Data Exfiltration The stealing of data by an adversary. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-10-21
@@ -483,6 +630,9 @@ The stealing of data by an adversary. #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -505,9 +655,10 @@ _version_: 1 ### Detect Zerologon Attack Uncover activity related to the execution of Zerologon CVE-2020-11472, a technique wherein attackers target a Microsoft Windows Domain Controller to reset its computer account password. The result from this attack is attackers can now provide themselves high privileges and take over Domain Controller. The included searches in this Analytic Story are designed to identify attempts to reset Domain Controller Computer Account via exploit code remotely or via the use of tool Mimikatz as payload carrier. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-09-18
@@ -515,6 +666,15 @@ Uncover activity related to the execution of Zerologon CVE-2020-11472, a techniq #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -543,9 +703,10 @@ _version_: 1 ### Disabling Security Tools Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-02-04
@@ -553,6 +714,19 @@ Looks for activities and techniques associated with the disabling of security to #### Detection Profile +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -579,9 +753,10 @@ _version_: 2 ### F5 TMUI RCE CVE-2020-5902 Uncover activity consistent with CVE-2020-5902. Discovered by Positive Technologies researchers, this vulnerability affects F5 BIG-IP, BIG-IQ. and Traffix SDC devices (vulnerable versions in F5 support link below). This vulnerability allows unauthenticated users, along with authenticated users, who have access to the configuration utility to execute system commands, create/delete files, disable services, and/or execute Java code. This vulnerability can result in full system compromise. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-08-02
@@ -589,6 +764,9 @@ Uncover activity consistent with CVE-2020-5902. Discovered by Positive Technolog #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -615,9 +793,10 @@ _version_: 1 ### Lateral Movement Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-02-04
@@ -625,6 +804,17 @@ Detect and investigate tactics, techniques, and procedures around how attackers #### Detection Profile +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -647,9 +837,10 @@ _version_: 2 ### Malicious PowerShell Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-08-23
@@ -657,6 +848,17 @@ Attackers are finding stealthy ways "live off the land," leveraging utilities an #### Detection Profile +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -681,9 +883,10 @@ _version_: 4 ### Phishing Payloads Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2019-04-29
@@ -691,6 +894,11 @@ Detect signs of malicious payloads that may indicate that your environment has b #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -713,9 +921,10 @@ _version_: 1 ### Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-01-22
@@ -723,6 +932,17 @@ Monitor your environment for suspicious behaviors that resemble the techniques e #### Detection Profile +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -747,9 +967,10 @@ _version_: 1 ### SQL Injection Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-19
@@ -757,6 +978,9 @@ Use the searches in this Analytic Story to help you detect structured query lang #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -781,9 +1005,10 @@ _version_: 1 ### Sunburst Malware Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and management software. It was discovered by FireEye in December 2020. The actors behind this campaign gained access to numerous public and private organizations around the world. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-12-14
@@ -791,6 +1016,29 @@ Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and managemen #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -815,9 +1063,10 @@ _version_: 1 ### Suspicious Command-Line Executions Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-02-03
@@ -825,6 +1074,19 @@ Leveraging the Windows command-line interface (CLI) is one of the most common at #### Detection Profile +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -851,9 +1113,10 @@ _version_: 2 ### Suspicious Compiled HTML Activity Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2021-02-11
@@ -861,6 +1124,15 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -887,9 +1159,10 @@ _version_: 1 ### Suspicious DNS Traffic Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-18
@@ -897,6 +1170,23 @@ Attackers often attempt to hide within or otherwise abuse the domain name system #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -923,9 +1213,10 @@ _version_: 1 ### Suspicious Emails Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-01-27
@@ -933,6 +1224,15 @@ Email remains one of the primary means for attackers to gain an initial foothold #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -955,9 +1255,10 @@ _version_: 1 ### Suspicious MSHTA Activity Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2021-01-20
@@ -965,6 +1266,23 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -993,9 +1311,10 @@ _version_: 2 ### Suspicious Okta Activity Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-04-02
@@ -1003,6 +1322,15 @@ Monitor your Okta environment for suspicious activities. Due to the Covid outbre #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1029,9 +1357,10 @@ _version_: 1 ### Suspicious Regsvcs Regasm Activity Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2021-02-11
@@ -1039,6 +1368,19 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce #### Detection Profile +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1065,9 +1407,10 @@ _version_: 1 ### Suspicious Regsvr32 Activity Monitor and detect techniques used by attackers who leverage the regsvr32.exe process to execute malicious code. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2021-01-29
@@ -1075,6 +1418,11 @@ Monitor and detect techniques used by attackers who leverage the regsvr32.exe pr #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1101,9 +1449,10 @@ _version_: 1 ### Suspicious Rundll32 Activity Monitor and detect techniques used by attackers who leverage rundll32.exe to execute arbitrary malicious code. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2021-02-03
@@ -1111,6 +1460,23 @@ Monitor and detect techniques used by attackers who leverage rundll32.exe to exe #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1137,9 +1503,10 @@ _version_: 1 ### Suspicious WMI Use Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-10-23
@@ -1147,6 +1514,21 @@ Attackers are increasingly abusing Windows Management Instrumentation (WMI), a f #### Detection Profile +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1171,9 +1553,10 @@ _version_: 2 ### Suspicious Windows Registry Activities Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-05-31
@@ -1181,6 +1564,23 @@ Monitor and detect registry changes initiated from remote locations, which can b #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1205,9 +1605,10 @@ _version_: 1 ### Suspicious Zoom Child Processes Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-04-13
@@ -1215,6 +1616,11 @@ Attackers are using Zoom as an vector to increase privileges on a sytems. This s #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1239,9 +1645,10 @@ _version_: 1 ### Trusted Developer Utilities Proxy Execution Monitor and detect behaviors used by attackers who leverage trusted developer utilities to execute malicious code. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2021-01-12
@@ -1249,6 +1656,11 @@ Monitor and detect behaviors used by attackers who leverage trusted developer ut #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1275,9 +1687,10 @@ _version_: 1 ### Trusted Developer Utilities Proxy Execution MSBuild Monitor and detect techniques used by attackers who leverage the msbuild.exe process to execute malicious code. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2021-01-21
@@ -1285,6 +1698,13 @@ Monitor and detect techniques used by attackers who leverage the msbuild.exe pro #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1317,9 +1737,10 @@ _version_: 1 ### Windows DNS SIGRed CVE-2020-1350 Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpoint researchers, this vulnerability affects Windows 2003 to 2019, and is triggered by a malicious DNS response (only affects DNS over TCP). An attacker can use the malicious payload to cause a buffer overflow on the vulnerable system, leading to compromise. The included searches in this Analytic Story are designed to identify the large response payload for SIG and KEY DNS records which can be used for the exploit. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-07-28
@@ -1327,6 +1748,11 @@ Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpo #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1351,9 +1777,10 @@ _version_: 1 ### Windows Defense Evasion Tactics Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-05-31
@@ -1361,6 +1788,17 @@ Detect tactics used by malware to evade defenses on Windows endpoints. A few of #### Detection Profile +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1383,9 +1821,10 @@ _version_: 1 ### Windows Log Manipulation Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-12
@@ -1393,6 +1832,15 @@ Adversaries often try to cover their tracks by manipulating Windows logs. Use th #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1419,9 +1867,10 @@ _version_: 2 ### Windows Persistence Techniques Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-05-31
@@ -1429,6 +1878,33 @@ Monitor for activities and techniques associated with maintaining persistence on #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1459,9 +1935,10 @@ _version_: 2 ### Windows Privilege Escalation Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-02-04
@@ -1469,6 +1946,15 @@ Monitor for and investigate activities that may be associated with a Windows pri #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1497,9 +1983,10 @@ _version_: 2 ### Asset Tracking Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-13
@@ -1507,6 +1994,9 @@ Keep a careful inventory of every asset on your network to make it easier to det #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1529,9 +2019,10 @@ _version_: 1 ### Monitor Backup Solution Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-12
@@ -1539,6 +2030,11 @@ Address common concerns when monitoring your backup processes. These searches ca #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1561,9 +2057,10 @@ _version_: 1 ### Monitor for Unauthorized Software Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-15
@@ -1571,6 +2068,9 @@ Identify and investigate prohibited/unauthorized software or processes that may #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1593,9 +2093,10 @@ _version_: 1 ### Monitor for Updates Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-15
@@ -1603,6 +2104,9 @@ Monitor your enterprise to ensure that your endpoints are being patched and upda #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1625,9 +2129,10 @@ _version_: 1 ### Prohibited Traffic Allowed or Protocol Mismatch Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-11
@@ -1635,6 +2140,15 @@ Detect instances of prohibited network traffic allowed in the environment, as we #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1657,9 +2171,10 @@ _version_: 1 ### Router and Infrastructure Security Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-12
@@ -1667,6 +2182,21 @@ Validate the security configuration of network infrastructure and verify that on #### Detection Profile +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1691,9 +2221,10 @@ _version_: 1 ### Use of Cleartext Protocols Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-15
@@ -1701,6 +2232,9 @@ Leverage searches that detect cleartext network protocols that may leak credenti #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1729,9 +2263,10 @@ _version_: 1 ### AWS Cross Account Activity Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-06-04
@@ -1739,6 +2274,17 @@ Track when a user assumes an IAM role in another AWS account to obtain cross-acc #### Detection Profile +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1761,9 +2307,10 @@ _version_: 1 ### AWS Cryptomining Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-03-08
@@ -1771,6 +2318,19 @@ Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomin #### Detection Profile +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1793,9 +2353,10 @@ _version_: 1 ### AWS Network ACL Activity Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-05-21
@@ -1803,6 +2364,15 @@ Monitor your AWS network infrastructure for bad configurations and malicious act #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1827,9 +2397,10 @@ _version_: 2 ### AWS Security Hub Alerts This story is focused around detecting Security Hub alerts generated from AWS + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-08-04
@@ -1837,6 +2408,11 @@ This story is focused around detecting Security Hub alerts generated from AWS #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1859,9 +2435,10 @@ _version_: 1 ### AWS Suspicious Provisioning Activities Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-03-16
@@ -1869,6 +2446,15 @@ Monitor your AWS provisioning activities for behaviors originating from unfamili #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1891,9 +2477,10 @@ _version_: 1 ### AWS User Monitoring Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-03-12
@@ -1901,6 +2488,17 @@ Detect and investigate dormant user accounts for your AWS environment that have #### Detection Profile +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1925,9 +2523,10 @@ _version_: 1 ### Cloud Cryptomining Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2019-10-02
@@ -1935,6 +2534,17 @@ Monitor your cloud compute instances for activities related to cryptojacking/cry #### Detection Profile +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1957,9 +2567,10 @@ _version_: 1 ### Cloud Federated Credential Abuse This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2021-01-26
@@ -1967,6 +2578,31 @@ This analytical story addresses events that indicate abuse of cloud federated cr #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -1993,9 +2629,10 @@ _version_: 1 ### Container Implantation Monitoring and Investigation Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-02-20
@@ -2003,6 +2640,11 @@ Use the searches in this story to monitor your Kubernetes registry repositories #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2025,9 +2667,10 @@ _version_: 1 ### GCP Cross Account Activity Track when a user assumes an IAM role in another GCP account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-09-01
@@ -2035,6 +2678,15 @@ Track when a user assumes an IAM role in another GCP account to obtain cross-acc #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2057,9 +2709,10 @@ _version_: 1 ### Kubernetes Scanning Activity This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-04-15
@@ -2067,6 +2720,19 @@ This story addresses detection against Kubernetes cluster fingerprint scan and a #### Detection Profile +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2089,9 +2755,10 @@ _version_: 1 ### Kubernetes Sensitive Object Access Activity This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-05-20
@@ -2099,6 +2766,25 @@ This story addresses detection and response of accounts acccesing Kubernetes clu #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2121,9 +2807,10 @@ _version_: 1 ### Kubernetes Sensitive Role Activity This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-05-20
@@ -2131,6 +2818,25 @@ This story addresses detection and response around Sensitive Role usage within a #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2153,9 +2859,10 @@ _version_: 1 ### Office 365 Detections This story is focused around detecting Office 365 Attacks. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-12-16
@@ -2163,6 +2870,31 @@ This story is focused around detecting Office 365 Attacks. #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2185,9 +2917,10 @@ _version_: 1 ### Suspicious AWS EC2 Activities Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-02-09
@@ -2195,6 +2928,19 @@ Use the searches in this Analytic Story to monitor your AWS EC2 instances for ev #### Detection Profile +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2217,9 +2963,10 @@ _version_: 1 ### Suspicious AWS Login Activities Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2019-05-01
@@ -2227,6 +2974,15 @@ Monitor your AWS authentication events using your CloudTrail logs. Searches with #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2249,9 +3005,10 @@ _version_: 1 ### Suspicious AWS S3 Activities Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-07-24
@@ -2259,6 +3016,15 @@ Use the searches in this Analytic Story to monitor your AWS S3 buckets for evide #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2283,9 +3049,10 @@ _version_: 2 ### Suspicious AWS Traffic Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-05-07
@@ -2293,6 +3060,9 @@ Leverage these searches to monitor your AWS network traffic for evidence of anom #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2315,9 +3085,10 @@ _version_: 1 ### Suspicious Cloud Authentication Activities Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-06-04
@@ -2325,6 +3096,17 @@ Monitor your cloud authentication events. Searches within this Analytic Story le #### Detection Profile +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2349,9 +3131,10 @@ _version_: 1 ### Suspicious Cloud Instance Activities Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-08-25
@@ -2359,6 +3142,13 @@ Monitor your cloud infrastructure provisioning activities for behaviors originat #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2381,9 +3171,10 @@ _version_: 1 ### Suspicious Cloud Provisioning Activities Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-08-20
@@ -2391,6 +3182,15 @@ Monitor your cloud infrastructure provisioning activities for behaviors originat #### Detection Profile +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2413,9 +3213,10 @@ _version_: 1 ### Suspicious Cloud User Activities Detect and investigate suspicious activities by users and roles in your cloud environments. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-09-04
@@ -2423,6 +3224,13 @@ Detect and investigate suspicious activities by users and roles in your cloud en #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2447,9 +3255,10 @@ _version_: 1 ### Suspicious GCP Storage Activities Use the searches in this Analytic Story to monitor your GCP Storage buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open storage buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-08-05
@@ -2457,6 +3266,11 @@ Use the searches in this Analytic Story to monitor your GCP Storage buckets for #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2481,9 +3295,10 @@ _version_: 1 ### Unusual AWS EC2 Modifications Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-04-09
@@ -2491,6 +3306,9 @@ Identify unusual changes to your AWS EC2 instances that may indicate malicious a #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2519,9 +3337,10 @@ _version_: 1 ### ColdRoot MacOS RAT Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2019-01-09
@@ -2529,6 +3348,11 @@ Leverage searches that allow you to detect and investigate unusual activities th #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2555,9 +3379,10 @@ _version_: 1 ### DHS Report TA18-074A Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-01-22
@@ -2565,6 +3390,35 @@ Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2587,9 +3441,10 @@ _version_: 2 ### Dynamic DNS Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and deny lists. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-09-06
@@ -2597,6 +3452,11 @@ Detect and investigate hosts in your environment that may be communicating with #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2625,9 +3485,10 @@ _version_: 2 ### Emotet Malware DHS Report TA18-201A Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-01-27
@@ -2635,6 +3496,25 @@ Detect rarely used executables, specific registry paths that may confer malware #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2661,9 +3541,10 @@ _version_: 1 ### Hidden Cobra Malware Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-01-22
@@ -2671,6 +3552,27 @@ Monitor for and investigate activities, including the creation or deletion of hi #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2695,9 +3597,10 @@ _version_: 2 ### Orangeworm Attack Group Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-01-22
@@ -2705,6 +3608,13 @@ Detect activities and various techniques associated with the Orangeworm Attack G #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2729,9 +3639,10 @@ _version_: 2 ### Ransomware Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-02-04
@@ -2739,6 +3650,47 @@ Leverage searches that allow you to detect and investigate unusual activities th #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2763,9 +3715,10 @@ _version_: 1 ### Ransomware Cloud Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware. These searches include cloud related objects that may be targeted by malicious actors via cloud providers own encryption features. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-10-27
@@ -2773,6 +3726,11 @@ Leverage searches that allow you to detect and investigate unusual activities th #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2799,9 +3757,10 @@ _version_: 1 ### Ryuk Ransomware Leverage searches that allow you to detect and investigate unusual activities that might relate to the Ryuk ransomware, including looking for file writes associated with Ryuk, Stopping Security Access Manager, DisableAntiSpyware registry key modification, suspicious psexec use, and more. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-11-06
@@ -2809,6 +3768,29 @@ Leverage searches that allow you to detect and investigate unusual activities th #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2835,9 +3817,10 @@ _version_: 1 ### SamSam Ransomware Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-12-13
@@ -2845,6 +3828,33 @@ Leverage searches that allow you to detect and investigate unusual activities th #### Detection Profile +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2871,9 +3881,10 @@ _version_: 1 ### Unusual Processes Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2020-02-04
@@ -2881,6 +3892,21 @@ Quickly identify systems running new or unusual processes in your environment th #### Detection Profile +* + +* + +* + +* + +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2907,9 +3933,10 @@ _version_: 2 ### Windows File Extension and Association Abuse Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-01-26
@@ -2917,6 +3944,13 @@ Detect and investigate suspected abuse of file extensions and Windows file assoc #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2941,9 +3975,10 @@ _version_: 1 ### Windows Service Abuse Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-11-02
@@ -2951,6 +3986,13 @@ Windows services are often used by attackers for persistence and the ability to #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -2981,9 +4023,10 @@ _version_: 3 ### Apache Struts Vulnerability Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-12-06
@@ -2991,6 +4034,13 @@ Detect and investigate activities--such as unusually long `Content-Type` length, #### Detection Profile +* + +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -3013,9 +4063,10 @@ _version_: 1 ### JBoss Vulnerability In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-14
@@ -3023,6 +4074,11 @@ In March of 2016, adversaries were seen using JexBoss--an open-source utility us #### Detection Profile +* + +* + + #### ATT&CK | ID | Technique | Tactic | @@ -3045,9 +4101,10 @@ _version_: 1 ### Spectre And Meltdown Vulnerabilities Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-01-08
@@ -3055,6 +4112,9 @@ Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploita #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -3077,9 +4137,10 @@ _version_: 1 ### Splunk Enterprise Vulnerability Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2017-09-19
@@ -3087,6 +4148,9 @@ Keeping your Splunk deployment up to date is critical and may help you reduce th #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | @@ -3111,9 +4175,10 @@ _version_: 1 ### Splunk Enterprise Vulnerability CVE-2018-11409 Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. + - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: +- **ATT&CK**: - **Last Updated**: 2018-06-14
@@ -3121,6 +4186,9 @@ Reduce the risk of CVE-2018-11409, an information disclosure vulnerability withi #### Detection Profile +* + + #### ATT&CK | ID | Technique | Tactic | From 4cf0e04e43c7b60ee4f42988e28536ef4b73d083 Mon Sep 17 00:00:00 2001 From: divious1 Date: Wed, 3 Mar 2021 15:45:09 -0500 Subject: [PATCH 13/62] working stories with enrichment --- bin/doc_gen.py | 17 +- bin/jinja2_templates/doc_stories_markdown.j2 | 13 +- docs/detections.md | 116 +- docs/detections.wiki | 8023 +++++++++++++++++- docs/stories.md | 2003 +++-- 5 files changed, 9235 insertions(+), 937 deletions(-) diff --git a/bin/doc_gen.py b/bin/doc_gen.py index 97c3d1969d..acb39d02dc 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -73,6 +73,7 @@ def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_de # enrich stories with information from detections: data_models, mitre_ids, kill_chain_phases sto_to_data_models = {} sto_to_mitre_attack_ids = {} + sto_to_mitre_attacks = {} sto_to_kill_chain_phases = {} sto_to_det = {} for detection in sorted_detections: @@ -104,17 +105,27 @@ def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_de else: sto_to_kill_chain_phases[story] = set(detection['tags']['kill_chain_phases']) + if 'mitre_attacks' in detection: + if story in sto_to_mitre_attacks.keys(): + for mitre_attack in detection['mitre_attacks']: + if mitre_attack not in sto_to_mitre_attacks[story]: + sto_to_mitre_attacks[story].append(mitre_attack) + else: + sto_to_mitre_attacks[story] = detection['mitre_attacks'] + + # add the enrich objects to the story for story in sorted_stories: - print(story) story['detections'] = sorted(sto_to_det[story['name']]) if story['name'] in sto_to_data_models: story['data_models'] = sorted(sto_to_data_models[story['name']]) if story['name'] in sto_to_mitre_attack_ids: story['mitre_attack_ids'] = sorted(sto_to_mitre_attack_ids[story['name']]) + if story['name'] in sto_to_mitre_attacks: + story['mitre_attacks'] = sto_to_mitre_attacks[story['name']] if story['name'] in sto_to_kill_chain_phases: story['kill_chain_phases'] = sorted(sto_to_kill_chain_phases[story['name']]) - #sort stories into categories + # sort stories into categories categories = [] category_names = set() for story in sorted_stories: @@ -134,7 +145,6 @@ def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_de j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), trim_blocks=False) - # write markdown template = j2_env.get_template('doc_stories_markdown.j2') output_path = path.join(OUTPUT_DIR + '/stories.md') @@ -143,6 +153,7 @@ def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_de f.write(output) messages.append("doc_gen.py wrote {0} stories documentation in markdown to: {1}".format(len(stories),output_path)) + def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE): types = ["endpoint", "application", "cloud", "network", "web", "experimental", "deprecated"] manifest_files = [] diff --git a/bin/jinja2_templates/doc_stories_markdown.j2 b/bin/jinja2_templates/doc_stories_markdown.j2 index 2c8ea192ae..4d17395558 100644 --- a/bin/jinja2_templates/doc_stories_markdown.j2 +++ b/bin/jinja2_templates/doc_stories_markdown.j2 @@ -11,10 +11,9 @@ All the Analytic Stories shipped to different Splunk products. Below is a breakd ### {{ story.name }} {{ story.description }} - - **Product**: {{ story.tags.product|join(', ') }} -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: {{ story.data_models|join(', ') }} +- **ATT&CK**: {% for mitre_attack_id in story.mitre_attack_ids %}[{{ mitre_attack_id }}](https://attack.mitre.org/techniques/{{ mitre_attack_id }}/){% if not loop.last %}, {% endif %}{% endfor %} - **Last Updated**: {{ story.date }}
@@ -22,17 +21,19 @@ All the Analytic Stories shipped to different Splunk products. Below is a breakd #### Detection Profile {% for detection in story.detections %} -* {{ detection.name }} +* [{{ detection }}](detections.md#{{ detection|lower|replace(" ", "-") }}) {% endfor %} #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +{%- for attack in story.mitre_attacks %} +| {{ attack.technique_id }} | {{ attack.technique }} | {{ attack.tactic|join(', ') }} | +{%- endfor %} #### Kill Chain Phase -{% for phase in story.tags.kill_chain_phases %} +{% for phase in story.kill_chain_phases %} * {{ phase }} {% endfor %} diff --git a/docs/detections.md b/docs/detections.md index 494ab90ce8..3bb8737e21 100644 --- a/docs/detections.md +++ b/docs/detections.md @@ -6179,64 +6179,6 @@ _version_: 3 ### Attempted Credential Dump From Registry via Reg exe Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: Endpoint -- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/) -- **Last Updated**: 2019-12-02 - -
- details - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `attempted_credential_dump_from_registry_via_reg_exe_filter` -``` -#### Associated Analytic Story - -* Credential Dumping - - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required field - - -#### ATT&CK - -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| -| T1003.002 | Security Account Manager | Credential Access | - -#### Kill Chain Phase - -* Actions on Objectives - - -#### Known False Positives -None identified. - -#### Reference - - -#### Test Dataset - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - - -_version_: 4 -
- ---- - -### Attempted Credential Dump From Registry via Reg exe -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - - **Product**: UEBA for Security Cloud - **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) @@ -6304,6 +6246,64 @@ _version_: 1 --- +### Attempted Credential Dump From Registry via Reg exe +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/) +- **Last Updated**: 2019-12-02 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` +``` +#### Associated Analytic Story + +* Credential Dumping + + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.002 | Security Account Manager | Credential Access | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +None identified. + +#### Reference + + +#### Test Dataset + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + + +_version_: 4 +
+ +--- + ### BCDEdit Failure Recovery Modification This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. diff --git a/docs/detections.wiki b/docs/detections.wiki index 78ffda0338..b095327c7a 100644 --- a/docs/detections.wiki +++ b/docs/detections.wiki @@ -6,6 +6,129 @@ All the detections shipped to different Splunk products. Below is a breakdown by ==Application== +===Detect New Login Attempts to Routers=== +The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': +* '''Last Updated''': 2017-09-12 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user +| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) +| where isOutlier=1 +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| `drop_dm_object_name("Authentication")` +| `detect_new_login_attempts_to_routers_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Router_and_Infrastructure_Security|Router and Infrastructure Security]] + + +====How To Implement==== +To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Legitimate router connections may appear as new connections + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Email Attachments With Lots Of Spaces=== +Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Email +* '''ATT&CK''': +* '''Last Updated''': 2017-09-19 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Email")` +| eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) +| search space_ratio >= 0.1 +| rex field=recipient_address "(?.*)@" +| `email_attachments_with_lots_of_spaces_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Emotet_Malware__DHS_Report_TA18-201A_|Emotet Malware DHS Report TA18-201A ]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Emails|Suspicious Emails]] + + +====How To Implement==== +You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ + **Splunk Phantom Playbook Integration**\ +If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +None at this time + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + ===Email files written outside of the Outlook directory=== The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. @@ -67,6 +190,135 @@ Administrators and users sometimes prefer backing up their email data by moving ---- +===Email servers sending high volume traffic to hosts=== +This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.002/ T1114.002] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d +| `drop_dm_object_name("All_Traffic")` +| eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out +| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip +| eval minimum_data_samples = 4, deviation_threshold = 3 +| where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") +| eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) +| table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average +| `email_servers_sending_high_volume_traffic_to_hosts_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Collection_and_Staging|Collection and Staging]] + + +====How To Implement==== +This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.002 +| Remote Email Collection +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Monitor Email For Brand Abuse=== +This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Email +* '''ATT&CK''': +* '''Last Updated''': 2018-01-05 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id +| `drop_dm_object_name("All_Email")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| eval temp=split(src_user, "@") +| eval email_domain=mvindex(temp, 1) +| lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse +| search domain_abuse=true +| table message_id, src_user, email_domain, recipients, firstTime, lastTime +| `monitor_email_for_brand_abuse_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Brand_Monitoring|Brand Monitoring]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Emails|Suspicious Emails]] + + +====How To Implement==== +You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +None at this time + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + ===Multiple Okta Users With Invalid Credentials From The Same IP=== This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. @@ -127,6 +379,66 @@ A single public IP address servicing multiple legitmate users may trigger this s ---- +===No Windows Updates in a time frame=== +This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Updates +* '''ATT&CK''': +* '''Last Updated''': 2017-09-15 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product +| rename Updates.dest as Host +| rename Updates.status as "Update Status" +| rename Updates.vendor_product as Product +| eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) +| `security_content_ctime(lastTime)` +| search isOutlier=1 +| rename lastTime as "Last Update Time", +| table Host, "Update Status", Product, "Last Update Time" +| `no_windows_updates_in_a_time_frame_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Monitor_for_Updates|Monitor for Updates]] + + +====How To Implement==== +To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + ===Okta Account Lockout Events=== Detect Okta user lockout events @@ -301,6 +613,313 @@ Users in your enviornment may legitmately be travelling and loggin in from diffe ---- +===Phishing Email Detection by Machine Learning Method - SSA=== +Malicious mails can conduct phishing that induces readers to open attachment, click links or trigger third party service. This detect uses Natural Language Processing (NLP) approach to analyze an email message's content (Sender, Subject and Body) and judge whether it is a phishing email. The detection adopts a deep learning (neural network) model that employs character level embeddings plus LSTM layers to perform classification. The model is pre-trained and then published as ONNX format. Current sample model is trained using the dataset published at https://github.com/splunk/attack_data/tree/master/datasets/T1566_Phishing_Email/splunk_train.json User are expected to re-train the model by combining with their own training data for better accuracy using the provided model file (SMLE notebook). DSP pipeline then processes the email message and passes it as an event to Apply ML Models function, which returns the probability of a phishing email. Current implementation assumes the email is fed to DSP in JSON format contains at least email's sender, subject and its message body, including reply content, if any. + +* '''Product''': UEBA for Security Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1566/ T1566] +* '''Last Updated''': 2020-08-25 + +
+
+ +====Search==== + +| from read_ssa_enriched_events() +| eval eventLine=concat(ucast(map_get(input_event, "From"), "string", " "), " ", ucast(map_get(input_event, "Subject"), "string", " "), " ", ucast(map_get(input_event, "Content"), "string", " "), " "), _time=map_get(input_event, "_time") +| where eventLine IS NOT NULL +| eval mapC={" ": 32, "!": 33, "\"": 34, "#": 35, "$": 36, "%": 37, "&": 38, "`": 39, "(": 40, ")": 41, "*": 42, "+": 43, ",": 44, "-": 45, ".": 46, "/": 47, "0": 48, "1": 49, "2": 50, "3": 51, "4": 52, "5": 53, "6": 54, "7": 55, "8": 56, "9": 57, ":": 58, ";": 59, "<": 60, "=": 61, ">": 62, "?": 63, "@": 64, "A": 65, "B": 66, "C": 67, "D": 68, "E": 69, "F": 70, "G": 71, "H": 72, "I": 73, "J": 74, "K": 75, "L": 76, "M": 77, "N": 78, "O": 79, "P": 80, "Q": 81, "R": 82, "S": 83, "T": 84, "U": 85, "V": 86, "W": 87, "X": 88, "Y": 89, "Z": 90, "[": 91, "\\": 92, "]": 93, "^": 94, "_": 95, "`": 96, "a": 97, "b": 98, "c": 99, "d": 100, "e": 101, "f": 102, "g": 103, "h": 104, "i": 105, "j": 106, "k": 107, "l": 108, "m": 109, "n": 110, "o": 111, "p": 112, "q": 113, "r": 114, "s": 115, "t": 116, "u": 117, "v": 118, "w": 119, "x": 120, "y": 121, "z": 122, "{": 123, " +|": 124, "}": 125, "~": 126}, ml_in = for_each(iterator(mvrange(1,129), "i"), cast(map_get(mapC, substr(eventLine, i, 1)), "float") ) +| apply_model connection_id="YOUR_S3_ONNX_CONNECTOR_ID" name="phishing_email_v8" path="s3://smle-experiments/models/phishing_email" +| eval probability = mvindex(ml_out, 0) +| where probability > 0.5 +| eval start_time=_time, end_time=_time, entities="TBD", body="TBD" +| select probability, body, entities, start_time, end_time +| into write_ssa_detected_events(); + +====Associated Analytic Story==== + + +====How To Implement==== +Events are fed to DSP contains at least email's sender, subject and its message body. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566 +| Phishing +| Initial Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Because of imbalance of anomaly data in training, the model will less likely report false positive. Instead, the model is more prone to false negative. Current best recall score is ~85% + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Spectre and Meltdown Vulnerable Systems=== +The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Vulnerabilities +* '''ATT&CK''': +* '''Last Updated''': 2017-01-07 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest +| `drop_dm_object_name(Vulnerabilities)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `spectre_and_meltdown_vulnerable_systems_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Spectre_And_Meltdown_Vulnerabilities|Spectre And Meltdown Vulnerabilities]] + + +====How To Implement==== +The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It is possible that your vulnerability scanner is not detecting that the patches have been applied. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Suspicious Email - UBA Anomaly=== +This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': UEBA +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1566/ T1566] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model +| `drop_dm_object_name(All_UEBA_Events)` +| `drop_dm_object_name(UEBA_Anomalies)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_email___uba_anomaly_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Emails|Suspicious Emails]] + + +====How To Implement==== +You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566 +| Phishing +| Initial Access +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and add the URL to an allow list if you determine that it is a legitimate sender. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Suspicious Email Attachment Extensions=== +This search looks for emails that have attachments with suspicious file extensions. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Email +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1566.001/ T1566.001] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Email")` +| `suspicious_email_attachments` +| `suspicious_email_attachment_extensions_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Emotet_Malware__DHS_Report_TA18-201A_|Emotet Malware DHS Report TA18-201A ]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Emails|Suspicious Emails]] + + +====How To Implement==== +You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ + **Splunk Phantom Playbook Integration**\ +If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566.001 +| Spearphishing Attachment +| Initial Access +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Suspicious Java Classes=== +This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2018-12-06 + +
+
+ +====Search==== +`stream_http` http_method=POST http_content_length>1 +| regex form_data="(?i)java\.lang\.(?:runtime +|processbuilder)" +| rename src_ip as src +| stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_java_classes_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Apache_Struts_Vulnerability|Apache Struts Vulnerability]] + + +====How To Implement==== +In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +There are no known false positives. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + ===Web Servers Executing Suspicious Processes=== This search looks for suspicious processes on all systems labeled as web servers. @@ -566,6 +1185,61 @@ bucket with S3 encryption * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json +''version'': 1 + + + +---- + +===AWS EKS Kubernetes cluster sensitive object access=== +This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-06-23 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 +|table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason +|dedup user.username user.groups{} +|`aws_eks_kubernetes_cluster_sensitive_object_access_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Object_Access_Activity|Kubernetes Sensitive Object Access Activity]] + + +====How To Implement==== +You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. This search works with cloudwatch logs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. + +====Reference==== + + +====Test Dataset==== + + ''version'': 1
@@ -1123,6 +1797,128 @@ You must be ingesting your cloud infrastructure logs. You also must run the base * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json +''version'': 1 + + + +---- + +===Amazon EKS Kubernetes Pod scan detection=== +This search provides detection information on unauthenticated requests against Kubernetes' Pods API + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1526/ T1526] +* '''Last Updated''': 2020-04-15 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" +| rename source as cluster_name sourceIPs{} as src_ip +| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `amazon_eks_kubernetes_pod_scan_detection_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Scanning_Activity|Kubernetes Scanning Activity]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Reconnaissance + + +====Known False Positives==== +Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Amazon EKS Kubernetes cluster scan detection=== +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1526/ T1526] +* '''Last Updated''': 2020-04-15 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" +| rename sourceIPs{} as src_ip +| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +|`amazon_eks_kubernetes_cluster_scan_detection_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Scanning_Activity|Kubernetes Scanning Activity]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Reconnaissance + + +====Known False Positives==== +Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. + +====Reference==== + + +====Test Dataset==== + + ''version'': 1
@@ -2693,6 +3489,318 @@ The false-positive rate may vary based on the values of`dataPointThreshold` and ====Test Dataset==== +''version'': 1 + + + +---- + +===GCP Detect accounts with high risk roles by project=== +This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-10-09 + +
+
+ +====Search==== +`google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}.role=roles/owner OR roles/editor OR roles/iam.serviceAccountUser OR roles/iam.serviceAccountAdmin OR roles/iam.serviceAccountTokenCreator OR roles/dataflow.developer OR roles/dataflow.admin OR roles/composer.admin OR roles/dataproc.admin OR roles/dataproc.editor +| table data.resource.type data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.authorizationInfo{}.resource data.protoPayload.response.bindings{}.role data.protoPayload.response.bindings{}.members{} +| `gcp_detect_accounts_with_high_risk_roles_by_project_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#GCP_Cross_Account_Activity|GCP Cross Account Activity]] + + +====How To Implement==== +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Accounts with high risk roles should be reduced to the minimum number needed, however specific tasks and setups may be simply expected behavior within organization + +====Reference==== + +* https://github.com/dxa4481/gcploit + +* https://www.youtube.com/watch?v=Ml09R38jpok + +* https://cloud.google.com/iam/docs/understanding-roles + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===GCP Detect gcploit framework=== +This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-10-08 + +
+
+ +====Search==== +`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s +| table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent +| `gcp_detect_gcploit_framework_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#GCP_Cross_Account_Activity|GCP Cross Account Activity]] + + +====How To Implement==== +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects + +====Reference==== + +* https://github.com/dxa4481/gcploit + +* https://www.youtube.com/watch?v=Ml09R38jpok + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===GCP Detect high risk permissions by resource and account=== +This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-10-09 + +
+
+ +====Search==== +`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.permission=iam.serviceAccounts.getaccesstoken OR iam.serviceAccounts.setIamPolicy OR iam.serviceAccounts.actas OR dataflow.jobs.create OR composer.environments.create OR dataproc.clusters.create +|table data.protoPayload.requestMetadata.callerIp data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.response.bindings{}.members{} data.resource.labels.project_id +| `gcp_detect_high_risk_permissions_by_resource_and_account_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#GCP_Cross_Account_Activity|GCP Cross Account Activity]] + + +====How To Implement==== +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +High risk permissions are part of any GCP environment, however it is important to track resource and accounts usage, this search may produce false positives. + +====Reference==== + +* https://github.com/dxa4481/gcploit + +* https://www.youtube.com/watch?v=Ml09R38jpok + +* https://cloud.google.com/iam/docs/permissions-reference + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===GCP Kubernetes cluster pod scan detection=== +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1526/ T1526] +* '''Last Updated''': 2020-07-17 + +
+
+ +====Search==== +`google_gcp_pubsub_message` category=kube-audit +|spath input=properties.log +|search responseStatus.code=401 +|table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod +| `gcp_kubernetes_cluster_pod_scan_detection_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Scanning_Activity|Kubernetes Scanning Activity]] + + +====How To Implement==== +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Reconnaissance + + +====Known False Positives==== +Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===GCP Kubernetes cluster scan detection=== +This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1526/ T1526] +* '''Last Updated''': 2020-04-15 + +
+
+ +====Search==== +`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" +| rename data.protoPayload.requestMetadata.callerIp as src_ip +| stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name +| rename data.resource.labels.cluster_name as cluster_name +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `gcp_kubernetes_cluster_scan_detection_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Scanning_Activity|Kubernetes Scanning Activity]] + + +====How To Implement==== +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Reconnaissance + + +====Known False Positives==== +Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. + +====Reference==== + + +====Test Dataset==== + + ''version'': 1
@@ -2752,6 +3860,1070 @@ unknown ====Test Dataset==== +''version'': 1 + + + +---- + +===Kubernetes AWS detect RBAC authorization by account=== +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-06-23 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* +| table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason +| stats count by user.username annotations.authorization.k8s.io/reason +| rare user.username annotations.authorization.k8s.io/reason +|`kubernetes_aws_detect_rbac_authorization_by_account_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Role_Activity|Kubernetes Sensitive Role Activity]] + + +====How To Implement==== +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes AWS detect most active service accounts by pod=== +This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-06-23 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts objectRef.resource=pods +| table sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision +| top sourceIPs{} user.username verb annotations.authorization.k8s.io/decision +|`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Role_Activity|Kubernetes Sensitive Role Activity]] + + +====How To Implement==== +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes AWS detect sensitive role access=== +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-06-23 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 +| table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason +| dedup user.username user.groups{} +|`kubernetes_aws_detect_sensitive_role_access_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Role_Activity|Kubernetes Sensitive Role Activity]] + + +====How To Implement==== +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes AWS detect service accounts forbidden failure access=== +This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-06-23 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure +| table sourceIPs{} user.username userAgent verb responseStatus.status requestURI +| `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Object_Access_Activity|Kubernetes Sensitive Object Access Activity]] + + +====How To Implement==== +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes AWS detect suspicious kubectl calls=== +This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-06-23 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous +| table src_ip src_user verb userAgent requestURI +| stats count by src_ip src_user verb userAgent requestURI +|`kubernetes_aws_detect_suspicious_kubectl_calls_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Object_Access_Activity|Kubernetes Sensitive Object Access Activity]] + + +====How To Implement==== +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes Azure detect RBAC authorization by account=== +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-05-26 + +
+
+ +====Search==== +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search annotations.authorization.k8s.io/reason=* +| table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason +|stats count by user.username annotations.authorization.k8s.io/reason +| rare user.username annotations.authorization.k8s.io/reason +|`kubernetes_azure_detect_rbac_authorization_by_account_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Role_Activity|Kubernetes Sensitive Role Activity]] + + +====How To Implement==== +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes Azure detect most active service accounts by pod namespace=== +This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-05-26 + +
+
+ +====Search==== +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow +| table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace +| top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace +|`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Role_Activity|Kubernetes Sensitive Role Activity]] + + +====How To Implement==== +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes Azure detect sensitive object access=== +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-05-20 + +
+
+ +====Search==== +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow +|table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason +|dedup user.username user.groups{} +|`kubernetes_azure_detect_sensitive_object_access_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Object_Access_Activity|Kubernetes Sensitive Object Access Activity]] + + +====How To Implement==== +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes Azure detect sensitive role access=== +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-05-20 + +
+
+ +====Search==== +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search objectRef.resource=clusterroles OR clusterrolebindings +| table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason +| dedup user.username user.groups{} +|`kubernetes_azure_detect_sensitive_role_access_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Role_Activity|Kubernetes Sensitive Role Activity]] + + +====How To Implement==== +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes Azure detect service accounts forbidden failure access=== +This search provides information on Kubernetes service accounts with failure or forbidden access status + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-05-20 + +
+
+ +====Search==== +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden +| table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace +|`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Object_Access_Activity|Kubernetes Sensitive Object Access Activity]] + + +====How To Implement==== +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes Azure detect suspicious kubectl calls=== +This search provides information on rare Kubectl calls with IP, verb namespace and object access context + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-05-26 + +
+
+ +====Search==== +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration +| search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 +| table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI +| rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI +|`kubernetes_azure_detect_suspicious_kubectl_calls_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Object_Access_Activity|Kubernetes Sensitive Object Access Activity]] + + +====How To Implement==== +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes Azure pod scan fingerprint=== +This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-05-20 + +
+
+ +====Search==== +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search responseStatus.code=401 +| table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod +|`kubernetes_azure_pod_scan_fingerprint_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Scanning_Activity|Kubernetes Scanning Activity]] + + +====How To Implement==== +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Reconnaissance + + +====Known False Positives==== +Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes Azure scan fingerprint=== +This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1526/ T1526] +* '''Last Updated''': 2020-05-19 + +
+
+ +====Search==== +`kubernetes_azure` category=kube-audit +| spath input=properties.log +| search responseStatus.code=401 +| table sourceIPs{} userAgent verb requestURI responseStatus.reason +|`kubernetes_azure_scan_fingerprint_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Scanning_Activity|Kubernetes Scanning Activity]] + + +====How To Implement==== +You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Reconnaissance + + +====Known False Positives==== +Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes GCP detect RBAC authorizations by account=== +This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-07-11 + +
+
+ +====Search==== +`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole +| table src_ip src_user data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason +| rare src_user data.labels.authorization.k8s.io/reason +|`kubernetes_gcp_detect_rbac_authorizations_by_account_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Role_Activity|Kubernetes Sensitive Role Activity]] + + +====How To Implement==== +You must install splunk AWS add on for GCP. This search works with pubsub messaging service logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes GCP detect most active service accounts by pod=== +This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-07-10 + +
+
+ +====Search==== +`google_gcp_pubsub_message` data.protoPayload.request.spec.group{}=system:serviceaccounts +| table src_ip src_user http_user_agent data.protoPayload.request.spec.nonResourceAttributes.verb data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource +| top src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource +|`kubernetes_gcp_detect_most_active_service_accounts_by_pod_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Role_Activity|Kubernetes Sensitive Role Activity]] + + +====How To Implement==== +You must install splunk GCP add on. This search works with pubsub messaging service logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes GCP detect sensitive object access=== +This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-07-11 + +
+
+ +====Search==== +`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.resource=configmaps OR secrets +| table data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name data.protoPayload.request.metadata.namespace data.labels.authorization.k8s.io/decision +| dedup data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name +|`kubernetes_gcp_detect_sensitive_object_access_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Object_Access_Activity|Kubernetes Sensitive Object Access Activity]] + + +====How To Implement==== +You must install splunk add on for GCP . This search works with pubsub messaging service logs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes GCP detect sensitive role access=== +This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-07-11 + +
+
+ +====Search==== +`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole dest=apis/rbac.authorization.k8s.io/v1 src_ip!=::1 +| table src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason +| dedup src_ip src_user +|`kubernetes_gcp_detect_sensitive_role_access_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Role_Activity|Kubernetes Sensitive Role Activity]] + + +====How To Implement==== +You must install splunk add on for GCP. This search works with pubsub messaging servicelogs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Sensitive role resource access is necessary for cluster operation, however source IP, user agent, decision and reason may indicate possible malicious use. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes GCP detect service accounts forbidden failure access=== +This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-06-23 + +
+
+ +====Search==== +`google_gcp_pubsub_message` system:serviceaccounts data.protoPayload.response.status.allowed!=* +| table src_ip src_user http_user_agent data.protoPayload.response.spec.resourceAttributes.namespace data.resource.labels.cluster_name data.protoPayload.response.spec.resourceAttributes.verb data.protoPayload.request.status.allowed data.protoPayload.response.status.reason data.labels.authorization.k8s.io/decision +| dedup src_ip src_user +| `kubernetes_gcp_detect_service_accounts_forbidden_failure_access_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Object_Access_Activity|Kubernetes Sensitive Object Access Activity]] + + +====How To Implement==== +You must install splunk add on for GCP. This search works with pubsub messaging service logs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +This search can give false positives as there might be inherent issues with authentications and permissions at cluster. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Kubernetes GCP detect suspicious kubectl calls=== +This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-07-11 + +
+
+ +====Search==== +`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous +| table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path +|dedup src_ip src_user +|`kubernetes_gcp_detect_suspicious_kubectl_calls_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Kubernetes_Sensitive_Object_Access_Activity|Kubernetes Sensitive Object Access Activity]] + + +====How To Implement==== +You must install splunk add on for GCP. This search works with pubsub messaging logs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets + +====Reference==== + + +====Test Dataset==== + + ''version'': 1
@@ -3551,6 +5723,3946 @@ unknown * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json +''version'': 1 + + + +---- + +===aws detect attach to role policy=== +This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-07-27 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` attach policy +| spath requestParameters.policyArn +| table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate +| `aws_detect_attach_to_role_policy_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Cross_Account_Activity|AWS Cross Account Activity]] + + +====How To Implement==== +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===aws detect permanent key creation=== +This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-07-27 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` CreateAccessKey +| spath eventName +| search eventName=CreateAccessKey "userIdentity.type"=IAMUser +| table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId +|`aws_detect_permanent_key_creation_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Cross_Account_Activity|AWS Cross Account Activity]] + + +====How To Implement==== +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===aws detect role creation=== +This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-07-27 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* +| table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate +| `aws_detect_role_creation_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Cross_Account_Activity|AWS Cross Account Activity]] + + +====How To Implement==== +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===aws detect sts assume role abuse=== +This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-07-27 + +
+
+ +====Search==== +`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role +| table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate +| `aws_detect_sts_assume_role_abuse_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Cross_Account_Activity|AWS Cross Account Activity]] + + +====How To Implement==== +You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===aws detect sts get session token abuse=== +This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1550/ T1550] +* '''Last Updated''': 2020-07-27 + +
+
+ +====Search==== +`aws_cloudwatchlogs_eks` ASIA userIdentity.type=IAMUser +| spath eventName +| search eventName=GetSessionToken +| table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region +| `aws_detect_sts_get_session_token_abuse_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Cross_Account_Activity|AWS Cross Account Activity]] + + +====How To Implement==== +You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1550 +| Use Alternate Authentication Material +| Defense Evasion, Lateral Movement +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===gcp detect oauth token abuse=== +This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-09-01 + +
+
+ +====Search==== +`google_gcp_pubsub_message` type.googleapis.com/google.cloud.audit.AuditLog +|table protoPayload.@type protoPayload.status.details{}.@type protoPayload.status.details{}.violations{}.callerIp protoPayload.status.details{}.violations{}.type protoPayload.status.message +| `gcp_detect_oauth_token_abuse_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#GCP_Cross_Account_Activity|GCP Cross Account Activity]] + + +====How To Implement==== +You must install splunk GCP add-on. This search works with gcp:pubsub:message logs + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Known False Positives==== +GCP Oauth token abuse detection will only work if there are access policies in place along with audit logs. + +====Reference==== + +* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-1 + +* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-2 + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + + + +==Deprecated== + + +===AWS Cloud Provisioning From Previously Unseen City=== +This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2018-03-16 + +
+
+ +====Search==== +`cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search City=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by City +| eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newCity=1 +| table City] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, City, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_city_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Suspicious_Provisioning_Activities|AWS Suspicious Provisioning Activities]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===AWS Cloud Provisioning From Previously Unseen Country=== +This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2018-03-16 + +
+
+ +====Search==== +`cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Country=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by Country +| eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newCountry=1 +| table Country] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, Country, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_country_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Suspicious_Provisioning_Activities|AWS Suspicious Provisioning Activities]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===AWS Cloud Provisioning From Previously Unseen IP Address=== +This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2018-03-16 + +
+
+ +====Search==== +`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Country=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress +| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newIP=1 +| table sourceIPAddress] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_ip_address_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Suspicious_Provisioning_Activities|AWS Suspicious Provisioning Activities]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===AWS Cloud Provisioning From Previously Unseen Region=== +This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2018-03-16 + +
+
+ +====Search==== +`cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) +| iplocation sourceIPAddress +| search Region=* +| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country +| inputlookup append=t previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country +| outputlookup previously_seen_provisioning_activity_src.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by Region +| eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newRegion=1 +| table Region] +| spath output=user userIdentity.arn +| rename sourceIPAddress as src_ip +| table _time, user, src_ip, Region, eventName, errorCode +| `aws_cloud_provisioning_from_previously_unseen_region_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Suspicious_Provisioning_Activities|AWS Suspicious Provisioning Activities]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ + This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Abnormally High AWS Instances Launched by User=== +This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`cloudtrail` eventName=RunInstances errorCode=success +| bucket span=10m _time +| stats count AS instances_launched by _time userName +| eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev +| eval threshold_value = 4 +| eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) +| search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") +| eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) +| table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev +| `abnormally_high_aws_instances_launched_by_user_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Cryptomining|AWS Cryptomining]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_EC2_Activities|Suspicious AWS EC2 Activities]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Abnormally High AWS Instances Launched by User - MLTK=== +This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` +| bucket span=10m _time +| stats count as instances_launched by _time src_user +| apply ec2_excessive_runinstances_v1 +| rename "IsOutlier(instances_launched)" as isOutlier +| where isOutlier=1 + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Cryptomining|AWS Cryptomining]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_EC2_Activities|Suspicious AWS EC2 Activities]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Abnormally High AWS Instances Terminated by User=== +This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`cloudtrail` eventName=TerminateInstances errorCode=success +| bucket span=10m _time +| stats count AS instances_terminated by _time userName +| eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev +| eval threshold_value = 4 +| eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) +| search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") +| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) +|table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev +| `abnormally_high_aws_instances_terminated_by_user_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_EC2_Activities|Suspicious AWS EC2 Activities]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Abnormally High AWS Instances Terminated by User - MLTK=== +This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` +| bucket span=10m _time +| stats count as instances_terminated by _time src_user +| apply ec2_excessive_terminateinstances_v1 +| rename "IsOutlier(instances_terminated)" as isOutlier +| where isOutlier=1 + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_EC2_Activities|Suspicious AWS EC2 Activities]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Clients Connecting to Multiple DNS Servers=== +This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src +| `drop_dm_object_name("Network_Resolution")` +|where dest_count > 5 +| `clients_connecting_to_multiple_dns_servers_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#DNS_Hijacking|DNS Hijacking]] + +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_DNS_Traffic|Suspicious DNS Traffic]] + +* [[Documentation:ESSOC:stories:UseCase#Host_Redirection|Host Redirection]] + + +====How To Implement==== +This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ +This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Cloud Network Access Control List Deleted=== +Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-09-08 + +
+
+ +====Search==== +`cloudtrail` eventName=DeleteNetworkAcl +|rename userIdentity.arn as arn +| stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `cloud_network_access_control_list_deleted_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Cloud_Network_ACL_Activity|Cloud Network ACL Activity]] + + +====How To Implement==== +You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the `cloud_network_access_control_list_deleted_filter` macro. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It's possible that a user has legitimately deleted a network ACL. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===DNS Query Requests Resolved by Unauthorized DNS Servers=== +This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.004/ T1071.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest +| `drop_dm_object_name("DNS")` +| `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#DNS_Hijacking|DNS Hijacking]] + +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_DNS_Traffic|Suspicious DNS Traffic]] + +* [[Documentation:ESSOC:stories:UseCase#Host_Redirection|Host Redirection]] + + +====How To Implement==== +To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.004 +| DNS +| Command and Control +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Detect API activity from users without MFA=== +This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2018-05-17 + +
+
+ +====Search==== +`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false +| search NOT [ +| inputlookup aws_service_accounts +| fields identity +| rename identity as user] +| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_api_activity_from_users_without_mfa_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_User_Monitoring|AWS User Monitoring]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ +This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** AWS User ARN, **Field:** userIdentity.arn\ +1. \ +1. **Label:** AWS User Type, **Field:** userIdentity.type\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect AWS API Activities From Unapproved Accounts=== +This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`cloudtrail` errorCode=success +| rename userName as identity +| search NOT [ +| inputlookup identity_lookup_expanded +| fields identity] +| search NOT [ +| inputlookup aws_service_accounts +| fields identity] +| rename identity as user +| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_aws_api_activities_from_unapproved_accounts_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_User_Monitoring|AWS User Monitoring]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ +This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** First Time, **Field:** firstTime\ +1. \ +1. **Label:** Last Time, **Field:** lastTime\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Detect DNS requests to Phishing Sites leveraging EvilGinx2=== +This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1566.003/ T1566.003] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host +| `drop_dm_object_name(DNS)` +| rex field=query ".*?(?[^./:]+\.(\S{2,3} +|\S{2,3}.\S{2,3}))$" +| stats count values(query) as query by domain dest src answer +| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` +| search NOT [ inputlookup legit_domains.csv +| fields domain] +| join domain type=outer [ +| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site +| rename "Web.*" as * +| rex field=site ".*?(?[^./:]+\.(\S{2,3} +|\S{2,3}.\S{2,3}))$" +| table dest domain url] +| table count src dest query answer domain url +| `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Common_Phishing_Frameworks|Common Phishing Frameworks]] + + +====How To Implement==== +You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\ + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566.003 +| Spearphishing via Service +| Initial Access +|} + +====Kill Chain Phase==== + +* Delivery + +* Command and Control + + +====Known False Positives==== +If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Detect Long DNS TXT Record Response=== +This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type +| `drop_dm_object_name("DNS")` +| eval anslen=len(answer) +| search anslen>100 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count +| table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" +| `detect_long_dns_txt_record_response_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_DNS_Traffic|Suspicious DNS Traffic]] + +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] + + +====How To Implement==== +To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Detect Mimikatz Using Loaded Images=== +This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] +* '''Last Updated''': 2019-12-03 + +
+
+ +====Search==== +`sysmon` EventCode=7 +| stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image +| search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mimikatz_using_loaded_images_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] + +* [[Documentation:ESSOC:stories:UseCase#Detect_Zerologon_Attack|Detect Zerologon Attack]] + +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] + + +====How To Implement==== +This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Other tools can import the same DLLs. These tools should be part of a whitelist. + +====Reference==== + +* https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Mimikatz Via PowerShell And EventCode 4703=== +This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] +* '''Last Updated''': 2019-02-27 + +
+
+ +====Search==== +`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe +| rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" +| where privs="SeDebugPrivilege" +| stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message +| rename privs as "Enabled Privilege" +| rename Process_Name as process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_mimikatz_via_powershell_and_eventcode_4703_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] + + +====How To Implement==== +You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Detect Spike in AWS API Activity=== +This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup api_call_by_user_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup api_call_by_user_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_aws_api_activity_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_User_Monitoring|AWS User Monitoring]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ +This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +1. \ +1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\ +1. \ +1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== + + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Detect Spike in Network ACL Activity=== +This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] +* '''Last Updated''': 2018-05-21 + +
+
+ +====Search==== +`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup network_acl_activity_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup network_acl_activity_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_network_acl_activity_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Network_ACL_Activity|AWS Network ACL Activity]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Spike in Security Group Activity=== +This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2018-04-18 + +
+
+ +====Search==== +`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` +| spath output=arn path=userIdentity.arn +| stats count as apiCalls by arn +| inputlookup security_group_activity_baseline append=t +| fields - latestCount +| stats values(*) as * by arn +| rename apiCalls as latestCount +| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 +| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) +| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) +| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls +| outputlookup security_group_activity_baseline +| eval dataPointThreshold = 15, deviationThreshold = 3 +| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) +| where isSpike=1 +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=user userIdentity.arn +| stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +| `detect_spike_in_security_group_activity_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_User_Monitoring|AWS User Monitoring]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect USB device insertion=== +The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change_Analysis +* '''ATT&CK''': +* '''Last Updated''': 2017-11-27 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest +| `drop_dm_object_name("All_Changes")` +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| `detect_usb_device_insertion_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Data_Protection|Data Protection]] + + +====How To Implement==== +To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Installation + +* Actions on Objectives + + +====Known False Positives==== +Legitimate USB activity will also be detected. Please verify and investigate as appropriate. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect new API calls from user roles=== +This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2018-04-16 + +
+
+ +====Search==== +`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole +| stats earliest(_time) as earliest latest(_time) as latest by userName eventName +| inputlookup append=t previously_seen_api_calls_from_user_roles +| stats min(earliest) as earliest, max(latest) as latest by userName eventName +| outputlookup previously_seen_api_calls_from_user_roles +| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) +| where newApiCallfromUserRole=1 +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| table eventName userName] +|rename userName as user +| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| `detect_new_api_calls_from_user_roles_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_User_Monitoring|AWS User Monitoring]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect new user AWS Console Login=== +This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`cloudtrail` eventName=ConsoleLogin +| rename userIdentity.arn as user +| stats earliest(_time) as firstTime latest(_time) as lastTime by user +| inputlookup append=t previously_seen_users_console_logins_cloudtrail +| stats min(firstTime) as firstTime max(lastTime) as lastTime by user +| eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| where userStatus ="First Time Logging into AWS Console" +| `detect_new_user_aws_console_login_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_Login_Activities|Suspicious AWS Login Activities]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Detect web traffic to dynamic domain providers=== +This search looks for web connections to dynamic DNS providers. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Web +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.001/ T1071.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `dynamic_dns_web_traffic` +| `detect_web_traffic_to_dynamic_domain_providers_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Dynamic_DNS|Dynamic DNS]] + + +====How To Implement==== +This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ +This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ +Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` Deprecated because duplicate. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.001 +| Web Protocols +| Command and Control +|} + +====Kill Chain Phase==== + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Detection of DNS Tunnels=== +This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003] +* '''Last Updated''': 2017-09-18 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" +| rename "DNS.src" as src "DNS.query" as message +| eval length=len(message) +| stats sum(length) as length by src +| append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" +| rename "DNS.src" as src "DNS.answer" as message +| eval message=if(message=="unknown","", message) +| eval length=len(message) +| stats sum(length) as length by src ] +| stats sum(length) as length by src +| where length > 10000 +| `detection_of_dns_tunnels_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Data_Protection|Data Protection]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_DNS_Traffic|Suspicious DNS Traffic]] + +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] + + +====How To Implement==== +To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|} + +====Kill Chain Phase==== + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===EC2 Instance Modified With Previously Unseen User=== +This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success +| stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn +| rename userIdentity.arn as arn +| inputlookup append=t previously_seen_ec2_modifications_by_user +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn +| outputlookup previously_seen_ec2_modifications_by_user +| eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newUser=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename arn as userIdentity.arn +| table userIdentity.arn] +| spath output=dest responseElements.instancesSet.items{}.instanceId +| spath output=user userIdentity.arn +| table _time, user, dest +| `ec2_instance_modified_with_previously_unseen_user_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Unusual_AWS_EC2_Modifications|Unusual AWS EC2 Modifications]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===EC2 Instance Started In Previously Unseen Region=== +This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2018-02-23 + +
+
+ +====Search==== +`cloudtrail` earliest=-1h StartInstances +| stats earliest(_time) as earliest latest(_time) as latest by awsRegion +| inputlookup append=t previously_seen_aws_regions.csv +| stats min(earliest) as earliest max(latest) as latest by awsRegion +| outputlookup previously_seen_aws_regions.csv +| eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| where regionStatus="Instance Started in a New Region" +| `ec2_instance_started_in_previously_unseen_region_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Cryptomining|AWS Cryptomining]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_EC2_Activities|Suspicious AWS EC2 Activities]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===EC2 Instance Started With Previously Unseen AMI=== +This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2018-03-12 + +
+
+ +====Search==== +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success +| stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId +| rename requestParameters.instancesSet.items{}.imageId as amiID +| inputlookup append=t previously_seen_ec2_amis.csv +| stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID +| outputlookup previously_seen_ec2_amis.csv +| eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| where newAMI=1 +| rename amiID as requestParameters.instancesSet.items{}.imageId +| table requestParameters.instancesSet.items{}.imageId] +| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID +| table firstTime, lastTime, arn, amiID, dest, instanceType +| `ec2_instance_started_with_previously_unseen_ami_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Cryptomining|AWS Cryptomining]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===EC2 Instance Started With Previously Unseen Instance Type=== +This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-02-07 + +
+
+ +====Search==== +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success +| fillnull value="m1.small" requestParameters.instanceType +| stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType +| rename requestParameters.instanceType as instanceType +| inputlookup append=t previously_seen_ec2_instance_types.csv +| stats min(earliest) as earliest max(latest) as latest by instanceType +| outputlookup previously_seen_ec2_instance_types.csv +| eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) +| `security_content_ctime(earliest)` +| `security_content_ctime(latest)` +| where newType=1 +| rename instanceType as requestParameters.instanceType +| table requestParameters.instanceType] +| spath output=user userIdentity.arn +| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest +| table _time, user, dest, instanceType +| `ec2_instance_started_with_previously_unseen_instance_type_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Cryptomining|AWS Cryptomining]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===EC2 Instance Started With Previously Unseen User=== +This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success +| stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn +| rename userIdentity.arn as arn +| inputlookup append=t previously_seen_ec2_launches_by_user.csv +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn +| outputlookup previously_seen_ec2_launches_by_user.csv +| eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newUser=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename arn as userIdentity.arn +| table userIdentity.arn] +| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user +| table _time, user, dest, instanceType +| `ec2_instance_started_with_previously_unseen_user_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#AWS_Cryptomining|AWS Cryptomining]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_AWS_EC2_Activities|Suspicious AWS EC2 Activities]] + + +====How To Implement==== +You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Execution of File With Spaces Before Extension=== +This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036.003/ T1036.003] +* '''Last Updated''': 2020-11-19 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `execution_of_file_with_spaces_before_extension_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Windows_File_Extension_and_Association_Abuse|Windows File Extension and Association Abuse]] + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Extended Period Without Successful Netbackup Backups=== +This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2017-09-12 + +
+
+ +====Search==== +`netbackup` MESSAGE="Disk/Partition backup completed successfully." +| stats latest(_time) as latestTime by COMPUTERNAME +| `security_content_ctime(latestTime)` +| rename COMPUTERNAME as dest +| eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) +| search isOutlier=1 +| table latestTime, dest +| `extended_period_without_successful_netbackup_backups_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Monitor_Backup_Solution|Monitor Backup Solution]] + + +====How To Implement==== +To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===First time seen command line argument=== +This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search [ +| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process +| `drop_dm_object_name(Processes)` +| inputlookup append=t previously_seen_cmd_line_arguments +| stats min(firstTime) as firstTime, max(lastTime) as lastTime by process +| outputlookup previously_seen_cmd_line_arguments +| eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) +| where newCmdLineArgument=1 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table process] +| `first_time_seen_command_line_argument_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#DHS_Report_TA18-074A|DHS Report TA18-074A]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Command-Line_Executions|Suspicious Command-Line Executions]] + +* [[Documentation:ESSOC:stories:UseCase#Orangeworm_Attack_Group|Orangeworm Attack Group]] + +* [[Documentation:ESSOC:stories:UseCase#Possible_Backdoor_Activity_Associated_With_MUDCARP_Espionage_Campaigns|Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns]] + +* [[Documentation:ESSOC:stories:UseCase#Hidden_Cobra_Malware|Hidden Cobra Malware]] + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|- +| T1059.003 +| Windows Command Shell +| Execution +|} + +====Kill Chain Phase==== + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name + +====Reference==== + + +====Test Dataset==== + + +''version'': 5 +
+
+ +---- + +===GCP GCR container uploaded=== +This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1525/ T1525] +* '''Last Updated''': 2020-02-20 + +
+
+ +====Search==== + +|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path +| `drop_dm_object_name("Storage")` +| `gcp_gcr_container_uploaded_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Container_Implantation_Monitoring_and_Investigation|Container Implantation Monitoring and Investigation]] + + +====How To Implement==== +You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1525 +| Implant Container Image +| Persistence +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Identify New User Accounts=== +This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.002/ T1078.002] +* '''Last Updated''': 2017-09-12 + +
+
+ +====Search==== + +| from datamodel Identity_Management.All_Identities +| eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") +| search empStatus="Accounts created in last week" +| `security_content_ctime(endDate)` +| `security_content_ctime(startDate)` +| table identity empStatus endDate startDate +| `identify_new_user_accounts_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Account_Monitoring_and_Controls|Account Monitoring and Controls]] + + +====How To Implement==== +To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.002 +| Domain Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments=== +This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.001/ T1059.001] +* '''Last Updated''': 2021-01-19 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* +| `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Malicious_PowerShell|Malicious PowerShell]] + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|} + +====Kill Chain Phase==== + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +Legitimate process can have this combination of command-line options, but it's not common. + +====Reference==== + + +====Test Dataset==== + + +''version'': 6 +
+
+ +---- + +===Monitor DNS For Brand Abuse=== +This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': +* '''Last Updated''': 2017-09-23 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query +| `drop_dm_object_name("DNS")` +| `security_content_ctime(firstTime)` +| `brand_abuse_dns` +| `monitor_dns_for_brand_abuse_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Brand_Monitoring|Brand Monitoring]] + + +====How To Implement==== +You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Delivery + +* Actions on Objectives + + +====Known False Positives==== +None at this time + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Open Redirect in Splunk Web=== +This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2017-09-19 + +
+
+ +====Search==== +index=_internal sourcetype=splunk_web_access return_to="/%09/*" +| `open_redirect_in_splunk_web_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Splunk_Enterprise_Vulnerability|Splunk Enterprise Vulnerability]] + + +====How To Implement==== +No extra steps needed to implement this search. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Osquery pack - ColdRoot detection=== +This search looks for ColdRoot events from the osx-attacks osquery pack. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2019-01-29 + +
+
+ +====Search==== + +| from datamodel Alerts.Alerts +| search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) +| rename columns.path as path +| bucket _time span=30s +| stats count(path) by _time, host, user, path +| `osquery_pack___coldroot_detection_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#ColdRoot_MacOS_RAT|ColdRoot MacOS RAT]] + + +====How To Implement==== +In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Installation + +* Command and Control + + +====Known False Positives==== +There are no known false positives. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Processes created by netsh=== +This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.004/ T1562.004] +* '''Last Updated''': 2020-11-23 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `processes_created_by_netsh_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Netsh_Abuse|Netsh Abuse]] + + +====How To Implement==== +To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.004 +| Disable or Modify System Firewall +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft. + +====Reference==== + + +====Test Dataset==== + + +''version'': 5 +
+
+ +---- + +===Prohibited Software On Endpoint=== +This search looks for applications on the endpoint that you have marked as prohibited. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': +* '''Last Updated''': 2019-10-11 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `prohibited_softwares` +| `prohibited_software_on_endpoint_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Monitor_for_Unauthorized_Software|Monitor for Unauthorized Software]] + +* [[Documentation:ESSOC:stories:UseCase#Emotet_Malware__DHS_Report_TA18-201A_|Emotet Malware DHS Report TA18-201A ]] + +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Installation + +* Command and Control + +* Actions on Objectives + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Reg exe used to hide files directories via registry keys=== +The search looks for command-line arguments used to hide a file or directory using the reg add command. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1564.001/ T1564.001] +* '''Last Updated''': 2019-02-27 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| regex process = "(/d\s+2)" +| `reg_exe_used_to_hide_files_directories_via_registry_keys_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Windows_Defense_Evasion_Tactics|Windows Defense Evasion Tactics]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Windows_Registry_Activities|Suspicious Windows Registry Activities]] + +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1564.001 +| Hidden Files and Directories +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None at the moment + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Remote Registry Key modifications=== +This search monitors for remote modifications to registry keys. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-03-02 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Registry)` +| `remote_registry_key_modifications_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Windows_Defense_Evasion_Tactics|Windows Defense Evasion Tactics]] + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Windows_Registry_Activities|Suspicious Windows Registry Activities]] + +* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]] + + +====How To Implement==== +To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Remote WMI Command Attempt=== +This search looks for wmic.exe being launched with parameters to operate on remote systems. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1047/ T1047] +* '''Last Updated''': 2018-12-03 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `remote_wmi_command_attempt_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_WMI_Use|Suspicious WMI Use]] + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Administrators may use this legitimately to gather info from remote systems. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Scheduled tasks used in BadRabbit ransomware=== +This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1053.005/ T1053.005] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| search (process=*rhaegal* OR process=*drogon* OR *viserion_*) +| `scheduled_tasks_used_in_badrabbit_ransomware_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +No known false positives + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Splunk Enterprise Information Disclosure=== +This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2018-06-14 + +
+
+ +====Search==== +index=_internal sourcetype=splunkd_ui_access server-info +| search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" +| rename clientip as src_ip, splunk_server as dest +| stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `splunk_enterprise_information_disclosure_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Splunk_Enterprise_Vulnerability_CVE-2018-11409|Splunk Enterprise Vulnerability CVE-2018-11409]] + + +====How To Implement==== +The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Suspicious Changes to File Associations=== +This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.001/ T1546.001] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest +| `drop_dm_object_name("Processes")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| join [ +| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest +| `drop_dm_object_name("Registry")` +| table process_id dest registry_path] +| `suspicious_changes_to_file_associations_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_Windows_Registry_Activities|Suspicious Windows Registry Activities]] + +* [[Documentation:ESSOC:stories:UseCase#Windows_File_Extension_and_Association_Abuse|Windows File Extension and Association Abuse]] + + +====How To Implement==== +To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.001 +| Change Default File Association +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. + +====Reference==== + + +====Test Dataset==== + + +''version'': 4 +
+
+ +---- + +===Suspicious File Write=== +The search looks for files created with names that have been linked to malicious activity. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2019-04-25 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `drop_dm_object_name(Filesystem)` +| `suspicious_writes` +| `suspicious_file_write_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Hidden_Cobra_Malware|Hidden Cobra Malware]] + + +====How To Implement==== +You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Suspicious writes to System Volume Information=== +This search detects writes to the 'System Volume Information' folder by something other than the System process. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036/ T1036] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== +(`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* +| stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `suspicious_writes_to_system_volume_information_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Collection_and_Staging|Collection and Staging]] + + +====How To Implement==== +You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036 +| Masquerading +| Defense Evasion +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Uncommon Processes On Endpoint=== +This search looks for applications on the endpoint that you have marked as uncommon. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1204.002/ T1204.002] +* '''Last Updated''': 2020-07-22 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `uncommon_processes` +|`uncommon_processes_on_endpoint_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Windows_Privilege_Escalation|Windows Privilege Escalation]] + +* [[Documentation:ESSOC:stories:UseCase#Unusual_Processes|Unusual Processes]] + +* [[Documentation:ESSOC:stories:UseCase#Cloud_Federated_Credential_Abuse|Cloud Federated Credential Abuse]] + + +====How To Implement==== +You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1204.002 +| Malicious File +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + + +''version'': 4 +
+
+ +---- + +===Unsigned Image Loaded by LSASS=== +This search detects loading of unsigned images by LSASS. Deprecated because too noisy. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001] +* '''Last Updated''': 2019-12-06 + +
+
+ +====Search==== +`sysmon` EventID=7 Image=*lsass.exe Signed=false +| stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 +| rename Computer as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `unsigned_image_loaded_by_lsass_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] + + +====How To Implement==== +This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. + +====Reference==== + +* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Unsuccessful Netbackup backups=== +This search gives you the hosts where a backup was attempted and then failed. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2017-09-12 + +
+
+ +====Search==== +`netbackup` +| stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE +| search MESSAGE="An error occurred, failed to backup." +| `security_content_ctime(latestTime)` +| rename COMPUTERNAME as dest, MESSAGE as signature +| table latestTime, dest, signature +| `unsuccessful_netbackup_backups_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Monitor_Backup_Solution|Monitor Backup Solution]] + + +====How To Implement==== +To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Windows DisableAntiSpyware Registry=== +The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.001/ T1562.001] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name +| `drop_dm_object_name(Registry)` +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `windows_disableantispyware_registry_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]] + + +====How To Implement==== +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.001 +| Disable or Modify Tools +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +It is unusual to turn this feature on a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Windows connhost exe started forcefully=== +The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.003/ T1059.003] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `windows_connhost_exe_started_forcefully_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]] + + +====How To Implement==== +You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.003 +| Windows Command Shell +| Execution +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +This process should not be ran forcefully, we have not see any false positives for this detection + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Windows hosts file modification=== +The search looks for modifications to the hosts file on all Windows endpoints across your environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2018-11-02 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* +| `drop_dm_object_name(Filesystem)` +| `windows_hosts_file_modification_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Host_Redirection|Host Redirection]] + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +There may be legitimate reasons for system administrators to add entries to this file. + +====Reference==== + + +====Test Dataset==== + + ''version'': 1
@@ -4144,69 +10256,6 @@ None identified. Attempts to disable security-related services should be identif ===Attempted Credential Dump From Registry via Reg exe=== Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.002/ T1003.002] -* '''Last Updated''': 2019-12-02 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `attempted_credential_dump_from_registry_via_reg_exe_filter` - -====Associated Analytic Story==== - -* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] - - -====How To Implement==== -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.002 -| Security Account Manager -| Credential Access -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -None identified. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - - -''version'': 4 -
-
- ----- - -===Attempted Credential Dump From Registry via Reg exe=== -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - * '''Product''': UEBA for Security Cloud * '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] @@ -4279,6 +10328,69 @@ None identified. ---- +===Attempted Credential Dump From Registry via Reg exe=== +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.002/ T1003.002] +* '''Last Updated''': 2019-12-02 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.002 +| Security Account Manager +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + ===BCDEdit Failure Recovery Modification=== This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. @@ -4472,6 +10584,67 @@ Unless there are specific use cases, manipulating or exporting certificates usin ---- +===Child Processes of Spoolsv exe=== +This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068] +* '''Last Updated''': 2020-03-16 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `child_processes_of_spoolsv_exe_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Windows_Privilege_Escalation|Windows Privilege Escalation]] + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + ===Common Ransomware Extensions=== The search looks for file modifications with extensions commonly used by Ransomware @@ -6003,6 +12176,188 @@ Legitimate logon activity by authorized NTLM systems may be detected by this sea ---- +===Detect Baron Samedit CVE-2021-3156=== +This search detects the heap-based buffer overflow of sudoedit + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068] +* '''Last Updated''': 2021-01-27 + +
+
+ +====Search==== +`linux_hosts` +| search "sudoedit -s \\" +| `detect_baron_samedit_cve_2021_3156_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Baron_Samedit_CVE-2021-3156|Baron Samedit CVE-2021-3156]] + + +====How To Implement==== +Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +unknown + +====Reference==== + +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Baron Samedit CVE-2021-3156 Segfault=== +This search detects the heap-based buffer overflow of sudoedit + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068] +* '''Last Updated''': 2021-01-29 + +
+
+ +====Search==== +`linux_hosts` +| search sudoedit segfault +| stats count min(_time) as firstTime max(_time) as lastTime by host +| search count > 5 +| `detect_baron_samedit_cve_2021_3156_segfault_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Baron_Samedit_CVE-2021-3156|Baron Samedit CVE-2021-3156]] + + +====How To Implement==== +Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged. This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +If sudoedit is throwing segfaults for other reasons this will pick those up too. + +====Reference==== + +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect Baron Samedit CVE-2021-3156 via OSQuery=== +This search detects the heap-based buffer overflow of sudoedit + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068] +* '''Last Updated''': 2021-01-28 + +
+
+ +====Search==== +`osquery_process` +| search "columns.cmdline"="sudoedit -s \\*" +| `detect_baron_samedit_cve_2021_3156_via_osquery_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Baron_Samedit_CVE-2021-3156|Baron Samedit CVE-2021-3156]] + + +====How To Implement==== +OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +unknown + +====Reference==== + +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + ===Detect Computer Changed with Anonymous Account=== This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. @@ -6842,6 +13197,80 @@ The activity may be legitimate. For this reason, it's best to verify the account ---- +===Detect Oulook exe writing a zip file=== +This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1566.001/ T1566.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename process_id as malicious_id +| rename parent_process_id as outlook_id +| join malicious_id type=inner[ +| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest +| `drop_dm_object_name(Filesystem)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename process_id as malicious_id +| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] +| table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path +| where file_name != "" +| `detect_oulook_exe_writing_a__zip_file_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Phishing_Payloads|Phishing Payloads]] + + +====How To Implement==== +You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566.001 +| Spearphishing Attachment +| Initial Access +|} + +====Kill Chain Phase==== + +* Installation + +* Actions on Objectives + + +====Known False Positives==== +It is not uncommon for outlook to write legitimate zip files to the disk. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + ===Detect Pass the Hash=== This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique. @@ -8322,6 +14751,69 @@ It is uncommon for normal users to execute a series of commands used for network ---- +===Detection of tools built by NirSoft=== +This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1072/ T1072] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detection_of_tools_built_by_nirsoft_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Emotet_Malware__DHS_Report_TA18-201A_|Emotet Malware DHS Report TA18-201A ]] + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1072 +| Software Deployment Tools +| Execution, Lateral Movement +|} + +====Kill Chain Phase==== + +* Installation + +* Actions on Objectives + + +====Known False Positives==== +While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + ===Disabling Remote User Account Control=== The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). @@ -8783,6 +15275,74 @@ A new child process of zoom isn't malicious by that fact alone. Further investig ---- +===First Time Seen Running Windows Service=== +This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1569.002/ T1569.002] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== +`wineventlog_system` EventCode=7036 +| rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" +| where state="running" +| lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen +| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) +| table _time dest service +| `first_time_seen_running_windows_service_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Windows_Service_Abuse|Windows Service Abuse]] + +* [[Documentation:ESSOC:stories:UseCase#Orangeworm_Attack_Group|Orangeworm Attack Group]] + +* [[Documentation:ESSOC:stories:UseCase#Sunburst_Malware|Sunburst Malware]] + + +====How To Implement==== +While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1569.002 +| Service Execution +| Execution +|} + +====Kill Chain Phase==== + +* Installation + +* Actions on Objectives + + +====Known False Positives==== +A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. + +====Reference==== + + +====Test Dataset==== + + +''version'': 4 +
+
+ +---- + ===First time seen command line argument=== This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel. @@ -9771,6 +16331,63 @@ Older systems that support kerberos RC4 by default NetApp may generate false pos ---- +===MacOS - Re-opened Applications=== +This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': +* '''Last Updated''': 2020-02-07 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `macos___re_opened_applications_filter` + +====Associated Analytic Story==== + + +====How To Implement==== +In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Installation + +* Command and Control + + +====Known False Positives==== +At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + ===Malicious PowerShell Process - Connect To Internet With Hidden Window=== This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. @@ -10604,6 +17221,64 @@ Although unlikely, administrators may use wmi to execute commands for legitimate ---- +===Processes Tapping Keyboard Events=== +This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2019-01-25 + +
+
+ +====Search==== + +| from datamodel Alerts.Alerts +| search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps +| rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id +| dedup host,process_name +| table host,process_name, cmd, process_id +| `processes_tapping_keyboard_events_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#ColdRoot_MacOS_RAT|ColdRoot MacOS RAT]] + + +====How To Implement==== +In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + ===Processes launching netsh=== This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. @@ -12262,6 +18937,69 @@ There are many legitimate applications that leverage shim databases for compatib ---- +===Remote Desktop Process Running On System=== +This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name(Processes)` +| `remote_desktop_process_running_on_system_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Hidden_Cobra_Malware|Hidden Cobra Malware]] + +* [[Documentation:ESSOC:stories:UseCase#Lateral_Movement|Lateral Movement]] + + +====How To Implement==== +To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Remote Desktop may be used legitimately by users on the network. + +====Reference==== + + +====Test Dataset==== + + +''version'': 5 +
+
+ +---- + ===Remote Process Instantiation via WMI=== This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. @@ -13350,6 +20088,134 @@ Single-letter executables are not always malicious. Investigate this activity wi ---- +===Spike in File Writes=== +The search looks for a sharp increase in the number of files written to a particular host + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-03-16 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest +| `drop_dm_object_name(Filesystem)` +| eventstats max(_time) as maxtime +| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) +| search isOutlier=1 +| `spike_in_file_writes_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] + +* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]] + +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] + + +====How To Implement==== +In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Sunburst Correlation DLL and Network Event=== +The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203] +* '''Last Updated''': 2020-12-14 + +
+
+ +====Search==== +(`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) +| eventstats dc(EventCode) AS dc_events +| where dc_events=2 +| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host +| rename host as dest +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `sunburst_correlation_dll_and_network_event_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Sunburst_Malware|Sunburst Malware]] + + +====How To Implement==== +This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1203 +| Exploitation for Client Execution +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +unknown + +====Reference==== + +* https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + ===Suspicious MSBuild Rename=== The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. @@ -15047,6 +21913,70 @@ Administrators may modify the boot configuration. * https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log +''version'': 1 + + + +---- + +===WMI Permanent Event Subscription=== +This search looks for the creation of WMI permanent event subscriptions. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1047/ T1047] +* '''Last Updated''': 2018-10-23 + +
+
+ +====Search==== +`wmi` EventCode=5861 Binding +| rex field=Message "Consumer =\s+(?[^; +|^$]+)" +| search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" +| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| rename ComputerName as dest +| `wmi_permanent_event_subscription_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_WMI_Use|Suspicious WMI Use]] + + +====How To Implement==== +To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Although unlikely, administrators may use event subscriptions for legitimate purposes. + +====Reference==== + + +====Test Dataset==== + + ''version'': 1
@@ -15114,6 +22044,69 @@ Although unlikely, administrators may use event subscriptions for legitimate pur ---- +===WMI Temporary Event Subscription=== +This search looks for the creation of WMI temporary event subscriptions. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1047/ T1047] +* '''Last Updated''': 2018-10-23 + +
+
+ +====Search==== +`wmi` EventCode=5860 Temporary +| rex field=Message "NotificationQuery =\s+(?[^; +|^$]+)" +| search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" +| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `wmi_temporary_event_subscription_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_WMI_Use|Suspicious WMI Use]] + + +====How To Implement==== +To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + ===Windows AdFind Exe=== This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. @@ -15463,6 +22456,157 @@ It's possible there can be long domain names that are legitimate. ---- +===DNS record changed=== +The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.004/ T1071.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| inputlookup discovered_dns_records +| rename answer as discovered_answer +| join domain[ +|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query +| rename DNS.query as query +| where query!="unknown" +| rex field=query "(?\w+\.\w+?)(?:$ +|/)"] +| makemv delim=" " answer +| makemv delim=" " type +| sort -count +| table count,src,domain,type,query,current_answer,discovered_answer +| makemv current_answer +| mvexpand current_answer +| makemv discovered_answer +| eval n=mvfind(discovered_answer, current_answer) +| where isnull(n) +| `dns_record_changed_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#DNS_Hijacking|DNS Hijacking]] + + +====How To Implement==== +To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ + + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.004 +| DNS +| Command and Control +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. + +====Reference==== + + +====Test Dataset==== + + +''version'': 3 +
+
+ +---- + +===Detect ARP Poisoning=== +By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557.002/ T1557.002] +* '''Last Updated''': 2020-08-11 + +
+
+ +====Search==== +`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" +| eval src_interface=src_int_prefix_long+src_int_suffix +| stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface +| `security_content_ctime(firstTime)` +|`security_content_ctime(lastTime)` +| `detect_arp_poisoning_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Router_and_Infrastructure_Security|Router and Infrastructure Security]] + + +====How To Implement==== +This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1200 +| Hardware Additions +| Initial Access +|- +| T1498 +| Network Denial of Service +| Impact +|- +| T1557.002 +| ARP Cache Poisoning +| Collection, Credential Access +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Delivery + +* Actions on Objectives + + +====Known False Positives==== +This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely). + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + ===Detect IPv6 Network Infrastructure Threats=== By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. @@ -16384,6 +23528,390 @@ Some users and applications may leverage Dynamic DNS to reach out to some domain ---- +===Excessive DNS Failures=== +This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.004/ T1071.004] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query" +| `drop_dm_object_name("DNS")` +| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain +| where isnull(domain) +| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank +| where isnull(rank) +| stats sum(count) as count mode(queries) as queries by src +| `get_asset(src)` +| where count>50 +| `excessive_dns_failures_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Suspicious_DNS_Traffic|Suspicious DNS Traffic]] + +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] + + +====How To Implement==== +To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.004 +| DNS +| Command and Control +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Hosts receiving high volume of network traffic from email server=== +This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.002/ T1114.002] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d +| `drop_dm_object_name("All_Traffic")` +| eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in +| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip +| eval minimum_data_samples = 4, deviation_threshold = 3 +| where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") +| eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) +| table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average +| `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Collection_and_Staging|Collection and Staging]] + + +====How To Implement==== +This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.002 +| Remote Email Collection +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Large Volume of DNS ANY Queries=== +The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1498.002/ T1498.002] +* '''Last Updated''': 2017-09-20 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" +| `drop_dm_object_name("DNS")` +| where count>200 +| `large_volume_of_dns_any_queries_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#DNS_Amplification_Attacks|DNS Amplification Attacks]] + + +====How To Implement==== +To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1498.002 +| Reflection Amplification +| Impact +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Prohibited Network Traffic Allowed=== +This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048/ T1048] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action +| lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport +| search is_prohibited=true +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `prohibited_network_traffic_allowed_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Prohibited_Traffic_Allowed_or_Protocol_Mismatch|Prohibited Traffic Allowed or Protocol Mismatch]] + +* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]] + +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] + + +====How To Implement==== +In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048 +| Exfiltration Over Alternative Protocol +| Exfiltration +|} + +====Kill Chain Phase==== + +* Delivery + +* Command and Control + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Protocol or Port Mismatch=== +This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port +|`security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `protocol_or_port_mismatch_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Prohibited_Traffic_Allowed_or_Protocol_Mismatch|Prohibited Traffic Allowed or Protocol Mismatch]] + +* [[Documentation:ESSOC:stories:UseCase#Command_and_Control|Command and Control]] + + +====How To Implement==== +Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Known False Positives==== +None identified + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Protocols passing authentication in cleartext=== +This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': +* '''Last Updated''': 2020-11-04 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `drop_dm_object_name("All_Traffic")` +| `protocols_passing_authentication_in_cleartext_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Use_of_Cleartext_Protocols|Use of Cleartext Protocols]] + + +====How To Implement==== +This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Reconnaissance + +* Actions on Objectives + + +====Known False Positives==== +Some networks may use kerberized FTP or telnet servers, however, this is rare. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + ===Remote Desktop Network Bruteforce=== This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. @@ -16790,6 +24318,375 @@ Very few legitimate Content-Type fields will have a length greater than 100 char ==Web== +===Detect F5 TMUI RCE CVE-2020-5902=== +This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1190/ T1190] +* '''Last Updated''': 2020-08-02 + +
+
+ +====Search==== +`f5_bigip_rogue` +| regex _raw="(hsqldb; +|.*\\.\\.;.*)" +| search `detect_f5_tmui_rce_cve_2020_5902_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#F5_TMUI_RCE_CVE-2020-5902|F5 TMUI RCE CVE-2020-5902]] + + +====How To Implement==== +To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog. As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it. I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;). + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Known False Positives==== +unknown + +====Reference==== + +* https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/ + +* https://support.f5.com/csp/article/K52145254 + +* https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect attackers scanning for vulnerable JBoss servers=== +This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Web +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1082/ T1082] +* '''Last Updated''': 2017-09-23 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#JBoss_Vulnerability|JBoss Vulnerability]] + +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] + + +====How To Implement==== +You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1082 +| System Information Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Reconnaissance + + +====Known False Positives==== +It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Detect malicious requests to exploit JBoss servers=== +This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Web +* '''ATT&CK''': +* '''Last Updated''': 2017-09-23 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest +| search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| table src, dest_ip, http_method, url, firstTime, lastTime +| `detect_malicious_requests_to_exploit_jboss_servers_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#JBoss_Vulnerability|JBoss Vulnerability]] + +* [[Documentation:ESSOC:stories:UseCase#SamSam_Ransomware|SamSam Ransomware]] + + +====How To Implement==== +You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +No known false positives for this detection. + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===Monitor Web Traffic For Brand Abuse=== +This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Web +* '''ATT&CK''': +* '''Last Updated''': 2017-09-23 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src +| `drop_dm_object_name("Web")` +| `security_content_ctime(firstTime)` +| `brand_abuse_web` +| `monitor_web_traffic_for_brand_abuse_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Brand_Monitoring|Brand Monitoring]] + + +====How To Implement==== +You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +None at this time + +====Reference==== + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + +===SQL Injection with Long URLs=== +This search looks for long URLs that have several SQL commands visible within them. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Web +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1190/ T1190] +* '''Last Updated''': 2020-07-21 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent +| `drop_dm_object_name("Web")` +| eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 +| where num_sql_cmds > 3 +| `sql_injection_with_long_urls_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#SQL_Injection|SQL Injection]] + + +====How To Implement==== +To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + +====Kill Chain Phase==== + +* Delivery + + +====Known False Positives==== +It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. + +====Reference==== + + +====Test Dataset==== + + +''version'': 2 +
+
+ +---- + +===Supernova Webshell=== +This search aims to detect the Supernova webshell used in the SUNBURST attack. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Web +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1505.003/ T1505.003] +* '''Last Updated''': 2021-01-06 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count from datamodel=Web.Web where web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s +| `supernova_webshell_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Sunburst_Malware|Sunburst Malware]] + + +====How To Implement==== +To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1505.003 +| Web Shell +| Persistence +|} + +====Kill Chain Phase==== + +* Exfiltration + + +====Known False Positives==== +There might be false positives associted with this detection since items like args as a web argument is pretty generic. + +====Reference==== + +* https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html + +* https://www.guidepointsecurity.com/supernova-solarwinds-net-webshell-analysis/ + + +====Test Dataset==== + + +''version'': 1 +
+
+ +---- + ===Web Fraud - Account Harvesting=== This search is used to identify the creation of multiple user accounts using the same email domain name. diff --git a/docs/stories.md b/docs/stories.md index c0e95b87d1..014facc0eb 100644 --- a/docs/stories.md +++ b/docs/stories.md @@ -11,10 +11,9 @@ All the Analytic Stories shipped to different Splunk products. Below is a breakd ### Brand Monitoring Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Email, Network_Resolution, Web +- **ATT&CK**: - **Last Updated**: 2017-12-19
@@ -22,11 +21,11 @@ Detect and investigate activity that may indicate that an adversary is using fau #### Detection Profile -* +* [Monitor DNS For Brand Abuse](detections.md#monitor-dns-for-brand-abuse) -* +* [Monitor Email For Brand Abuse](detections.md#monitor-email-for-brand-abuse) -* +* [Monitor Web Traffic For Brand Abuse](detections.md#monitor-web-traffic-for-brand-abuse) #### ATT&CK @@ -34,9 +33,12 @@ Detect and investigate activity that may indicate that an adversary is using fau | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase +* Actions on Objectives + +* Delivery + #### Reference @@ -55,10 +57,9 @@ _version_: 1 ### DNS Amplification Attacks DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1498.002](https://attack.mitre.org/techniques/T1498.002/) - **Last Updated**: 2016-09-13
@@ -66,17 +67,19 @@ DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it respond #### Detection Profile -* +* [Large Volume of DNS ANY Queries](detections.md#large-volume-of-dns-any-queries) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1498.002 | Reflection Amplification | Impact | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -93,10 +96,9 @@ _version_: 1 ### Data Protection Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Change_Analysis, Network_Resolution +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/), [T1189](https://attack.mitre.org/techniques/T1189/) - **Last Updated**: 2017-09-14
@@ -104,21 +106,30 @@ Fortify your data-protection arsenal--while continuing to ensure data confidenti #### Detection Profile -* +* [Detect USB device insertion](detections.md#detect-usb-device-insertion) -* +* [Detect hosts connecting to dynamic domain providers](detections.md#detect-hosts-connecting-to-dynamic-domain-providers) -* +* [Detection of DNS Tunnels](detections.md#detection-of-dns-tunnels) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1189 | Drive-by Compromise | Initial Access | +| T1071.001 | Web Protocols | Command and Control | +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | +| T1048 | Exfiltration Over Alternative Protocol | Exfiltration | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Installation + #### Reference @@ -137,10 +148,9 @@ _version_: 1 ### Host Redirection Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/), [T1071.004](https://attack.mitre.org/techniques/T1071.004/) - **Last Updated**: 2017-09-14
@@ -148,21 +158,28 @@ Detect evidence of tactics used to redirect traffic from a host to a destination #### Detection Profile -* +* [Clients Connecting to Multiple DNS Servers](detections.md#clients-connecting-to-multiple-dns-servers) -* +* [DNS Query Requests Resolved by Unauthorized DNS Servers](detections.md#dns-query-requests-resolved-by-unauthorized-dns-servers) -* +* [Windows hosts file modification](detections.md#windows-hosts-file-modification) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | +| T1071.004 | DNS | Command and Control | +| T1095 | Non-Application Layer Protocol | Command and Control | +| T1189 | Drive-by Compromise | Initial Access | +| T1048 | Exfiltration Over Alternative Protocol | Exfiltration | +| T1071.001 | Web Protocols | Command and Control | #### Kill Chain Phase +* Command and Control + #### Reference @@ -177,10 +194,9 @@ _version_: 1 ### Netsh Abuse Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1562.004](https://attack.mitre.org/techniques/T1562.004/) - **Last Updated**: 2017-01-05
@@ -188,19 +204,21 @@ Detect activities and various techniques associated with the abuse of `netsh.exe #### Detection Profile -* +* [Processes created by netsh](detections.md#processes-created-by-netsh) -* +* [Processes launching netsh](detections.md#processes-launching-netsh) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1562.004 | Disable or Modify System Firewall | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -219,10 +237,9 @@ _version_: 1 ### Web Fraud Detection Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1136](https://attack.mitre.org/techniques/T1136/) - **Last Updated**: 2018-10-08
@@ -230,21 +247,24 @@ Monitor your environment for activity consistent with common attack techniques b #### Detection Profile -* +* [Web Fraud - Account Harvesting](detections.md#web-fraud---account-harvesting) -* +* [Web Fraud - Anomalous User Clickspeed](detections.md#web-fraud---anomalous-user-clickspeed) -* +* [Web Fraud - Password Sharing Across Accounts](detections.md#web-fraud---password-sharing-across-accounts) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1136 | Create Account | Persistence | +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -267,10 +287,9 @@ _version_: 1 ### Baron Samedit CVE-2021-3156 Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Research Team, this vulnerability has been found to affect sudo across multiple Linux distributions (Ubuntu 20.04 and prior, Debian 10 and prior, Fedora 33 and prior). As this vulnerability was committed to code in July 2011, there will be many distributions affected. Successful exploitation of this vulnerability allows any unprivileged user to gain root privileges on the vulnerable host. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/) - **Last Updated**: 2021-01-27
@@ -278,21 +297,23 @@ Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Researc #### Detection Profile -* +* [Detect Baron Samedit CVE-2021-3156](detections.md#detect-baron-samedit-cve-2021-3156) -* +* [Detect Baron Samedit CVE-2021-3156 Segfault](detections.md#detect-baron-samedit-cve-2021-3156-segfault) -* +* [Detect Baron Samedit CVE-2021-3156 via OSQuery](detections.md#detect-baron-samedit-cve-2021-3156-via-osquery) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | #### Kill Chain Phase +* Exploitation + #### Reference @@ -307,10 +328,9 @@ _version_: 1 ### Cobalt Strike Cobalt Strike is threat emulation software. Red teams and penetration testers use Cobalt Strike to demonstrate the risk of a breach and evaluate mature security programs. Most recently, Cobalt Strike has become the choice tool by threat groups due to its ease of use and extensibility. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-16
@@ -318,19 +338,21 @@ Cobalt Strike is threat emulation software. Red teams and penetration testers us #### Detection Profile -* +* [Suspicious Rundll32 StartW](detections.md#suspicious-rundll32-startw) -* +* [Suspicious Rundll32 no CommandLine Arguments](detections.md#suspicious-rundll32-no-commandline-arguments) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1218.011 | Rundll32 | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -353,10 +375,9 @@ _version_: 1 ### Collection and Staging Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint, Network_Traffic +- **ATT&CK**: [T1036](https://attack.mitre.org/techniques/T1036/), [T1114.001](https://attack.mitre.org/techniques/T1114.001/), [T1114.002](https://attack.mitre.org/techniques/T1114.002/) - **Last Updated**: 2020-02-03
@@ -364,25 +385,29 @@ Monitor for and investigate activities--such as suspicious writes to the Windows #### Detection Profile -* +* [Email files written outside of the Outlook directory](detections.md#email-files-written-outside-of-the-outlook-directory) -* +* [Email servers sending high volume traffic to hosts](detections.md#email-servers-sending-high-volume-traffic-to-hosts) -* +* [Hosts receiving high volume of network traffic from email server](detections.md#hosts-receiving-high-volume-of-network-traffic-from-email-server) -* +* [Suspicious writes to System Volume Information](detections.md#suspicious-writes-to-system-volume-information) -* +* [Suspicious writes to windows Recycle Bin](detections.md#suspicious-writes-to-windows-recycle-bin) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1114.001 | Local Email Collection | Collection | +| T1114.002 | Remote Email Collection | Collection | +| T1036 | Masquerading | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -399,10 +424,9 @@ _version_: 1 ### Command and Control Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Network_Resolution, Network_Traffic +- **ATT&CK**: [T1048](https://attack.mitre.org/techniques/T1048/), [T1048.003](https://attack.mitre.org/techniques/T1048.003/), [T1071.001](https://attack.mitre.org/techniques/T1071.001/), [T1071.004](https://attack.mitre.org/techniques/T1071.004/), [T1095](https://attack.mitre.org/techniques/T1095/), [T1189](https://attack.mitre.org/techniques/T1189/) - **Last Updated**: 2018-06-01
@@ -410,41 +434,52 @@ Detect and investigate tactics, techniques, and procedures leveraged by attacker #### Detection Profile -* +* [Clients Connecting to Multiple DNS Servers](detections.md#clients-connecting-to-multiple-dns-servers) -* +* [DNS Query Length Outliers - MLTK](detections.md#dns-query-length-outliers---mltk) -* +* [DNS Query Length With High Standard Deviation](detections.md#dns-query-length-with-high-standard-deviation) -* +* [DNS Query Requests Resolved by Unauthorized DNS Servers](detections.md#dns-query-requests-resolved-by-unauthorized-dns-servers) -* +* [Detect Large Outbound ICMP Packets](detections.md#detect-large-outbound-icmp-packets) -* +* [Detect Long DNS TXT Record Response](detections.md#detect-long-dns-txt-record-response) -* +* [Detect Spike in blocked Outbound Traffic from your AWS](detections.md#detect-spike-in-blocked-outbound-traffic-from-your-aws) -* +* [Detect hosts connecting to dynamic domain providers](detections.md#detect-hosts-connecting-to-dynamic-domain-providers) -* +* [Detection of DNS Tunnels](detections.md#detection-of-dns-tunnels) -* +* [Excessive DNS Failures](detections.md#excessive-dns-failures) -* +* [Prohibited Network Traffic Allowed](detections.md#prohibited-network-traffic-allowed) -* +* [Protocol or Port Mismatch](detections.md#protocol-or-port-mismatch) -* +* [TOR Traffic](detections.md#tor-traffic) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | +| T1071.004 | DNS | Command and Control | +| T1095 | Non-Application Layer Protocol | Command and Control | +| T1189 | Drive-by Compromise | Initial Access | +| T1048 | Exfiltration Over Alternative Protocol | Exfiltration | +| T1071.001 | Web Protocols | Command and Control | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Delivery + #### Reference @@ -461,10 +496,9 @@ _version_: 1 ### Common Phishing Frameworks Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1566.003](https://attack.mitre.org/techniques/T1566.003/) - **Last Updated**: 2019-04-29
@@ -472,17 +506,21 @@ Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. #### Detection Profile -* +* [Detect DNS requests to Phishing Sites leveraging EvilGinx2](detections.md#detect-dns-requests-to-phishing-sites-leveraging-evilginx2) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1566.003 | Spearphishing via Service | Initial Access | #### Kill Chain Phase +* Command and Control + +* Delivery + #### Reference @@ -501,10 +539,9 @@ _version_: 1 ### Credential Dumping Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/), [T1003.001](https://attack.mitre.org/techniques/T1003.001/), [T1003.002](https://attack.mitre.org/techniques/T1003.002/), [T1003.003](https://attack.mitre.org/techniques/T1003.003/), [T1059.001](https://attack.mitre.org/techniques/T1059.001/) - **Last Updated**: 2020-02-04
@@ -512,49 +549,57 @@ Uncover activity consistent with credential dumping, a technique wherein attacke #### Detection Profile -* +* [Access LSASS Memory for Dump Creation](detections.md#access-lsass-memory-for-dump-creation) -* +* [Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass](detections.md#attempt-to-set-default-powershell-execution-policy-to-unrestricted-or-bypass) -* +* [Attempted Credential Dump From Registry via Reg exe](detections.md#attempted-credential-dump-from-registry-via-reg-exe) -* +* [Create Remote Thread into LSASS](detections.md#create-remote-thread-into-lsass) -* +* [Creation of Shadow Copy](detections.md#creation-of-shadow-copy) -* +* [Creation of Shadow Copy with wmic and powershell](detections.md#creation-of-shadow-copy-with-wmic-and-powershell) -* +* [Creation of lsass Dump with Taskmgr](detections.md#creation-of-lsass-dump-with-taskmgr) -* +* [Credential Dumping via Copy Command from Shadow Copy](detections.md#credential-dumping-via-copy-command-from-shadow-copy) -* +* [Credential Dumping via Symlink to Shadow Copy](detections.md#credential-dumping-via-symlink-to-shadow-copy) -* +* [Detect Credential Dumping through LSASS access](detections.md#detect-credential-dumping-through-lsass-access) -* +* [Detect Dump LSASS Memory using comsvcs](detections.md#detect-dump-lsass-memory-using-comsvcs) -* +* [Detect Mimikatz Using Loaded Images](detections.md#detect-mimikatz-using-loaded-images) -* +* [Dump LSASS via comsvcs DLL](detections.md#dump-lsass-via-comsvcs-dll) -* +* [Dump LSASS via procdump](detections.md#dump-lsass-via-procdump) -* +* [Dump LSASS via procdump Rename](detections.md#dump-lsass-via-procdump-rename) -* +* [Ntdsutil export ntds](detections.md#ntdsutil-export-ntds) -* +* [Unsigned Image Loaded by LSASS](detections.md#unsigned-image-loaded-by-lsass) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1003.001 | LSASS Memory | Credential Access | +| T1059.001 | PowerShell | Execution | +| T1003 | OS Credential Dumping | Credential Access | +| T1003.002 | Security Account Manager | Credential Access | +| T1003.003 | NTDS | Credential Access | #### Kill Chain Phase +* Actions on Objectives + +* Installation + #### Reference @@ -571,10 +616,9 @@ _version_: 3 ### DNS Hijacking Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/), [T1071.004](https://attack.mitre.org/techniques/T1071.004/), [T1189](https://attack.mitre.org/techniques/T1189/) - **Last Updated**: 2020-02-04
@@ -582,23 +626,32 @@ Secure your environment against DNS hijacks with searches that help you detect a #### Detection Profile -* +* [Clients Connecting to Multiple DNS Servers](detections.md#clients-connecting-to-multiple-dns-servers) -* +* [DNS Query Requests Resolved by Unauthorized DNS Servers](detections.md#dns-query-requests-resolved-by-unauthorized-dns-servers) -* +* [DNS record changed](detections.md#dns-record-changed) -* +* [Detect hosts connecting to dynamic domain providers](detections.md#detect-hosts-connecting-to-dynamic-domain-providers) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | +| T1071.004 | DNS | Command and Control | +| T1095 | Non-Application Layer Protocol | Command and Control | +| T1189 | Drive-by Compromise | Initial Access | +| T1048 | Exfiltration Over Alternative Protocol | Exfiltration | +| T1071.001 | Web Protocols | Command and Control | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + #### Reference @@ -619,10 +672,9 @@ _version_: 1 ### Data Exfiltration The stealing of data by an adversary. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1041](https://attack.mitre.org/techniques/T1041/) - **Last Updated**: 2020-10-21
@@ -630,17 +682,19 @@ The stealing of data by an adversary. #### Detection Profile -* +* [Detect SNICat SNI Exfiltration](detections.md#detect-snicat-sni-exfiltration) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1041 | Exfiltration Over C2 Channel | Exfiltration | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -655,10 +709,9 @@ _version_: 1 ### Detect Zerologon Attack Uncover activity related to the execution of Zerologon CVE-2020-11472, a technique wherein attackers target a Microsoft Windows Domain Controller to reset its computer account password. The result from this attack is attackers can now provide themselves high privileges and take over Domain Controller. The included searches in this Analytic Story are designed to identify attempts to reset Domain Controller Computer Account via exploit code remotely or via the use of tool Mimikatz as payload carrier. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/), [T1190](https://attack.mitre.org/techniques/T1190/), [T1210](https://attack.mitre.org/techniques/T1210/) - **Last Updated**: 2020-09-18
@@ -666,23 +719,29 @@ Uncover activity related to the execution of Zerologon CVE-2020-11472, a techniq #### Detection Profile -* +* [Detect Computer Changed with Anonymous Account](detections.md#detect-computer-changed-with-anonymous-account) -* +* [Detect Credential Dumping through LSASS access](detections.md#detect-credential-dumping-through-lsass-access) -* +* [Detect Mimikatz Using Loaded Images](detections.md#detect-mimikatz-using-loaded-images) -* +* [Detect Zerologon via Zeek](detections.md#detect-zerologon-via-zeek) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1210 | Exploitation of Remote Services | Lateral Movement | +| T1003.001 | LSASS Memory | Credential Access | +| T1190 | Exploit Public-Facing Application | Initial Access | #### Kill Chain Phase +* Actions on Objectives + +* Exploitation + #### Reference @@ -703,10 +762,9 @@ _version_: 1 ### Disabling Security Tools Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1112](https://attack.mitre.org/techniques/T1112/), [T1543.003](https://attack.mitre.org/techniques/T1543.003/), [T1553.004](https://attack.mitre.org/techniques/T1553.004/), [T1562.001](https://attack.mitre.org/techniques/T1562.001/), [T1562.004](https://attack.mitre.org/techniques/T1562.004/) - **Last Updated**: 2020-02-04
@@ -714,27 +772,35 @@ Looks for activities and techniques associated with the disabling of security to #### Detection Profile -* +* [Attempt To Add Certificate To Untrusted Store](detections.md#attempt-to-add-certificate-to-untrusted-store) -* +* [Attempt To Stop Security Service](detections.md#attempt-to-stop-security-service) -* +* [Processes launching netsh](detections.md#processes-launching-netsh) -* +* [Sc exe Manipulating Windows Services](detections.md#sc-exe-manipulating-windows-services) -* +* [Suspicious Reg exe Process](detections.md#suspicious-reg-exe-process) -* +* [Unload Sysmon Filter Driver](detections.md#unload-sysmon-filter-driver) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1553.004 | Install Root Certificate | Defense Evasion | +| T1562.001 | Disable or Modify Tools | Defense Evasion | +| T1562.004 | Disable or Modify System Firewall | Defense Evasion | +| T1543.003 | Windows Service | Persistence, Privilege Escalation | +| T1112 | Modify Registry | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + +* Installation + #### Reference @@ -753,10 +819,9 @@ _version_: 2 ### F5 TMUI RCE CVE-2020-5902 Uncover activity consistent with CVE-2020-5902. Discovered by Positive Technologies researchers, this vulnerability affects F5 BIG-IP, BIG-IQ. and Traffix SDC devices (vulnerable versions in F5 support link below). This vulnerability allows unauthenticated users, along with authenticated users, who have access to the configuration utility to execute system commands, create/delete files, disable services, and/or execute Java code. This vulnerability can result in full system compromise. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/) - **Last Updated**: 2020-08-02
@@ -764,17 +829,19 @@ Uncover activity consistent with CVE-2020-5902. Discovered by Positive Technolog #### Detection Profile -* +* [Detect F5 TMUI RCE CVE-2020-5902](detections.md#detect-f5-tmui-rce-cve-2020-5902) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1190 | Exploit Public-Facing Application | Initial Access | #### Kill Chain Phase +* Exploitation + #### Reference @@ -793,10 +860,9 @@ _version_: 1 ### Lateral Movement Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint, Network_Traffic +- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021.001/), [T1053.005](https://attack.mitre.org/techniques/T1053.005/), [T1550.002](https://attack.mitre.org/techniques/T1550.002/), [T1558.003](https://attack.mitre.org/techniques/T1558.003/) - **Last Updated**: 2020-02-04
@@ -804,25 +870,30 @@ Detect and investigate tactics, techniques, and procedures around how attackers #### Detection Profile -* +* [Detect Activity Related to Pass the Hash Attacks](detections.md#detect-activity-related-to-pass-the-hash-attacks) -* +* [Kerberoasting spn request with RC4 encryption](detections.md#kerberoasting-spn-request-with-rc4-encryption) -* +* [Remote Desktop Network Traffic](detections.md#remote-desktop-network-traffic) -* +* [Remote Desktop Process Running On System](detections.md#remote-desktop-process-running-on-system) -* +* [Schtasks scheduling job on remote system](detections.md#schtasks-scheduling-job-on-remote-system) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1550.002 | Pass the Hash | Defense Evasion, Lateral Movement | +| T1558.003 | Kerberoasting | Credential Access | +| T1021.001 | Remote Desktop Protocol | Lateral Movement | +| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -837,10 +908,9 @@ _version_: 2 ### Malicious PowerShell Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1027](https://attack.mitre.org/techniques/T1027/), [T1059.001](https://attack.mitre.org/techniques/T1059.001/) - **Last Updated**: 2017-08-23
@@ -848,25 +918,32 @@ Attackers are finding stealthy ways "live off the land," leveraging utilities an #### Detection Profile -* +* [Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass](detections.md#attempt-to-set-default-powershell-execution-policy-to-unrestricted-or-bypass) -* +* [Malicious PowerShell Process - Connect To Internet With Hidden Window](detections.md#malicious-powershell-process---connect-to-internet-with-hidden-window) -* +* [Malicious PowerShell Process - Encoded Command](detections.md#malicious-powershell-process---encoded-command) -* +* [Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments](detections.md#malicious-powershell-process---multiple-suspicious-command-line-arguments) -* +* [Malicious PowerShell Process With Obfuscation Techniques](detections.md#malicious-powershell-process-with-obfuscation-techniques) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1059.001 | PowerShell | Execution | +| T1027 | Obfuscated Files or Information | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Installation + #### Reference @@ -883,10 +960,9 @@ _version_: 4 ### Phishing Payloads Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/), [T1566.002](https://attack.mitre.org/techniques/T1566.002/) - **Last Updated**: 2019-04-29
@@ -894,19 +970,24 @@ Detect signs of malicious payloads that may indicate that your environment has b #### Detection Profile -* +* [Detect Oulook exe writing a zip file](detections.md#detect-oulook-exe-writing-a--zip-file) -* +* [Process Creating LNK file in Suspicious Location](detections.md#process-creating-lnk-file-in-suspicious-location) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1566.001 | Spearphishing Attachment | Initial Access | +| T1566.002 | Spearphishing Link | Initial Access | #### Kill Chain Phase +* Actions on Objectives + +* Installation + #### Reference @@ -921,10 +1002,9 @@ _version_: 1 ### Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/), [T1059.003](https://attack.mitre.org/techniques/T1059.003/), [T1547.001](https://attack.mitre.org/techniques/T1547.001/) - **Last Updated**: 2020-01-22
@@ -932,25 +1012,31 @@ Monitor your environment for suspicious behaviors that resemble the techniques e #### Detection Profile -* +* [First time seen command line argument](detections.md#first-time-seen-command-line-argument) -* +* [Malicious PowerShell Process - Connect To Internet With Hidden Window](detections.md#malicious-powershell-process---connect-to-internet-with-hidden-window) -* +* [Registry Keys Used For Persistence](detections.md#registry-keys-used-for-persistence) -* +* [Unusually Long Command Line](detections.md#unusually-long-command-line) -* +* [Unusually Long Command Line - MLTK](detections.md#unusually-long-command-line---mltk) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1059.001 | PowerShell | Execution | +| T1059.003 | Windows Command Shell | Execution | +| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + #### Reference @@ -967,10 +1053,9 @@ _version_: 1 ### SQL Injection Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Web +- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/) - **Last Updated**: 2017-09-19
@@ -978,17 +1063,19 @@ Use the searches in this Analytic Story to help you detect structured query lang #### Detection Profile -* +* [SQL Injection with Long URLs](detections.md#sql-injection-with-long-urls) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1190 | Exploit Public-Facing Application | Initial Access | #### Kill Chain Phase +* Delivery + #### Reference @@ -1005,10 +1092,9 @@ _version_: 1 ### Sunburst Malware Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and management software. It was discovered by FireEye in December 2020. The actors behind this campaign gained access to numerous public and private organizations around the world. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint, Network_Traffic, Web +- **ATT&CK**: [T1018](https://attack.mitre.org/techniques/T1018/), [T1027](https://attack.mitre.org/techniques/T1027/), [T1053.005](https://attack.mitre.org/techniques/T1053.005/), [T1059.003](https://attack.mitre.org/techniques/T1059.003/), [T1071.001](https://attack.mitre.org/techniques/T1071.001/), [T1071.002](https://attack.mitre.org/techniques/T1071.002/), [T1203](https://attack.mitre.org/techniques/T1203/), [T1505.003](https://attack.mitre.org/techniques/T1505.003/), [T1543.003](https://attack.mitre.org/techniques/T1543.003/), [T1569.002](https://attack.mitre.org/techniques/T1569.002/) - **Last Updated**: 2020-12-14
@@ -1016,37 +1102,56 @@ Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and managemen #### Detection Profile -* +* [Detect Outbound SMB Traffic](detections.md#detect-outbound-smb-traffic) -* +* [Detect Prohibited Applications Spawning cmd exe](detections.md#detect-prohibited-applications-spawning-cmd-exe) -* +* [First Time Seen Running Windows Service](detections.md#first-time-seen-running-windows-service) -* +* [Malicious PowerShell Process - Encoded Command](detections.md#malicious-powershell-process---encoded-command) -* +* [Sc exe Manipulating Windows Services](detections.md#sc-exe-manipulating-windows-services) -* +* [Scheduled Task Deleted Or Created via CMD](detections.md#scheduled-task-deleted-or-created-via-cmd) -* +* [Schtasks scheduling job on remote system](detections.md#schtasks-scheduling-job-on-remote-system) -* +* [Sunburst Correlation DLL and Network Event](detections.md#sunburst-correlation-dll-and-network-event) -* +* [Supernova Webshell](detections.md#supernova-webshell) -* +* [TOR Traffic](detections.md#tor-traffic) -* +* [Windows AdFind Exe](detections.md#windows-adfind-exe) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1071.002 | File Transfer Protocols | Command and Control | +| T1059.003 | Windows Command Shell | Execution | +| T1569.002 | Service Execution | Execution | +| T1027 | Obfuscated Files or Information | Defense Evasion | +| T1543.003 | Windows Service | Persistence, Privilege Escalation | +| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | +| T1203 | Exploitation for Client Execution | Execution | +| T1505.003 | Web Shell | Persistence | +| T1071.001 | Web Protocols | Command and Control | +| T1018 | Remote System Discovery | Discovery | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Exfiltration + +* Exploitation + +* Installation + #### Reference @@ -1063,10 +1168,9 @@ _version_: 1 ### Suspicious Command-Line Executions Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036.003/), [T1059.001](https://attack.mitre.org/techniques/T1059.001/), [T1059.003](https://attack.mitre.org/techniques/T1059.003/) - **Last Updated**: 2020-02-03
@@ -1074,27 +1178,36 @@ Leveraging the Windows command-line interface (CLI) is one of the most common at #### Detection Profile -* +* [Detect Prohibited Applications Spawning cmd exe](detections.md#detect-prohibited-applications-spawning-cmd-exe) -* +* [Detect Use of cmd exe to Launch Script Interpreters](detections.md#detect-use-of-cmd-exe-to-launch-script-interpreters) -* +* [First time seen command line argument](detections.md#first-time-seen-command-line-argument) -* +* [System Processes Run From Unexpected Locations](detections.md#system-processes-run-from-unexpected-locations) -* +* [Unusually Long Command Line](detections.md#unusually-long-command-line) -* +* [Unusually Long Command Line - MLTK](detections.md#unusually-long-command-line---mltk) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1059.003 | Windows Command Shell | Execution | +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | +| T1059.001 | PowerShell | Execution | +| T1036.003 | Rename System Utilities | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Exploitation + #### Reference @@ -1113,10 +1226,9 @@ _version_: 2 ### Suspicious Compiled HTML Activity Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218.001/) - **Last Updated**: 2021-02-11
@@ -1124,23 +1236,25 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce #### Detection Profile -* +* [Detect HTML Help Renamed](detections.md#detect-html-help-renamed) -* +* [Detect HTML Help Spawn Child Process](detections.md#detect-html-help-spawn-child-process) -* +* [Detect HTML Help URL in Command Line](detections.md#detect-html-help-url-in-command-line) -* +* [Detect HTML Help Using InfoTech Storage Handlers](detections.md#detect-html-help-using-infotech-storage-handlers) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1218.001 | Compiled HTML File | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -1159,10 +1273,9 @@ _version_: 1 ### Suspicious DNS Traffic Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048.003/), [T1071.004](https://attack.mitre.org/techniques/T1071.004/), [T1189](https://attack.mitre.org/techniques/T1189/) - **Last Updated**: 2017-09-18
@@ -1170,31 +1283,40 @@ Attackers often attempt to hide within or otherwise abuse the domain name system #### Detection Profile -* +* [Clients Connecting to Multiple DNS Servers](detections.md#clients-connecting-to-multiple-dns-servers) -* +* [DNS Query Length Outliers - MLTK](detections.md#dns-query-length-outliers---mltk) -* +* [DNS Query Length With High Standard Deviation](detections.md#dns-query-length-with-high-standard-deviation) -* +* [DNS Query Requests Resolved by Unauthorized DNS Servers](detections.md#dns-query-requests-resolved-by-unauthorized-dns-servers) -* +* [Detect Long DNS TXT Record Response](detections.md#detect-long-dns-txt-record-response) -* +* [Detect hosts connecting to dynamic domain providers](detections.md#detect-hosts-connecting-to-dynamic-domain-providers) -* +* [Detection of DNS Tunnels](detections.md#detection-of-dns-tunnels) -* +* [Excessive DNS Failures](detections.md#excessive-dns-failures) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | +| T1071.004 | DNS | Command and Control | +| T1095 | Non-Application Layer Protocol | Command and Control | +| T1189 | Drive-by Compromise | Initial Access | +| T1048 | Exfiltration Over Alternative Protocol | Exfiltration | +| T1071.001 | Web Protocols | Command and Control | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + #### Reference @@ -1213,10 +1335,9 @@ _version_: 1 ### Suspicious Emails Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Email, UEBA +- **ATT&CK**: [T1566](https://attack.mitre.org/techniques/T1566/), [T1566.001](https://attack.mitre.org/techniques/T1566.001/) - **Last Updated**: 2020-01-27
@@ -1224,23 +1345,26 @@ Email remains one of the primary means for attackers to gain an initial foothold #### Detection Profile -* +* [Email Attachments With Lots Of Spaces](detections.md#email-attachments-with-lots-of-spaces) -* +* [Monitor Email For Brand Abuse](detections.md#monitor-email-for-brand-abuse) -* +* [Suspicious Email - UBA Anomaly](detections.md#suspicious-email---uba-anomaly) -* +* [Suspicious Email Attachment Extensions](detections.md#suspicious-email-attachment-extensions) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1566 | Phishing | Initial Access | +| T1566.001 | Spearphishing Attachment | Initial Access | #### Kill Chain Phase +* Delivery + #### Reference @@ -1255,10 +1379,9 @@ _version_: 1 ### Suspicious MSHTA Activity Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059.003/), [T1218.005](https://attack.mitre.org/techniques/T1218.005/), [T1547.001](https://attack.mitre.org/techniques/T1547.001/) - **Last Updated**: 2021-01-20
@@ -1266,31 +1389,37 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce #### Detection Profile -* +* [Detect MSHTA Url in Command Line](detections.md#detect-mshta-url-in-command-line) -* +* [Detect Prohibited Applications Spawning cmd exe](detections.md#detect-prohibited-applications-spawning-cmd-exe) -* +* [Detect Rundll32 Inline HTA Execution](detections.md#detect-rundll32-inline-hta-execution) -* +* [Detect mshta inline hta execution](detections.md#detect-mshta-inline-hta-execution) -* +* [Detect mshta renamed](detections.md#detect-mshta-renamed) -* +* [Registry Keys Used For Persistence](detections.md#registry-keys-used-for-persistence) -* +* [Suspicious mshta child process](detections.md#suspicious-mshta-child-process) -* +* [Suspicious mshta spawn](detections.md#suspicious-mshta-spawn) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1218.005 | Mshta | Defense Evasion | +| T1059.003 | Windows Command Shell | Execution | +| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + +* Exploitation + #### Reference @@ -1311,10 +1440,9 @@ _version_: 2 ### Suspicious Okta Activity Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078.001/) - **Last Updated**: 2020-04-02
@@ -1322,20 +1450,20 @@ Monitor your Okta environment for suspicious activities. Due to the Covid outbre #### Detection Profile -* +* [Multiple Okta Users With Invalid Credentials From The Same IP](detections.md#multiple-okta-users-with-invalid-credentials-from-the-same-ip) -* +* [Okta Account Lockout Events](detections.md#okta-account-lockout-events) -* +* [Okta Failed SSO Attempts](detections.md#okta-failed-sso-attempts) -* +* [Okta User Logins From Multiple Cities](detections.md#okta-user-logins-from-multiple-cities) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078.001 | Default Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | #### Kill Chain Phase @@ -1357,10 +1485,9 @@ _version_: 1 ### Suspicious Regsvcs Regasm Activity Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218.009/) - **Last Updated**: 2021-02-11
@@ -1368,27 +1495,29 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce #### Detection Profile -* +* [Detect Regasm Spawning a Process](detections.md#detect-regasm-spawning-a-process) -* +* [Detect Regasm with Network Connection](detections.md#detect-regasm-with-network-connection) -* +* [Detect Regasm with no Command Line Arguments](detections.md#detect-regasm-with-no-command-line-arguments) -* +* [Detect Regsvcs Spawning a Process](detections.md#detect-regsvcs-spawning-a-process) -* +* [Detect Regsvcs with Network Connection](detections.md#detect-regsvcs-with-network-connection) -* +* [Detect Regsvcs with No Command Line Arguments](detections.md#detect-regsvcs-with-no-command-line-arguments) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1218.009 | Regsvcs/Regasm | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -1407,10 +1536,9 @@ _version_: 1 ### Suspicious Regsvr32 Activity Monitor and detect techniques used by attackers who leverage the regsvr32.exe process to execute malicious code. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1218.010](https://attack.mitre.org/techniques/T1218.010/) - **Last Updated**: 2021-01-29
@@ -1418,19 +1546,21 @@ Monitor and detect techniques used by attackers who leverage the regsvr32.exe pr #### Detection Profile -* +* [Detect Regsvr32 Application Control Bypass](detections.md#detect-regsvr32-application-control-bypass) -* +* [Suspicious Regsvr32 Register Suspicious Path](detections.md#suspicious-regsvr32-register-suspicious-path) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1218.010 | Regsvr32 | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -1449,10 +1579,9 @@ _version_: 1 ### Suspicious Rundll32 Activity Monitor and detect techniques used by attackers who leverage rundll32.exe to execute arbitrary malicious code. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/), [T1036.003](https://attack.mitre.org/techniques/T1036.003/), [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2021-02-03
@@ -1460,31 +1589,35 @@ Monitor and detect techniques used by attackers who leverage rundll32.exe to exe #### Detection Profile -* +* [Detect Rundll32 Application Control Bypass - advpack](detections.md#detect-rundll32-application-control-bypass---advpack) -* +* [Detect Rundll32 Application Control Bypass - setupapi](detections.md#detect-rundll32-application-control-bypass---setupapi) -* +* [Detect Rundll32 Application Control Bypass - syssetup](detections.md#detect-rundll32-application-control-bypass---syssetup) -* +* [Dump LSASS via comsvcs DLL](detections.md#dump-lsass-via-comsvcs-dll) -* +* [Suspicious Rundll32 Rename](detections.md#suspicious-rundll32-rename) -* +* [Suspicious Rundll32 StartW](detections.md#suspicious-rundll32-startw) -* +* [Suspicious Rundll32 dllregisterserver](detections.md#suspicious-rundll32-dllregisterserver) -* +* [Suspicious Rundll32 no CommandLine Arguments](detections.md#suspicious-rundll32-no-commandline-arguments) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1218.011 | Rundll32 | Defense Evasion | +| T1003.001 | LSASS Memory | Credential Access | +| T1036.003 | Rename System Utilities | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -1503,10 +1636,9 @@ _version_: 1 ### Suspicious WMI Use Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/), [T1546.003](https://attack.mitre.org/techniques/T1546.003/) - **Last Updated**: 2018-10-23
@@ -1514,29 +1646,32 @@ Attackers are increasingly abusing Windows Management Instrumentation (WMI), a f #### Detection Profile -* +* [Process Execution via WMI](detections.md#process-execution-via-wmi) -* +* [Remote Process Instantiation via WMI](detections.md#remote-process-instantiation-via-wmi) -* +* [Remote WMI Command Attempt](detections.md#remote-wmi-command-attempt) -* +* [Script Execution via WMI](detections.md#script-execution-via-wmi) -* +* [WMI Permanent Event Subscription](detections.md#wmi-permanent-event-subscription) -* +* [WMI Permanent Event Subscription - Sysmon](detections.md#wmi-permanent-event-subscription---sysmon) -* +* [WMI Temporary Event Subscription](detections.md#wmi-temporary-event-subscription) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1047 | Windows Management Instrumentation | Execution | +| T1546.003 | Windows Management Instrumentation Event Subscription | Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -1553,10 +1688,9 @@ _version_: 2 ### Suspicious Windows Registry Activities Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1546.001](https://attack.mitre.org/techniques/T1546.001/), [T1546.011](https://attack.mitre.org/techniques/T1546.011/), [T1546.012](https://attack.mitre.org/techniques/T1546.012/), [T1547.001](https://attack.mitre.org/techniques/T1547.001/), [T1547.010](https://attack.mitre.org/techniques/T1547.010/), [T1548.002](https://attack.mitre.org/techniques/T1548.002/), [T1564.001](https://attack.mitre.org/techniques/T1564.001/) - **Last Updated**: 2018-05-31
@@ -1564,31 +1698,41 @@ Monitor and detect registry changes initiated from remote locations, which can b #### Detection Profile -* +* [Disabling Remote User Account Control](detections.md#disabling-remote-user-account-control) -* +* [Monitor Registry Keys for Print Monitors](detections.md#monitor-registry-keys-for-print-monitors) -* +* [Reg exe used to hide files directories via registry keys](detections.md#reg-exe-used-to-hide-files-directories-via-registry-keys) -* +* [Registry Keys Used For Persistence](detections.md#registry-keys-used-for-persistence) -* +* [Registry Keys Used For Privilege Escalation](detections.md#registry-keys-used-for-privilege-escalation) -* +* [Registry Keys for Creating SHIM Databases](detections.md#registry-keys-for-creating-shim-databases) -* +* [Remote Registry Key modifications](detections.md#remote-registry-key-modifications) -* +* [Suspicious Changes to File Associations](detections.md#suspicious-changes-to-file-associations) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1548.002 | Bypass User Account Control | Defense Evasion, Privilege Escalation | +| T1222.001 | Windows File and Directory Permissions Modification | Defense Evasion | +| T1547.010 | Port Monitors | Persistence, Privilege Escalation | +| T1564.001 | Hidden Files and Directories | Defense Evasion | +| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation | +| T1546.012 | Image File Execution Options Injection | Persistence, Privilege Escalation | +| T1546.011 | Application Shimming | Persistence, Privilege Escalation | +| T1546.001 | Change Default File Association | Persistence, Privilege Escalation | +| T1112 | Modify Registry | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -1605,10 +1749,9 @@ _version_: 1 ### Suspicious Zoom Child Processes Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059.003/), [T1068](https://attack.mitre.org/techniques/T1068/) - **Last Updated**: 2020-04-13
@@ -1616,19 +1759,26 @@ Attackers are using Zoom as an vector to increase privileges on a sytems. This s #### Detection Profile -* +* [Detect Prohibited Applications Spawning cmd exe](detections.md#detect-prohibited-applications-spawning-cmd-exe) -* +* [First Time Seen Child Process of Zoom](detections.md#first-time-seen-child-process-of-zoom) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1059.003 | Windows Command Shell | Execution | +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | +| T1059.001 | PowerShell | Execution | +| T1036.003 | Rename System Utilities | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + +* Exploitation + #### Reference @@ -1645,10 +1795,9 @@ _version_: 1 ### Trusted Developer Utilities Proxy Execution Monitor and detect behaviors used by attackers who leverage trusted developer utilities to execute malicious code. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1127](https://attack.mitre.org/techniques/T1127/), [T1127, T1036.003](https://attack.mitre.org/techniques/T1127, T1036.003/) - **Last Updated**: 2021-01-12
@@ -1656,19 +1805,22 @@ Monitor and detect behaviors used by attackers who leverage trusted developer ut #### Detection Profile -* +* [Suspicious microsoft workflow compiler rename](detections.md#suspicious-microsoft-workflow-compiler-rename) -* +* [Suspicious microsoft workflow compiler usage](detections.md#suspicious-microsoft-workflow-compiler-usage) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| | | | +| T1127 | Trusted Developer Utilities Proxy Execution | Defense Evasion | #### Kill Chain Phase +* Exploitation + #### Reference @@ -1687,10 +1839,9 @@ _version_: 1 ### Trusted Developer Utilities Proxy Execution MSBuild Monitor and detect techniques used by attackers who leverage the msbuild.exe process to execute malicious code. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036.003/), [T1127.001](https://attack.mitre.org/techniques/T1127.001/) - **Last Updated**: 2021-01-21
@@ -1698,21 +1849,24 @@ Monitor and detect techniques used by attackers who leverage the msbuild.exe pro #### Detection Profile -* +* [Suspicious MSBuild Rename](detections.md#suspicious-msbuild-rename) -* +* [Suspicious MSBuild Spawn](detections.md#suspicious-msbuild-spawn) -* +* [Suspicious msbuild path](detections.md#suspicious-msbuild-path) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1127.001 | MSBuild | Defense Evasion | +| T1036.003 | Rename System Utilities | Defense Evasion | #### Kill Chain Phase +* Exploitation + #### Reference @@ -1737,10 +1891,9 @@ _version_: 1 ### Windows DNS SIGRed CVE-2020-1350 Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpoint researchers, this vulnerability affects Windows 2003 to 2019, and is triggered by a malicious DNS response (only affects DNS over TCP). An attacker can use the malicious payload to cause a buffer overflow on the vulnerable system, leading to compromise. The included searches in this Analytic Story are designed to identify the large response payload for SIG and KEY DNS records which can be used for the exploit. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Network_Resolution +- **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/) - **Last Updated**: 2020-07-28
@@ -1748,19 +1901,21 @@ Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpo #### Detection Profile -* +* [Detect Windows DNS SIGRed via Splunk Stream](detections.md#detect-windows-dns-sigred-via-splunk-stream) -* +* [Detect Windows DNS SIGRed via Zeek](detections.md#detect-windows-dns-sigred-via-zeek) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1203 | Exploitation for Client Execution | Execution | #### Kill Chain Phase +* Exploitation + #### Reference @@ -1777,10 +1932,9 @@ _version_: 1 ### Windows Defense Evasion Tactics Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1112](https://attack.mitre.org/techniques/T1112/), [T1222.001](https://attack.mitre.org/techniques/T1222.001/), [T1548.002](https://attack.mitre.org/techniques/T1548.002/), [T1564.001](https://attack.mitre.org/techniques/T1564.001/) - **Last Updated**: 2018-05-31
@@ -1788,25 +1942,35 @@ Detect tactics used by malware to evade defenses on Windows endpoints. A few of #### Detection Profile -* +* [Disabling Remote User Account Control](detections.md#disabling-remote-user-account-control) -* +* [Hiding Files And Directories With Attrib exe](detections.md#hiding-files-and-directories-with-attrib-exe) -* +* [Reg exe used to hide files directories via registry keys](detections.md#reg-exe-used-to-hide-files-directories-via-registry-keys) -* +* [Remote Registry Key modifications](detections.md#remote-registry-key-modifications) -* +* [Suspicious Reg exe Process](detections.md#suspicious-reg-exe-process) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1548.002 | Bypass User Account Control | Defense Evasion, Privilege Escalation | +| T1222.001 | Windows File and Directory Permissions Modification | Defense Evasion | +| T1547.010 | Port Monitors | Persistence, Privilege Escalation | +| T1564.001 | Hidden Files and Directories | Defense Evasion | +| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation | +| T1546.012 | Image File Execution Options Injection | Persistence, Privilege Escalation | +| T1546.011 | Application Shimming | Persistence, Privilege Escalation | +| T1546.001 | Change Default File Association | Persistence, Privilege Escalation | +| T1112 | Modify Registry | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -1821,10 +1985,9 @@ _version_: 1 ### Windows Log Manipulation Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1070](https://attack.mitre.org/techniques/T1070/), [T1070.001](https://attack.mitre.org/techniques/T1070.001/), [T1490](https://attack.mitre.org/techniques/T1490/) - **Last Updated**: 2017-09-12
@@ -1832,23 +1995,27 @@ Adversaries often try to cover their tracks by manipulating Windows logs. Use th #### Detection Profile -* +* [Deleting Shadow Copies](detections.md#deleting-shadow-copies) -* +* [Suspicious wevtutil Usage](detections.md#suspicious-wevtutil-usage) -* +* [USN Journal Deletion](detections.md#usn-journal-deletion) -* +* [Windows Event Log Cleared](detections.md#windows-event-log-cleared) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1490 | Inhibit System Recovery | Impact | +| T1070.001 | Clear Windows Event Logs | Defense Evasion | +| T1070 | Indicator Removal on Host | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -1867,10 +2034,9 @@ _version_: 2 ### Windows Persistence Techniques Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/), [T1222.001](https://attack.mitre.org/techniques/T1222.001/), [T1543.003](https://attack.mitre.org/techniques/T1543.003/), [T1546.011](https://attack.mitre.org/techniques/T1546.011/), [T1547.001](https://attack.mitre.org/techniques/T1547.001/), [T1547.010](https://attack.mitre.org/techniques/T1547.010/), [T1564.001](https://attack.mitre.org/techniques/T1564.001/), [T1574.009](https://attack.mitre.org/techniques/T1574.009/), [T1574.011](https://attack.mitre.org/techniques/T1574.011/) - **Last Updated**: 2018-05-31
@@ -1878,41 +2044,53 @@ Monitor for activities and techniques associated with maintaining persistence on #### Detection Profile -* +* [Certutil exe certificate extraction](detections.md#certutil-exe-certificate-extraction) -* +* [Detect Path Interception By Creation Of program exe](detections.md#detect-path-interception-by-creation-of-program-exe) -* +* [Hiding Files And Directories With Attrib exe](detections.md#hiding-files-and-directories-with-attrib-exe) -* +* [Monitor Registry Keys for Print Monitors](detections.md#monitor-registry-keys-for-print-monitors) -* +* [Reg exe Manipulating Windows Services Registry Keys](detections.md#reg-exe-manipulating-windows-services-registry-keys) -* +* [Reg exe used to hide files directories via registry keys](detections.md#reg-exe-used-to-hide-files-directories-via-registry-keys) -* +* [Registry Keys Used For Persistence](detections.md#registry-keys-used-for-persistence) -* +* [Registry Keys for Creating SHIM Databases](detections.md#registry-keys-for-creating-shim-databases) -* +* [Remote Registry Key modifications](detections.md#remote-registry-key-modifications) -* +* [Sc exe Manipulating Windows Services](detections.md#sc-exe-manipulating-windows-services) -* +* [Schtasks used for forcing a reboot](detections.md#schtasks-used-for-forcing-a-reboot) -* +* [Shim Database File Creation](detections.md#shim-database-file-creation) -* +* [Shim Database Installation With Suspicious Parameters](detections.md#shim-database-installation-with-suspicious-parameters) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1574.009 | Path Interception by Unquoted Path | Defense Evasion, Persistence, Privilege Escalation | +| T1222.001 | Windows File and Directory Permissions Modification | Defense Evasion | +| T1547.010 | Port Monitors | Persistence, Privilege Escalation | +| T1574.011 | Services Registry Permissions Weakness | Defense Evasion, Persistence, Privilege Escalation | +| T1564.001 | Hidden Files and Directories | Defense Evasion | +| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation | +| T1546.011 | Application Shimming | Persistence, Privilege Escalation | +| T1543.003 | Windows Service | Persistence, Privilege Escalation | +| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + +* Installation + #### Reference @@ -1935,10 +2113,9 @@ _version_: 2 ### Windows Privilege Escalation Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1204.002](https://attack.mitre.org/techniques/T1204.002/), [T1546.008](https://attack.mitre.org/techniques/T1546.008/), [T1546.012](https://attack.mitre.org/techniques/T1546.012/) - **Last Updated**: 2020-02-04
@@ -1946,23 +2123,30 @@ Monitor for and investigate activities that may be associated with a Windows pri #### Detection Profile -* +* [Child Processes of Spoolsv exe](detections.md#child-processes-of-spoolsv-exe) -* +* [Overwriting Accessibility Binaries](detections.md#overwriting-accessibility-binaries) -* +* [Registry Keys Used For Privilege Escalation](detections.md#registry-keys-used-for-privilege-escalation) -* +* [Uncommon Processes On Endpoint](detections.md#uncommon-processes-on-endpoint) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1068 | Exploitation for Privilege Escalation | Privilege Escalation | +| T1546.008 | Accessibility Features | Persistence, Privilege Escalation | +| T1546.012 | Image File Execution Options Injection | Persistence, Privilege Escalation | +| T1204.002 | Malicious File | Execution | #### Kill Chain Phase +* Actions on Objectives + +* Exploitation + #### Reference @@ -1983,10 +2167,9 @@ _version_: 2 ### Asset Tracking Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Network_Sessions +- **ATT&CK**: - **Last Updated**: 2017-09-13
@@ -1994,7 +2177,7 @@ Keep a careful inventory of every asset on your network to make it easier to det #### Detection Profile -* +* [Detect Unauthorized Assets by MAC address](detections.md#detect-unauthorized-assets-by-mac-address) #### ATT&CK @@ -2002,9 +2185,14 @@ Keep a careful inventory of every asset on your network to make it easier to det | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase +* Actions on Objectives + +* Delivery + +* Reconnaissance + #### Reference @@ -2019,10 +2207,9 @@ _version_: 1 ### Monitor Backup Solution Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: - **Last Updated**: 2017-09-12
@@ -2030,9 +2217,9 @@ Address common concerns when monitoring your backup processes. These searches ca #### Detection Profile -* +* [Extended Period Without Successful Netbackup Backups](detections.md#extended-period-without-successful-netbackup-backups) -* +* [Unsuccessful Netbackup backups](detections.md#unsuccessful-netbackup-backups) #### ATT&CK @@ -2040,7 +2227,6 @@ Address common concerns when monitoring your backup processes. These searches ca | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase @@ -2057,10 +2243,9 @@ _version_: 1 ### Monitor for Unauthorized Software Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: - **Last Updated**: 2017-09-15
@@ -2068,7 +2253,7 @@ Identify and investigate prohibited/unauthorized software or processes that may #### Detection Profile -* +* [Prohibited Software On Endpoint](detections.md#prohibited-software-on-endpoint) #### ATT&CK @@ -2076,9 +2261,14 @@ Identify and investigate prohibited/unauthorized software or processes that may | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Installation + #### Reference @@ -2093,10 +2283,9 @@ _version_: 1 ### Monitor for Updates Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Updates +- **ATT&CK**: - **Last Updated**: 2017-09-15
@@ -2104,7 +2293,7 @@ Monitor your enterprise to ensure that your endpoints are being patched and upda #### Detection Profile -* +* [No Windows Updates in a time frame](detections.md#no-windows-updates-in-a-time-frame) #### ATT&CK @@ -2112,7 +2301,6 @@ Monitor your enterprise to ensure that your endpoints are being patched and upda | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase @@ -2129,10 +2317,9 @@ _version_: 1 ### Prohibited Traffic Allowed or Protocol Mismatch Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Network_Resolution, Network_Traffic +- **ATT&CK**: [T1048](https://attack.mitre.org/techniques/T1048/), [T1048.003](https://attack.mitre.org/techniques/T1048.003/), [T1071.001](https://attack.mitre.org/techniques/T1071.001/), [T1189](https://attack.mitre.org/techniques/T1189/) - **Last Updated**: 2017-09-11
@@ -2140,23 +2327,32 @@ Detect instances of prohibited network traffic allowed in the environment, as we #### Detection Profile -* +* [Detect hosts connecting to dynamic domain providers](detections.md#detect-hosts-connecting-to-dynamic-domain-providers) -* +* [Prohibited Network Traffic Allowed](detections.md#prohibited-network-traffic-allowed) -* +* [Protocol or Port Mismatch](detections.md#protocol-or-port-mismatch) -* +* [TOR Traffic](detections.md#tor-traffic) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1189 | Drive-by Compromise | Initial Access | +| T1071.001 | Web Protocols | Command and Control | +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | +| T1048 | Exfiltration Over Alternative Protocol | Exfiltration | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Delivery + #### Reference @@ -2171,10 +2367,9 @@ _version_: 1 ### Router and Infrastructure Security Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Authentication, Network_Traffic +- **ATT&CK**: [T1020.001](https://attack.mitre.org/techniques/T1020.001/), [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1542.005](https://attack.mitre.org/techniques/T1542.005/), [T1557](https://attack.mitre.org/techniques/T1557/), [T1557.002](https://attack.mitre.org/techniques/T1557.002/) - **Last Updated**: 2017-09-12
@@ -2182,29 +2377,42 @@ Validate the security configuration of network infrastructure and verify that on #### Detection Profile -* +* [Detect ARP Poisoning](detections.md#detect-arp-poisoning) -* +* [Detect IPv6 Network Infrastructure Threats](detections.md#detect-ipv6-network-infrastructure-threats) -* +* [Detect New Login Attempts to Routers](detections.md#detect-new-login-attempts-to-routers) -* +* [Detect Port Security Violation](detections.md#detect-port-security-violation) -* +* [Detect Rogue DHCP Server](detections.md#detect-rogue-dhcp-server) -* +* [Detect Software Download To Network Device](detections.md#detect-software-download-to-network-device) -* +* [Detect Traffic Mirroring](detections.md#detect-traffic-mirroring) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1200 | Hardware Additions | Initial Access | +| T1498 | Network Denial of Service | Impact | +| T1557.002 | ARP Cache Poisoning | Collection, Credential Access | +| T1557 | Man-in-the-Middle | Collection, Credential Access | +| T1542.005 | TFTP Boot | Defense Evasion, Persistence | +| T1020.001 | Traffic Duplication | Exfiltration | #### Kill Chain Phase +* Actions on Objectives + +* Delivery + +* Exploitation + +* Reconnaissance + #### Reference @@ -2221,10 +2429,9 @@ _version_: 1 ### Use of Cleartext Protocols Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Network_Traffic +- **ATT&CK**: - **Last Updated**: 2017-09-15
@@ -2232,7 +2439,7 @@ Leverage searches that detect cleartext network protocols that may leak credenti #### Detection Profile -* +* [Protocols passing authentication in cleartext](detections.md#protocols-passing-authentication-in-cleartext) #### ATT&CK @@ -2240,9 +2447,12 @@ Leverage searches that detect cleartext network protocols that may leak credenti | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase +* Actions on Objectives + +* Reconnaissance + #### Reference @@ -2263,10 +2473,9 @@ _version_: 1 ### AWS Cross Account Activity Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1550](https://attack.mitre.org/techniques/T1550/) - **Last Updated**: 2018-06-04
@@ -2274,25 +2483,28 @@ Track when a user assumes an IAM role in another AWS account to obtain cross-acc #### Detection Profile -* +* [aws detect attach to role policy](detections.md#aws-detect-attach-to-role-policy) -* +* [aws detect permanent key creation](detections.md#aws-detect-permanent-key-creation) -* +* [aws detect role creation](detections.md#aws-detect-role-creation) -* +* [aws detect sts assume role abuse](detections.md#aws-detect-sts-assume-role-abuse) -* +* [aws detect sts get session token abuse](detections.md#aws-detect-sts-get-session-token-abuse) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1550 | Use Alternate Authentication Material | Defense Evasion, Lateral Movement | #### Kill Chain Phase +* Lateral Movement + #### Reference @@ -2307,10 +2519,9 @@ _version_: 1 ### AWS Cryptomining Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/), [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2018-03-08
@@ -2318,27 +2529,30 @@ Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomin #### Detection Profile -* +* [Abnormally High AWS Instances Launched by User](detections.md#abnormally-high-aws-instances-launched-by-user) -* +* [Abnormally High AWS Instances Launched by User - MLTK](detections.md#abnormally-high-aws-instances-launched-by-user---mltk) -* +* [EC2 Instance Started In Previously Unseen Region](detections.md#ec2-instance-started-in-previously-unseen-region) -* +* [EC2 Instance Started With Previously Unseen AMI](detections.md#ec2-instance-started-with-previously-unseen-ami) -* +* [EC2 Instance Started With Previously Unseen Instance Type](detections.md#ec2-instance-started-with-previously-unseen-instance-type) -* +* [EC2 Instance Started With Previously Unseen User](detections.md#ec2-instance-started-with-previously-unseen-user) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -2353,10 +2567,9 @@ _version_: 1 ### AWS Network ACL Activity Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562.007/) - **Last Updated**: 2018-05-21
@@ -2364,23 +2577,27 @@ Monitor your AWS network infrastructure for bad configurations and malicious act #### Detection Profile -* +* [AWS Network Access Control List Created with All Open Ports](detections.md#aws-network-access-control-list-created-with-all-open-ports) -* +* [AWS Network Access Control List Deleted](detections.md#aws-network-access-control-list-deleted) -* +* [Detect Spike in Network ACL Activity](detections.md#detect-spike-in-network-acl-activity) -* +* [Detect Spike in blocked Outbound Traffic from your AWS](detections.md#detect-spike-in-blocked-outbound-traffic-from-your-aws) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + #### Reference @@ -2397,10 +2614,9 @@ _version_: 2 ### AWS Security Hub Alerts This story is focused around detecting Security Hub alerts generated from AWS - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: - **Last Updated**: 2020-08-04
@@ -2408,9 +2624,9 @@ This story is focused around detecting Security Hub alerts generated from AWS #### Detection Profile -* +* [Detect Spike in AWS Security Hub Alerts for EC2 Instance](detections.md#detect-spike-in-aws-security-hub-alerts-for-ec2-instance) -* +* [Detect Spike in AWS Security Hub Alerts for User](detections.md#detect-spike-in-aws-security-hub-alerts-for-user) #### ATT&CK @@ -2418,7 +2634,6 @@ This story is focused around detecting Security Hub alerts generated from AWS | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase @@ -2435,10 +2650,9 @@ _version_: 1 ### AWS Suspicious Provisioning Activities Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2018-03-16
@@ -2446,20 +2660,20 @@ Monitor your AWS provisioning activities for behaviors originating from unfamili #### Detection Profile -* +* [AWS Cloud Provisioning From Previously Unseen City](detections.md#aws-cloud-provisioning-from-previously-unseen-city) -* +* [AWS Cloud Provisioning From Previously Unseen Country](detections.md#aws-cloud-provisioning-from-previously-unseen-country) -* +* [AWS Cloud Provisioning From Previously Unseen IP Address](detections.md#aws-cloud-provisioning-from-previously-unseen-ip-address) -* +* [AWS Cloud Provisioning From Previously Unseen Region](detections.md#aws-cloud-provisioning-from-previously-unseen-region) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | #### Kill Chain Phase @@ -2477,10 +2691,9 @@ _version_: 1 ### AWS User Monitoring Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2018-03-12
@@ -2488,25 +2701,27 @@ Detect and investigate dormant user accounts for your AWS environment that have #### Detection Profile -* +* [Detect API activity from users without MFA](detections.md#detect-api-activity-from-users-without-mfa) -* +* [Detect AWS API Activities From Unapproved Accounts](detections.md#detect-aws-api-activities-from-unapproved-accounts) -* +* [Detect Spike in AWS API Activity](detections.md#detect-spike-in-aws-api-activity) -* +* [Detect Spike in Security Group Activity](detections.md#detect-spike-in-security-group-activity) -* +* [Detect new API calls from user roles](detections.md#detect-new-api-calls-from-user-roles) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -2523,10 +2738,9 @@ _version_: 1 ### Cloud Cryptomining Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Change +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/), [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2019-10-02
@@ -2534,25 +2748,28 @@ Monitor your cloud compute instances for activities related to cryptojacking/cry #### Detection Profile -* +* [Abnormally High Number Of Cloud Instances Launched](detections.md#abnormally-high-number-of-cloud-instances-launched) -* +* [Cloud Compute Instance Created By Previously Unseen User](detections.md#cloud-compute-instance-created-by-previously-unseen-user) -* +* [Cloud Compute Instance Created In Previously Unused Region](detections.md#cloud-compute-instance-created-in-previously-unused-region) -* +* [Cloud Compute Instance Created With Previously Unseen Image](detections.md#cloud-compute-instance-created-with-previously-unseen-image) -* +* [Cloud Compute Instance Created With Previously Unseen Instance Type](detections.md#cloud-compute-instance-created-with-previously-unseen-instance-type) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -2567,10 +2784,9 @@ _version_: 1 ### Cloud Federated Credential Abuse This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003.001/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1136.003](https://attack.mitre.org/techniques/T1136.003/), [T1204.002](https://attack.mitre.org/techniques/T1204.002/), [T1546.012](https://attack.mitre.org/techniques/T1546.012/), [T1556](https://attack.mitre.org/techniques/T1556/) - **Last Updated**: 2021-01-26
@@ -2578,39 +2794,52 @@ This analytical story addresses events that indicate abuse of cloud federated cr #### Detection Profile -* +* [AWS SAML Access by Provider User and Principal](detections.md#aws-saml-access-by-provider-user-and-principal) -* +* [AWS SAML Update identity provider](detections.md#aws-saml-update-identity-provider) -* +* [Certutil exe certificate extraction](detections.md#certutil-exe-certificate-extraction) -* +* [Detect Mimikatz Using Loaded Images](detections.md#detect-mimikatz-using-loaded-images) -* +* [Detect Mimikatz Via PowerShell And EventCode 4703](detections.md#detect-mimikatz-via-powershell-and-eventcode-4703) -* +* [Detect Rare Executables](detections.md#detect-rare-executables) -* +* [O365 Add App Role Assignment Grant User](detections.md#o365-add-app-role-assignment-grant-user) -* +* [O365 Added Service Principal](detections.md#o365-added-service-principal) -* +* [O365 Excessive SSO logon errors](detections.md#o365-excessive-sso-logon-errors) -* +* [O365 New Federated Domain Added](detections.md#o365-new-federated-domain-added) -* +* [Registry Keys Used For Privilege Escalation](detections.md#registry-keys-used-for-privilege-escalation) -* +* [Uncommon Processes On Endpoint](detections.md#uncommon-processes-on-endpoint) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1003.001 | LSASS Memory | Credential Access | +| T1136.003 | Cloud Account | Persistence | +| T1556 | Modify Authentication Process | Credential Access, Defense Evasion | +| T1546.012 | Image File Execution Options Injection | Persistence, Privilege Escalation | +| T1204.002 | Malicious File | Execution | #### Kill Chain Phase +* Actions on Objective + +* Actions on Objectives + +* Command and Control + +* Installation + #### Reference @@ -2629,10 +2858,9 @@ _version_: 1 ### Container Implantation Monitoring and Investigation Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1525](https://attack.mitre.org/techniques/T1525/) - **Last Updated**: 2020-02-20
@@ -2640,16 +2868,16 @@ Use the searches in this story to monitor your Kubernetes registry repositories #### Detection Profile -* +* [GCP GCR container uploaded](detections.md#gcp-gcr-container-uploaded) -* +* [New container uploaded to AWS ECR](detections.md#new-container-uploaded-to-aws-ecr) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1525 | Implant Container Image | Persistence | #### Kill Chain Phase @@ -2667,10 +2895,9 @@ _version_: 1 ### GCP Cross Account Activity Track when a user assumes an IAM role in another GCP account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2020-09-01
@@ -2678,23 +2905,25 @@ Track when a user assumes an IAM role in another GCP account to obtain cross-acc #### Detection Profile -* +* [GCP Detect accounts with high risk roles by project](detections.md#gcp-detect-accounts-with-high-risk-roles-by-project) -* +* [GCP Detect gcploit framework](detections.md#gcp-detect-gcploit-framework) -* +* [GCP Detect high risk permissions by resource and account](detections.md#gcp-detect-high-risk-permissions-by-resource-and-account) -* +* [gcp detect oauth token abuse](detections.md#gcp-detect-oauth-token-abuse) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | #### Kill Chain Phase +* Lateral Movement + #### Reference @@ -2709,10 +2938,9 @@ _version_: 1 ### Kubernetes Scanning Activity This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/) - **Last Updated**: 2020-04-15
@@ -2720,27 +2948,29 @@ This story addresses detection against Kubernetes cluster fingerprint scan and a #### Detection Profile -* +* [Amazon EKS Kubernetes Pod scan detection](detections.md#amazon-eks-kubernetes-pod-scan-detection) -* +* [Amazon EKS Kubernetes cluster scan detection](detections.md#amazon-eks-kubernetes-cluster-scan-detection) -* +* [GCP Kubernetes cluster pod scan detection](detections.md#gcp-kubernetes-cluster-pod-scan-detection) -* +* [GCP Kubernetes cluster scan detection](detections.md#gcp-kubernetes-cluster-scan-detection) -* +* [Kubernetes Azure pod scan fingerprint](detections.md#kubernetes-azure-pod-scan-fingerprint) -* +* [Kubernetes Azure scan fingerprint](detections.md#kubernetes-azure-scan-fingerprint) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1526 | Cloud Service Discovery | Discovery | #### Kill Chain Phase +* Reconnaissance + #### Reference @@ -2755,10 +2985,9 @@ _version_: 1 ### Kubernetes Sensitive Object Access Activity This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: - **Last Updated**: 2020-05-20
@@ -2766,23 +2995,23 @@ This story addresses detection and response of accounts acccesing Kubernetes clu #### Detection Profile -* +* [AWS EKS Kubernetes cluster sensitive object access](detections.md#aws-eks-kubernetes-cluster-sensitive-object-access) -* +* [Kubernetes AWS detect service accounts forbidden failure access](detections.md#kubernetes-aws-detect-service-accounts-forbidden-failure-access) -* +* [Kubernetes AWS detect suspicious kubectl calls](detections.md#kubernetes-aws-detect-suspicious-kubectl-calls) -* +* [Kubernetes Azure detect sensitive object access](detections.md#kubernetes-azure-detect-sensitive-object-access) -* +* [Kubernetes Azure detect service accounts forbidden failure access](detections.md#kubernetes-azure-detect-service-accounts-forbidden-failure-access) -* +* [Kubernetes Azure detect suspicious kubectl calls](detections.md#kubernetes-azure-detect-suspicious-kubectl-calls) -* +* [Kubernetes GCP detect sensitive object access](detections.md#kubernetes-gcp-detect-sensitive-object-access) -* +* [Kubernetes GCP detect service accounts forbidden failure access](detections.md#kubernetes-gcp-detect-service-accounts-forbidden-failure-access) -* +* [Kubernetes GCP detect suspicious kubectl calls](detections.md#kubernetes-gcp-detect-suspicious-kubectl-calls) #### ATT&CK @@ -2790,9 +3019,10 @@ This story addresses detection and response of accounts acccesing Kubernetes clu | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase +* Lateral Movement + #### Reference @@ -2807,10 +3037,9 @@ _version_: 1 ### Kubernetes Sensitive Role Activity This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: - **Last Updated**: 2020-05-20
@@ -2818,23 +3047,23 @@ This story addresses detection and response around Sensitive Role usage within a #### Detection Profile -* +* [Kubernetes AWS detect RBAC authorization by account](detections.md#kubernetes-aws-detect-rbac-authorization-by-account) -* +* [Kubernetes AWS detect most active service accounts by pod](detections.md#kubernetes-aws-detect-most-active-service-accounts-by-pod) -* +* [Kubernetes AWS detect sensitive role access](detections.md#kubernetes-aws-detect-sensitive-role-access) -* +* [Kubernetes Azure detect RBAC authorization by account](detections.md#kubernetes-azure-detect-rbac-authorization-by-account) -* +* [Kubernetes Azure detect most active service accounts by pod namespace](detections.md#kubernetes-azure-detect-most-active-service-accounts-by-pod-namespace) -* +* [Kubernetes Azure detect sensitive role access](detections.md#kubernetes-azure-detect-sensitive-role-access) -* +* [Kubernetes GCP detect RBAC authorizations by account](detections.md#kubernetes-gcp-detect-rbac-authorizations-by-account) -* +* [Kubernetes GCP detect most active service accounts by pod](detections.md#kubernetes-gcp-detect-most-active-service-accounts-by-pod) -* +* [Kubernetes GCP detect sensitive role access](detections.md#kubernetes-gcp-detect-sensitive-role-access) #### ATT&CK @@ -2842,9 +3071,10 @@ This story addresses detection and response around Sensitive Role usage within a | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase +* Lateral Movement + #### Reference @@ -2859,10 +3089,9 @@ _version_: 1 ### Office 365 Detections This story is focused around detecting Office 365 Attacks. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1110](https://attack.mitre.org/techniques/T1110/), [T1110.001](https://attack.mitre.org/techniques/T1110.001/), [T1114](https://attack.mitre.org/techniques/T1114/), [T1114.002](https://attack.mitre.org/techniques/T1114.002/), [T1114.003](https://attack.mitre.org/techniques/T1114.003/), [T1136.003](https://attack.mitre.org/techniques/T1136.003/), [T1556](https://attack.mitre.org/techniques/T1556/), [T1562.007](https://attack.mitre.org/techniques/T1562.007/) - **Last Updated**: 2020-12-16
@@ -2870,39 +3099,52 @@ This story is focused around detecting Office 365 Attacks. #### Detection Profile -* +* [High Number of Login Failures from a single source](detections.md#high-number-of-login-failures-from-a-single-source) -* +* [O365 Add App Role Assignment Grant User](detections.md#o365-add-app-role-assignment-grant-user) -* +* [O365 Added Service Principal](detections.md#o365-added-service-principal) -* +* [O365 Bypass MFA via Trusted IP](detections.md#o365-bypass-mfa-via-trusted-ip) -* +* [O365 Disable MFA](detections.md#o365-disable-mfa) -* +* [O365 Excessive Authentication Failures Alert](detections.md#o365-excessive-authentication-failures-alert) -* +* [O365 Excessive SSO logon errors](detections.md#o365-excessive-sso-logon-errors) -* +* [O365 New Federated Domain Added](detections.md#o365-new-federated-domain-added) -* +* [O365 PST export alert](detections.md#o365-pst-export-alert) -* +* [O365 Suspicious Admin Email Forwarding](detections.md#o365-suspicious-admin-email-forwarding) -* +* [O365 Suspicious Rights Delegation](detections.md#o365-suspicious-rights-delegation) -* +* [O365 Suspicious User Email Forwarding](detections.md#o365-suspicious-user-email-forwarding) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1110.001 | Password Guessing | Credential Access | +| T1136.003 | Cloud Account | Persistence | +| T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion | +| T1556 | Modify Authentication Process | Credential Access, Defense Evasion | +| T1110 | Brute Force | Credential Access | +| T1114 | Email Collection | Collection | +| T1114.003 | Email Forwarding Rule | Collection | +| T1114.002 | Remote Email Collection | Collection | #### Kill Chain Phase +* Actions on Objective + +* Actions on Objectives + +* Not Applicable + #### Reference @@ -2917,10 +3159,9 @@ _version_: 1 ### Suspicious AWS EC2 Activities Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/), [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2018-02-09
@@ -2928,27 +3169,30 @@ Use the searches in this Analytic Story to monitor your AWS EC2 instances for ev #### Detection Profile -* +* [Abnormally High AWS Instances Launched by User](detections.md#abnormally-high-aws-instances-launched-by-user) -* +* [Abnormally High AWS Instances Launched by User - MLTK](detections.md#abnormally-high-aws-instances-launched-by-user---mltk) -* +* [Abnormally High AWS Instances Terminated by User](detections.md#abnormally-high-aws-instances-terminated-by-user) -* +* [Abnormally High AWS Instances Terminated by User - MLTK](detections.md#abnormally-high-aws-instances-terminated-by-user---mltk) -* +* [EC2 Instance Started In Previously Unseen Region](detections.md#ec2-instance-started-in-previously-unseen-region) -* +* [EC2 Instance Started With Previously Unseen User](detections.md#ec2-instance-started-with-previously-unseen-user) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -2963,10 +3207,9 @@ _version_: 1 ### Suspicious AWS Login Activities Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Authentication +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/), [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2019-05-01
@@ -2974,23 +3217,26 @@ Monitor your AWS authentication events using your CloudTrail logs. Searches with #### Detection Profile -* +* [Detect AWS Console Login by User from New City](detections.md#detect-aws-console-login-by-user-from-new-city) -* +* [Detect AWS Console Login by User from New Country](detections.md#detect-aws-console-login-by-user-from-new-country) -* +* [Detect AWS Console Login by User from New Region](detections.md#detect-aws-console-login-by-user-from-new-region) -* +* [Detect new user AWS Console Login](detections.md#detect-new-user-aws-console-login) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -3005,10 +3251,9 @@ _version_: 1 ### Suspicious AWS S3 Activities Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2018-07-24
@@ -3016,23 +3261,25 @@ Use the searches in this Analytic Story to monitor your AWS S3 buckets for evide #### Detection Profile -* +* [Detect New Open S3 Buckets over AWS CLI](detections.md#detect-new-open-s3-buckets-over-aws-cli) -* +* [Detect New Open S3 buckets](detections.md#detect-new-open-s3-buckets) -* +* [Detect S3 access from a new IP](detections.md#detect-s3-access-from-a-new-ip) -* +* [Detect Spike in S3 Bucket deletion](detections.md#detect-spike-in-s3-bucket-deletion) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1530 | Data from Cloud Storage Object | Collection | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -3049,10 +3296,9 @@ _version_: 2 ### Suspicious AWS Traffic Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: - **Last Updated**: 2018-05-07
@@ -3060,7 +3306,7 @@ Leverage these searches to monitor your AWS network traffic for evidence of anom #### Detection Profile -* +* [Detect Spike in blocked Outbound Traffic from your AWS](detections.md#detect-spike-in-blocked-outbound-traffic-from-your-aws) #### ATT&CK @@ -3068,9 +3314,12 @@ Leverage these searches to monitor your AWS network traffic for evidence of anom | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + #### Reference @@ -3085,10 +3334,9 @@ _version_: 1 ### Suspicious Cloud Authentication Activities Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Authentication +- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/) - **Last Updated**: 2020-06-04
@@ -3096,25 +3344,28 @@ Monitor your cloud authentication events. Searches within this Analytic Story le #### Detection Profile -* +* [AWS Cross Account Activity From Previously Unseen Account](detections.md#aws-cross-account-activity-from-previously-unseen-account) -* +* [Detect AWS Console Login by New User](detections.md#detect-aws-console-login-by-new-user) -* +* [Detect AWS Console Login by User from New City](detections.md#detect-aws-console-login-by-user-from-new-city) -* +* [Detect AWS Console Login by User from New Country](detections.md#detect-aws-console-login-by-user-from-new-country) -* +* [Detect AWS Console Login by User from New Region](detections.md#detect-aws-console-login-by-user-from-new-region) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -3131,10 +3382,9 @@ _version_: 1 ### Suspicious Cloud Instance Activities Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Change +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-08-25
@@ -3142,21 +3392,23 @@ Monitor your cloud infrastructure provisioning activities for behaviors originat #### Detection Profile -* +* [Abnormally High Number Of Cloud Instances Destroyed](detections.md#abnormally-high-number-of-cloud-instances-destroyed) -* +* [Abnormally High Number Of Cloud Instances Launched](detections.md#abnormally-high-number-of-cloud-instances-launched) -* +* [Cloud Instance Modified By Previously Unseen User](detections.md#cloud-instance-modified-by-previously-unseen-user) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -3171,10 +3423,9 @@ _version_: 1 ### Suspicious Cloud Provisioning Activities Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Change +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/) - **Last Updated**: 2018-08-20
@@ -3182,20 +3433,20 @@ Monitor your cloud infrastructure provisioning activities for behaviors originat #### Detection Profile -* +* [Cloud Provisioning Activity From Previously Unseen City](detections.md#cloud-provisioning-activity-from-previously-unseen-city) -* +* [Cloud Provisioning Activity From Previously Unseen Country](detections.md#cloud-provisioning-activity-from-previously-unseen-country) -* +* [Cloud Provisioning Activity From Previously Unseen IP Address](detections.md#cloud-provisioning-activity-from-previously-unseen-ip-address) -* +* [Cloud Provisioning Activity From Previously Unseen Region](detections.md#cloud-provisioning-activity-from-previously-unseen-region) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | #### Kill Chain Phase @@ -3213,10 +3464,9 @@ _version_: 1 ### Suspicious Cloud User Activities Detect and investigate suspicious activities by users and roles in your cloud environments. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Change +- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2020-09-04
@@ -3224,21 +3474,24 @@ Detect and investigate suspicious activities by users and roles in your cloud en #### Detection Profile -* +* [Abnormally High Number Of Cloud Infrastructure API Calls](detections.md#abnormally-high-number-of-cloud-infrastructure-api-calls) -* +* [Abnormally High Number Of Cloud Security Group API Calls](detections.md#abnormally-high-number-of-cloud-security-group-api-calls) -* +* [Cloud API Calls From Previously Unseen User Roles](detections.md#cloud-api-calls-from-previously-unseen-user-roles) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | +| T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -3255,10 +3508,9 @@ _version_: 1 ### Suspicious GCP Storage Activities Use the searches in this Analytic Story to monitor your GCP Storage buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open storage buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/) - **Last Updated**: 2020-08-05
@@ -3266,19 +3518,21 @@ Use the searches in this Analytic Story to monitor your GCP Storage buckets for #### Detection Profile -* +* [Detect GCP Storage access from a new IP](detections.md#detect-gcp-storage-access-from-a-new-ip) -* +* [Detect New Open GCP Storage Buckets](detections.md#detect-new-open-gcp-storage-buckets) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1530 | Data from Cloud Storage Object | Collection | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -3295,10 +3549,9 @@ _version_: 1 ### Unusual AWS EC2 Modifications Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078.004/) - **Last Updated**: 2018-04-09
@@ -3306,14 +3559,14 @@ Identify unusual changes to your AWS EC2 instances that may indicate malicious a #### Detection Profile -* +* [EC2 Instance Modified With Previously Unseen User](detections.md#ec2-instance-modified-with-previously-unseen-user) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | #### Kill Chain Phase @@ -3337,10 +3590,9 @@ _version_: 1 ### ColdRoot MacOS RAT Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: - **Last Updated**: 2019-01-09
@@ -3348,9 +3600,9 @@ Leverage searches that allow you to detect and investigate unusual activities th #### Detection Profile -* +* [Osquery pack - ColdRoot detection](detections.md#osquery-pack---coldroot-detection) -* +* [Processes Tapping Keyboard Events](detections.md#processes-tapping-keyboard-events) #### ATT&CK @@ -3358,9 +3610,12 @@ Leverage searches that allow you to detect and investigate unusual activities th | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase +* Command and Control + +* Installation + #### Reference @@ -3379,10 +3634,9 @@ _version_: 1 ### DHS Report TA18-074A Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint, Network_Traffic +- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/), [T1053.005](https://attack.mitre.org/techniques/T1053.005/), [T1059.001](https://attack.mitre.org/techniques/T1059.001/), [T1059.003](https://attack.mitre.org/techniques/T1059.003/), [T1071.002](https://attack.mitre.org/techniques/T1071.002/), [T1112](https://attack.mitre.org/techniques/T1112/), [T1136.001](https://attack.mitre.org/techniques/T1136.001/), [T1204.002](https://attack.mitre.org/techniques/T1204.002/), [T1543.003](https://attack.mitre.org/techniques/T1543.003/), [T1547.001](https://attack.mitre.org/techniques/T1547.001/), [T1562.004](https://attack.mitre.org/techniques/T1562.004/) - **Last Updated**: 2020-01-22
@@ -3390,43 +3644,59 @@ Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA #### Detection Profile -* +* [Create local admin accounts using net exe](detections.md#create-local-admin-accounts-using-net-exe) -* +* [Detect New Local Admin account](detections.md#detect-new-local-admin-account) -* +* [Detect Outbound SMB Traffic](detections.md#detect-outbound-smb-traffic) -* +* [Detect PsExec With accepteula Flag](detections.md#detect-psexec-with-accepteula-flag) -* +* [First time seen command line argument](detections.md#first-time-seen-command-line-argument) -* +* [Malicious PowerShell Process - Execution Policy Bypass](detections.md#malicious-powershell-process---execution-policy-bypass) -* +* [Processes launching netsh](detections.md#processes-launching-netsh) -* +* [Registry Keys Used For Persistence](detections.md#registry-keys-used-for-persistence) -* +* [SMB Traffic Spike](detections.md#smb-traffic-spike) -* +* [SMB Traffic Spike - MLTK](detections.md#smb-traffic-spike---mltk) -* +* [Sc exe Manipulating Windows Services](detections.md#sc-exe-manipulating-windows-services) -* +* [Scheduled Task Deleted Or Created via CMD](detections.md#scheduled-task-deleted-or-created-via-cmd) -* +* [Single Letter Process On Endpoint](detections.md#single-letter-process-on-endpoint) -* +* [Suspicious Reg exe Process](detections.md#suspicious-reg-exe-process) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1136.001 | Local Account | Persistence | +| T1071.002 | File Transfer Protocols | Command and Control | +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | +| T1059.001 | PowerShell | Execution | +| T1059.003 | Windows Command Shell | Execution | +| T1562.004 | Disable or Modify System Firewall | Defense Evasion | +| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation | +| T1543.003 | Windows Service | Persistence, Privilege Escalation | +| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | +| T1204.002 | Malicious File | Execution | +| T1112 | Modify Registry | Defense Evasion | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Installation + #### Reference @@ -3441,10 +3711,9 @@ _version_: 2 ### Dynamic DNS Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and deny lists. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Network_Resolution, Web +- **ATT&CK**: [T1071.001](https://attack.mitre.org/techniques/T1071.001/), [T1189](https://attack.mitre.org/techniques/T1189/) - **Last Updated**: 2018-09-06
@@ -3452,19 +3721,26 @@ Detect and investigate hosts in your environment that may be communicating with #### Detection Profile -* +* [Detect hosts connecting to dynamic domain providers](detections.md#detect-hosts-connecting-to-dynamic-domain-providers) -* +* [Detect web traffic to dynamic domain providers](detections.md#detect-web-traffic-to-dynamic-domain-providers) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1189 | Drive-by Compromise | Initial Access | +| T1071.001 | Web Protocols | Command and Control | +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | +| T1048 | Exfiltration Over Alternative Protocol | Exfiltration | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + #### Reference @@ -3485,10 +3761,9 @@ _version_: 2 ### Emotet Malware DHS Report TA18-201A Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Email, Endpoint, Network_Traffic +- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/), [T1059.003](https://attack.mitre.org/techniques/T1059.003/), [T1072](https://attack.mitre.org/techniques/T1072/), [T1547.001](https://attack.mitre.org/techniques/T1547.001/), [T1566.001](https://attack.mitre.org/techniques/T1566.001/) - **Last Updated**: 2020-01-27
@@ -3496,33 +3771,47 @@ Detect rarely used executables, specific registry paths that may confer malware #### Detection Profile -* +* [Detect Rare Executables](detections.md#detect-rare-executables) -* +* [Detect Use of cmd exe to Launch Script Interpreters](detections.md#detect-use-of-cmd-exe-to-launch-script-interpreters) -* +* [Detection of tools built by NirSoft](detections.md#detection-of-tools-built-by-nirsoft) -* +* [Email Attachments With Lots Of Spaces](detections.md#email-attachments-with-lots-of-spaces) -* +* [Prohibited Software On Endpoint](detections.md#prohibited-software-on-endpoint) -* +* [Registry Keys Used For Persistence](detections.md#registry-keys-used-for-persistence) -* +* [SMB Traffic Spike](detections.md#smb-traffic-spike) -* +* [SMB Traffic Spike - MLTK](detections.md#smb-traffic-spike---mltk) -* +* [Suspicious Email Attachment Extensions](detections.md#suspicious-email-attachment-extensions) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1059.003 | Windows Command Shell | Execution | +| T1072 | Software Deployment Tools | Execution, Lateral Movement | +| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation | +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | +| T1566.001 | Spearphishing Attachment | Initial Access | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Delivery + +* Exploitation + +* Installation + #### Reference @@ -3541,10 +3830,9 @@ _version_: 1 ### Hidden Cobra Malware Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint, Network_Resolution, Network_Traffic +- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021.001/), [T1021.002](https://attack.mitre.org/techniques/T1021.002/), [T1048.003](https://attack.mitre.org/techniques/T1048.003/), [T1059.001](https://attack.mitre.org/techniques/T1059.001/), [T1059.003](https://attack.mitre.org/techniques/T1059.003/), [T1070.005](https://attack.mitre.org/techniques/T1070.005/), [T1071.002](https://attack.mitre.org/techniques/T1071.002/), [T1071.004](https://attack.mitre.org/techniques/T1071.004/) - **Last Updated**: 2020-01-22
@@ -3552,35 +3840,46 @@ Monitor for and investigate activities, including the creation or deletion of hi #### Detection Profile -* +* [Create or delete windows shares using net exe](detections.md#create-or-delete-windows-shares-using-net-exe) -* +* [DNS Query Length Outliers - MLTK](detections.md#dns-query-length-outliers---mltk) -* +* [DNS Query Length With High Standard Deviation](detections.md#dns-query-length-with-high-standard-deviation) -* +* [Detect Outbound SMB Traffic](detections.md#detect-outbound-smb-traffic) -* +* [First time seen command line argument](detections.md#first-time-seen-command-line-argument) -* +* [Remote Desktop Network Traffic](detections.md#remote-desktop-network-traffic) -* +* [Remote Desktop Process Running On System](detections.md#remote-desktop-process-running-on-system) -* +* [SMB Traffic Spike](detections.md#smb-traffic-spike) -* +* [SMB Traffic Spike - MLTK](detections.md#smb-traffic-spike---mltk) -* +* [Suspicious File Write](detections.md#suspicious-file-write) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1070.005 | Network Share Connection Removal | Defense Evasion | +| T1071.004 | DNS | Command and Control | +| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | +| T1071.002 | File Transfer Protocols | Command and Control | +| T1059.001 | PowerShell | Execution | +| T1059.003 | Windows Command Shell | Execution | +| T1021.001 | Remote Desktop Protocol | Lateral Movement | +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + #### Reference @@ -3597,10 +3896,9 @@ _version_: 2 ### Orangeworm Attack Group Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/), [T1059.003](https://attack.mitre.org/techniques/T1059.003/), [T1543.003](https://attack.mitre.org/techniques/T1543.003/), [T1569.002](https://attack.mitre.org/techniques/T1569.002/) - **Last Updated**: 2020-01-22
@@ -3608,21 +3906,31 @@ Detect activities and various techniques associated with the Orangeworm Attack G #### Detection Profile -* +* [First Time Seen Running Windows Service](detections.md#first-time-seen-running-windows-service) -* +* [First time seen command line argument](detections.md#first-time-seen-command-line-argument) -* +* [Sc exe Manipulating Windows Services](detections.md#sc-exe-manipulating-windows-services) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1569.002 | Service Execution | Execution | +| T1059.001 | PowerShell | Execution | +| T1059.003 | Windows Command Shell | Execution | +| T1574.011 | Services Registry Permissions Weakness | Defense Evasion, Persistence, Privilege Escalation | +| T1543.003 | Windows Service | Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Installation + #### Reference @@ -3639,10 +3947,9 @@ _version_: 2 ### Ransomware Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint, Network_Traffic +- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021.002/), [T1036.003](https://attack.mitre.org/techniques/T1036.003/), [T1047](https://attack.mitre.org/techniques/T1047/), [T1048](https://attack.mitre.org/techniques/T1048/), [T1053.005](https://attack.mitre.org/techniques/T1053.005/), [T1070](https://attack.mitre.org/techniques/T1070/), [T1070.001](https://attack.mitre.org/techniques/T1070.001/), [T1071.001](https://attack.mitre.org/techniques/T1071.001/), [T1485](https://attack.mitre.org/techniques/T1485/), [T1490](https://attack.mitre.org/techniques/T1490/), [T1547.001](https://attack.mitre.org/techniques/T1547.001/) - **Last Updated**: 2020-02-04
@@ -3650,55 +3957,77 @@ Leverage searches that allow you to detect and investigate unusual activities th #### Detection Profile -* +* [BCDEdit Failure Recovery Modification](detections.md#bcdedit-failure-recovery-modification) -* +* [Common Ransomware Extensions](detections.md#common-ransomware-extensions) -* +* [Common Ransomware Notes](detections.md#common-ransomware-notes) -* +* [Deleting Shadow Copies](detections.md#deleting-shadow-copies) -* +* [Prohibited Network Traffic Allowed](detections.md#prohibited-network-traffic-allowed) -* +* [Registry Keys Used For Persistence](detections.md#registry-keys-used-for-persistence) -* +* [Remote Process Instantiation via WMI](detections.md#remote-process-instantiation-via-wmi) -* +* [SMB Traffic Spike](detections.md#smb-traffic-spike) -* +* [SMB Traffic Spike - MLTK](detections.md#smb-traffic-spike---mltk) -* +* [Scheduled tasks used in BadRabbit ransomware](detections.md#scheduled-tasks-used-in-badrabbit-ransomware) -* +* [Schtasks used for forcing a reboot](detections.md#schtasks-used-for-forcing-a-reboot) -* +* [Spike in File Writes](detections.md#spike-in-file-writes) -* +* [Suspicious wevtutil Usage](detections.md#suspicious-wevtutil-usage) -* +* [System Processes Run From Unexpected Locations](detections.md#system-processes-run-from-unexpected-locations) -* +* [TOR Traffic](detections.md#tor-traffic) -* +* [USN Journal Deletion](detections.md#usn-journal-deletion) -* +* [Unusually Long Command Line](detections.md#unusually-long-command-line) -* +* [Unusually Long Command Line - MLTK](detections.md#unusually-long-command-line---mltk) -* +* [WBAdmin Delete System Backups](detections.md#wbadmin-delete-system-backups) -* +* [Windows Event Log Cleared](detections.md#windows-event-log-cleared) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1490 | Inhibit System Recovery | Impact | +| T1485 | Data Destruction | Impact | +| T1482 | Domain Trust Discovery | Discovery | +| T1048 | Exfiltration Over Alternative Protocol | Exfiltration | +| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation | +| T1021.001 | Remote Desktop Protocol | Lateral Movement | +| T1047 | Windows Management Instrumentation | Execution | +| T1486 | Data Encrypted for Impact | Impact | +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | +| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | +| T1070.001 | Clear Windows Event Logs | Defense Evasion | +| T1036.003 | Rename System Utilities | Defense Evasion | +| T1071.001 | Web Protocols | Command and Control | +| T1070 | Indicator Removal on Host | Defense Evasion | +| T1562.001 | Disable or Modify Tools | Defense Evasion | +| T1489 | Service Stop | Impact | +| T1059.003 | Windows Command Shell | Execution | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Delivery + #### Reference @@ -3715,10 +4044,9 @@ _version_: 1 ### Ransomware Cloud Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware. These searches include cloud related objects that may be targeted by malicious actors via cloud providers own encryption features. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/) - **Last Updated**: 2020-10-27
@@ -3726,16 +4054,16 @@ Leverage searches that allow you to detect and investigate unusual activities th #### Detection Profile -* +* [AWS Detect Users creating keys with encrypt policy without MFA](detections.md#aws-detect-users-creating-keys-with-encrypt-policy-without-mfa) -* +* [AWS Detect Users with KMS keys performing encryption S3](detections.md#aws-detect-users-with-kms-keys-performing-encryption-s3) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1486 | Data Encrypted for Impact | Impact | #### Kill Chain Phase @@ -3757,10 +4085,9 @@ _version_: 1 ### Ryuk Ransomware Leverage searches that allow you to detect and investigate unusual activities that might relate to the Ryuk ransomware, including looking for file writes associated with Ryuk, Stopping Security Access Manager, DisableAntiSpyware registry key modification, suspicious psexec use, and more. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint, Network_Traffic +- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021.001/), [T1059.003](https://attack.mitre.org/techniques/T1059.003/), [T1482](https://attack.mitre.org/techniques/T1482/), [T1485](https://attack.mitre.org/techniques/T1485/), [T1486](https://attack.mitre.org/techniques/T1486/), [T1489](https://attack.mitre.org/techniques/T1489/), [T1490](https://attack.mitre.org/techniques/T1490/), [T1562.001](https://attack.mitre.org/techniques/T1562.001/) - **Last Updated**: 2020-11-06
@@ -3768,37 +4095,61 @@ Leverage searches that allow you to detect and investigate unusual activities th #### Detection Profile -* +* [BCDEdit Failure Recovery Modification](detections.md#bcdedit-failure-recovery-modification) -* +* [Common Ransomware Notes](detections.md#common-ransomware-notes) -* +* [NLTest Domain Trust Discovery](detections.md#nltest-domain-trust-discovery) -* +* [Remote Desktop Network Bruteforce](detections.md#remote-desktop-network-bruteforce) -* +* [Remote Desktop Network Traffic](detections.md#remote-desktop-network-traffic) -* +* [Ryuk Test Files Detected](detections.md#ryuk-test-files-detected) -* +* [Spike in File Writes](detections.md#spike-in-file-writes) -* +* [WBAdmin Delete System Backups](detections.md#wbadmin-delete-system-backups) -* +* [Windows DisableAntiSpyware Registry](detections.md#windows-disableantispyware-registry) -* +* [Windows Security Account Manager Stopped](detections.md#windows-security-account-manager-stopped) -* +* [Windows connhost exe started forcefully](detections.md#windows-connhost-exe-started-forcefully) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1490 | Inhibit System Recovery | Impact | +| T1485 | Data Destruction | Impact | +| T1482 | Domain Trust Discovery | Discovery | +| T1048 | Exfiltration Over Alternative Protocol | Exfiltration | +| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation | +| T1021.001 | Remote Desktop Protocol | Lateral Movement | +| T1047 | Windows Management Instrumentation | Execution | +| T1486 | Data Encrypted for Impact | Impact | +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | +| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | +| T1070.001 | Clear Windows Event Logs | Defense Evasion | +| T1036.003 | Rename System Utilities | Defense Evasion | +| T1071.001 | Web Protocols | Command and Control | +| T1070 | Indicator Removal on Host | Defense Evasion | +| T1562.001 | Disable or Modify Tools | Defense Evasion | +| T1489 | Service Stop | Impact | +| T1059.003 | Windows Command Shell | Execution | #### Kill Chain Phase +* Actions on Objectives + +* Delivery + +* Exploitation + +* Reconnaissance + #### Reference @@ -3817,10 +4168,9 @@ _version_: 1 ### SamSam Ransomware Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint, Network_Traffic, Web +- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021.001/), [T1021.002](https://attack.mitre.org/techniques/T1021.002/), [T1082](https://attack.mitre.org/techniques/T1082/), [T1204.002](https://attack.mitre.org/techniques/T1204.002/), [T1485](https://attack.mitre.org/techniques/T1485/), [T1486](https://attack.mitre.org/techniques/T1486/), [T1490](https://attack.mitre.org/techniques/T1490/) - **Last Updated**: 2018-12-13
@@ -3828,41 +4178,57 @@ Leverage searches that allow you to detect and investigate unusual activities th #### Detection Profile -* +* [Batch File Write to System32](detections.md#batch-file-write-to-system32) -* +* [Common Ransomware Extensions](detections.md#common-ransomware-extensions) -* +* [Common Ransomware Notes](detections.md#common-ransomware-notes) -* +* [Deleting Shadow Copies](detections.md#deleting-shadow-copies) -* +* [Detect PsExec With accepteula Flag](detections.md#detect-psexec-with-accepteula-flag) -* +* [Detect attackers scanning for vulnerable JBoss servers](detections.md#detect-attackers-scanning-for-vulnerable-jboss-servers) -* +* [Detect malicious requests to exploit JBoss servers](detections.md#detect-malicious-requests-to-exploit-jboss-servers) -* +* [File with Samsam Extension](detections.md#file-with-samsam-extension) -* +* [Prohibited Software On Endpoint](detections.md#prohibited-software-on-endpoint) -* +* [Remote Desktop Network Bruteforce](detections.md#remote-desktop-network-bruteforce) -* +* [Remote Desktop Network Traffic](detections.md#remote-desktop-network-traffic) -* +* [Samsam Test File Write](detections.md#samsam-test-file-write) -* +* [Spike in File Writes](detections.md#spike-in-file-writes) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1204.002 | Malicious File | Execution | +| T1485 | Data Destruction | Impact | +| T1490 | Inhibit System Recovery | Impact | +| T1021.002 | SMB/Windows Admin Shares | Lateral Movement | +| T1082 | System Information Discovery | Discovery | +| T1021.001 | Remote Desktop Protocol | Lateral Movement | +| T1486 | Data Encrypted for Impact | Impact | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Delivery + +* Installation + +* Reconnaissance + #### Reference @@ -3881,10 +4247,9 @@ _version_: 1 ### Unusual Processes Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1016](https://attack.mitre.org/techniques/T1016/), [T1036.003](https://attack.mitre.org/techniques/T1036.003/), [T1204.002](https://attack.mitre.org/techniques/T1204.002/), [T1218.011](https://attack.mitre.org/techniques/T1218.011/) - **Last Updated**: 2020-02-04
@@ -3892,29 +4257,38 @@ Quickly identify systems running new or unusual processes in your environment th #### Detection Profile -* +* [Detect Rare Executables](detections.md#detect-rare-executables) -* +* [Detect processes used for System Network Configuration Discovery](detections.md#detect-processes-used-for-system-network-configuration-discovery) -* +* [RunDLL Loading DLL By Ordinal](detections.md#rundll-loading-dll-by-ordinal) -* +* [System Processes Run From Unexpected Locations](detections.md#system-processes-run-from-unexpected-locations) -* +* [Uncommon Processes On Endpoint](detections.md#uncommon-processes-on-endpoint) -* +* [Unusually Long Command Line](detections.md#unusually-long-command-line) -* +* [Unusually Long Command Line - MLTK](detections.md#unusually-long-command-line---mltk) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1016 | System Network Configuration Discovery | Discovery | +| T1218.011 | Rundll32 | Defense Evasion | +| T1036.003 | Rename System Utilities | Defense Evasion | +| T1204.002 | Malicious File | Execution | #### Kill Chain Phase +* Actions on Objectives + +* Command and Control + +* Installation + #### Reference @@ -3933,10 +4307,9 @@ _version_: 2 ### Windows File Extension and Association Abuse Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036.003/), [T1546.001](https://attack.mitre.org/techniques/T1546.001/) - **Last Updated**: 2018-01-26
@@ -3944,21 +4317,24 @@ Detect and investigate suspected abuse of file extensions and Windows file assoc #### Detection Profile -* +* [Execution of File With Spaces Before Extension](detections.md#execution-of-file-with-spaces-before-extension) -* +* [Execution of File with Multiple Extensions](detections.md#execution-of-file-with-multiple-extensions) -* +* [Suspicious Changes to File Associations](detections.md#suspicious-changes-to-file-associations) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1036.003 | Rename System Utilities | Defense Evasion | +| T1546.001 | Change Default File Association | Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + #### Reference @@ -3975,10 +4351,9 @@ _version_: 1 ### Windows Service Abuse Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1543.003](https://attack.mitre.org/techniques/T1543.003/), [T1569.002](https://attack.mitre.org/techniques/T1569.002/), [T1574.011](https://attack.mitre.org/techniques/T1574.011/) - **Last Updated**: 2017-11-02
@@ -3986,21 +4361,29 @@ Windows services are often used by attackers for persistence and the ability to #### Detection Profile -* +* [First Time Seen Running Windows Service](detections.md#first-time-seen-running-windows-service) -* +* [Reg exe Manipulating Windows Services Registry Keys](detections.md#reg-exe-manipulating-windows-services-registry-keys) -* +* [Sc exe Manipulating Windows Services](detections.md#sc-exe-manipulating-windows-services) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1569.002 | Service Execution | Execution | +| T1059.001 | PowerShell | Execution | +| T1059.003 | Windows Command Shell | Execution | +| T1574.011 | Services Registry Permissions Weakness | Defense Evasion, Persistence, Privilege Escalation | +| T1543.003 | Windows Service | Persistence, Privilege Escalation | #### Kill Chain Phase +* Actions on Objectives + +* Installation + #### Reference @@ -4023,10 +4406,9 @@ _version_: 3 ### Apache Struts Vulnerability Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Endpoint +- **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/) - **Last Updated**: 2018-12-06
@@ -4034,21 +4416,27 @@ Detect and investigate activities--such as unusually long `Content-Type` length, #### Detection Profile -* +* [Suspicious Java Classes](detections.md#suspicious-java-classes) -* +* [Unusually Long Content-Type Length](detections.md#unusually-long-content-type-length) -* +* [Web Servers Executing Suspicious Processes](detections.md#web-servers-executing-suspicious-processes) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1082 | System Information Discovery | Discovery | #### Kill Chain Phase +* Actions on Objectives + +* Delivery + +* Exploitation + #### Reference @@ -4063,10 +4451,9 @@ _version_: 1 ### JBoss Vulnerability In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Web +- **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/) - **Last Updated**: 2017-09-14
@@ -4074,19 +4461,23 @@ In March of 2016, adversaries were seen using JexBoss--an open-source utility us #### Detection Profile -* +* [Detect attackers scanning for vulnerable JBoss servers](detections.md#detect-attackers-scanning-for-vulnerable-jboss-servers) -* +* [Detect malicious requests to exploit JBoss servers](detections.md#detect-malicious-requests-to-exploit-jboss-servers) #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| - +| T1082 | System Information Discovery | Discovery | #### Kill Chain Phase +* Delivery + +* Reconnaissance + #### Reference @@ -4101,10 +4492,9 @@ _version_: 1 ### Spectre And Meltdown Vulnerabilities Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: Vulnerabilities +- **ATT&CK**: - **Last Updated**: 2018-01-08
@@ -4112,7 +4502,7 @@ Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploita #### Detection Profile -* +* [Spectre and Meltdown Vulnerable Systems](detections.md#spectre-and-meltdown-vulnerable-systems) #### ATT&CK @@ -4120,7 +4510,6 @@ Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploita | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase @@ -4137,10 +4526,9 @@ _version_: 1 ### Splunk Enterprise Vulnerability Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: - **Last Updated**: 2017-09-19
@@ -4148,7 +4536,7 @@ Keeping your Splunk deployment up to date is critical and may help you reduce th #### Detection Profile -* +* [Open Redirect in Splunk Web](detections.md#open-redirect-in-splunk-web) #### ATT&CK @@ -4156,9 +4544,10 @@ Keeping your Splunk deployment up to date is critical and may help you reduce th | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase +* Delivery + #### Reference @@ -4175,10 +4564,9 @@ _version_: 1 ### Splunk Enterprise Vulnerability CVE-2018-11409 Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. - - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: -- **ATT&CK**: +- **Datamodel**: +- **ATT&CK**: - **Last Updated**: 2018-06-14
@@ -4186,7 +4574,7 @@ Reduce the risk of CVE-2018-11409, an information disclosure vulnerability withi #### Detection Profile -* +* [Splunk Enterprise Information Disclosure](detections.md#splunk-enterprise-information-disclosure) #### ATT&CK @@ -4194,9 +4582,10 @@ Reduce the risk of CVE-2018-11409, an information disclosure vulnerability withi | ID | Technique | Tactic | | ----------- | ----------- |--------------| - #### Kill Chain Phase +* Delivery + #### Reference From dd80dae944b025461cd0a3bf9fda961a763500aa Mon Sep 17 00:00:00 2001 From: divious1 Date: Wed, 3 Mar 2021 20:44:55 -0500 Subject: [PATCH 14/62] adding splunk docs wiki --- bin/doc_gen.py | 20 +- bin/jinja2_templates/doc_stories_wiki.j2 | 57 + docs/detections.md | 116 +- docs/detections.wiki | 126 +- docs/stories.md | 2 +- docs/stories.wiki | 5692 ++++++++++++++++++++++ 6 files changed, 5881 insertions(+), 132 deletions(-) create mode 100644 bin/jinja2_templates/doc_stories_wiki.j2 create mode 100644 docs/stories.wiki diff --git a/bin/doc_gen.py b/bin/doc_gen.py index acb39d02dc..7cb43264cd 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -153,6 +153,15 @@ def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_de f.write(output) messages.append("doc_gen.py wrote {0} stories documentation in markdown to: {1}".format(len(stories),output_path)) + # write wikimarkup + template = j2_env.get_template('doc_stories_wiki.j2') + output_path = path.join(OUTPUT_DIR + '/stories.wiki') + output = template.render(categories=categories) + with open(output_path, 'w', encoding="utf-8") as f: + f.write(output) + messages.append("doc_gen.py wrote {0} stories documentation in mediawiki to: {1}".format(len(stories),output_path)) + return sorted_stories, messages + def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE): types = ["endpoint", "application", "cloud", "network", "web", "experimental", "deprecated"] @@ -260,19 +269,10 @@ if __name__ == "__main__": messages = [] if type == 'all': sorted_detections, messages = generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE) - generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE) + sorted_stories, messages = generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE) # print all the messages from generation for m in messages: print(m) print("finished successfully!") - -# stories = load_objects("stories/*.yml") -# detections = [] -# detections = load_objects("detections/*/*.yml") -# detections.extend(load_objects("detections/*/*/*.yml")) - - - #story_count, path = write_splunk_docs(stories, detections, OUTPUT_DIR) - #print("{0} story documents have been successfully written to {1}".format(story_count, path)) diff --git a/bin/jinja2_templates/doc_stories_wiki.j2 b/bin/jinja2_templates/doc_stories_wiki.j2 new file mode 100644 index 0000000000..ca074e92ba --- /dev/null +++ b/bin/jinja2_templates/doc_stories_wiki.j2 @@ -0,0 +1,57 @@ +=Splunk Security Content Analytic Story = + +---- +All the Analytic Stories shipped to different Splunk products. Below is a breakdown by Category. +{% for category in categories %} +=={{ category.name }}== + +{% for story in category.stories %} +==={{ story.name}}=== +{{ story.description }} + +* '''Product''': {{ story.tags.product|join(', ') }} +* '''Datamodel''': {{ story.data_models|join(', ') }} +* '''ATT&CK''': {% for mitre_attack_id in story.mitre_attack_ids %}[https://attack.mitre.org/techniques/{{ mitre_attack_id }}/ {{ mitre_attack_id }}]{% if not loop.last %}, {% endif %}{% endfor %} +* '''Last Updated''': {{ story.date }} + +
+
+ +====Detection Profile==== +{% for detection in story.detections %} +* [[Documentation:ESSOC:detections:Detections#{{ detection|replace(" ", "_") }}|{{ detection }}]] +{% endfor %} + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +{%-for attack in story.mitre_attacks %} +|- +| {{ attack.technique_id }} +| {{ attack.technique }} +| {{ attack.tactic|join(', ') }} +{%- endfor %} +|} + +====Kill Chain Phase==== +{% for phase in story.kill_chain_phases %} +* {{ phase }} +{% endfor %} + +====Reference==== +{% for reference in story.references %} +* {{ reference }} +{% endfor %} + +''version'': {{story.version}} +
+
+ +---- +{% endfor %} + +{% endfor %} + +[[Category:V:ESSOC:drafts]] diff --git a/docs/detections.md b/docs/detections.md index 3bb8737e21..494ab90ce8 100644 --- a/docs/detections.md +++ b/docs/detections.md @@ -6179,6 +6179,64 @@ _version_: 3 ### Attempted Credential Dump From Registry via Reg exe Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/) +- **Last Updated**: 2019-12-02 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` +``` +#### Associated Analytic Story + +* Credential Dumping + + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required field + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.002 | Security Account Manager | Credential Access | + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +None identified. + +#### Reference + + +#### Test Dataset + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + + +_version_: 4 +
+ +--- + +### Attempted Credential Dump From Registry via Reg exe +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + - **Product**: UEBA for Security Cloud - **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) @@ -6246,64 +6304,6 @@ _version_: 1 --- -### Attempted Credential Dump From Registry via Reg exe -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: Endpoint -- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/) -- **Last Updated**: 2019-12-02 - -
- details - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `attempted_credential_dump_from_registry_via_reg_exe_filter` -``` -#### Associated Analytic Story - -* Credential Dumping - - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required field - - -#### ATT&CK - -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| -| T1003.002 | Security Account Manager | Credential Access | - -#### Kill Chain Phase - -* Actions on Objectives - - -#### Known False Positives -None identified. - -#### Reference - - -#### Test Dataset - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - - -_version_: 4 -
- ---- - ### BCDEdit Failure Recovery Modification This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. diff --git a/docs/detections.wiki b/docs/detections.wiki index b095327c7a..d80f91a6c7 100644 --- a/docs/detections.wiki +++ b/docs/detections.wiki @@ -10256,6 +10256,69 @@ None identified. Attempts to disable security-related services should be identif ===Attempted Credential Dump From Registry via Reg exe=== Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.002/ T1003.002] +* '''Last Updated''': 2019-12-02 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.002 +| Security Account Manager +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +===Attempted Credential Dump From Registry via Reg exe=== +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + * '''Product''': UEBA for Security Cloud * '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] @@ -10328,69 +10391,6 @@ None identified. ---- -===Attempted Credential Dump From Registry via Reg exe=== -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.002/ T1003.002] -* '''Last Updated''': 2019-12-02 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `attempted_credential_dump_from_registry_via_reg_exe_filter` - -====Associated Analytic Story==== - -* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] - - -====How To Implement==== -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -====Required field==== - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.002 -| Security Account Manager -| Credential Access -|} - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -None identified. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - - -''version'': 4 -
-
- ----- - ===BCDEdit Failure Recovery Modification=== This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. diff --git a/docs/stories.md b/docs/stories.md index 014facc0eb..81fc5be9c0 100644 --- a/docs/stories.md +++ b/docs/stories.md @@ -590,8 +590,8 @@ Uncover activity consistent with credential dumping, a technique wherein attacke | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | | T1059.001 | PowerShell | Execution | -| T1003 | OS Credential Dumping | Credential Access | | T1003.002 | Security Account Manager | Credential Access | +| T1003 | OS Credential Dumping | Credential Access | | T1003.003 | NTDS | Credential Access | #### Kill Chain Phase diff --git a/docs/stories.wiki b/docs/stories.wiki new file mode 100644 index 0000000000..a561af72b9 --- /dev/null +++ b/docs/stories.wiki @@ -0,0 +1,5692 @@ +=Splunk Security Content Analytic Story = + +---- +All the Analytic Stories shipped to different Splunk products. Below is a breakdown by Category. + +==Abuse== + + +===Brand Monitoring=== +Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Email, Network_Resolution, Web +* '''ATT&CK''': +* '''Last Updated''': 2017-12-19 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Monitor_DNS_For_Brand_Abuse|Monitor DNS For Brand Abuse]] + +* [[Documentation:ESSOC:detections:Detections#Monitor_Email_For_Brand_Abuse|Monitor Email For Brand Abuse]] + +* [[Documentation:ESSOC:detections:Detections#Monitor_Web_Traffic_For_Brand_Abuse|Monitor Web Traffic For Brand Abuse]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Delivery + + +====Reference==== + +* https://www.zerofox.com/blog/what-is-digital-risk-monitoring/ + +* https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/ + +* https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/ + + +''version'': 1 +
+
+ +---- + +===DNS Amplification Attacks=== +DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1498.002/ T1498.002] +* '''Last Updated''': 2016-09-13 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Large_Volume_of_DNS_ANY_Queries|Large Volume of DNS ANY Queries]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1498.002 +| Reflection Amplification +| Impact +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://www.us-cert.gov/ncas/alerts/TA13-088A + +* https://www.imperva.com/learn/application-security/dns-amplification/ + + +''version'': 1 +
+
+ +---- + +===Data Protection=== +Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change_Analysis, Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''Last Updated''': 2017-09-14 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_USB_device_insertion|Detect USB device insertion]] + +* [[Documentation:ESSOC:detections:Detections#Detect_hosts_connecting_to_dynamic_domain_providers|Detect hosts connecting to dynamic domain providers]] + +* [[Documentation:ESSOC:detections:Detections#Detection_of_DNS_Tunnels|Detection of DNS Tunnels]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1189 +| Drive-by Compromise +| Initial Access +|- +| T1071.001 +| Web Protocols +| Command and Control +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|- +| T1048 +| Exfiltration Over Alternative Protocol +| Exfiltration +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Installation + + +====Reference==== + +* https://www.cisecurity.org/controls/data-protection/ + +* https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022 + +* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ + + +''version'': 1 +
+
+ +---- + +===Host Redirection=== +Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.004/ T1071.004] +* '''Last Updated''': 2017-09-14 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Clients_Connecting_to_Multiple_DNS_Servers|Clients Connecting to Multiple DNS Servers]] + +* [[Documentation:ESSOC:detections:Detections#DNS_Query_Requests_Resolved_by_Unauthorized_DNS_Servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] + +* [[Documentation:ESSOC:detections:Detections#Windows_hosts_file_modification|Windows hosts file modification]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|- +| T1071.004 +| DNS +| Command and Control +|- +| T1095 +| Non-Application Layer Protocol +| Command and Control +|- +| T1189 +| Drive-by Compromise +| Initial Access +|- +| T1048 +| Exfiltration Over Alternative Protocol +| Exfiltration +|- +| T1071.001 +| Web Protocols +| Command and Control +|} + +====Kill Chain Phase==== + +* Command and Control + + +====Reference==== + +* https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/ + + +''version'': 1 +
+
+ +---- + +===Netsh Abuse=== +Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.004/ T1562.004] +* '''Last Updated''': 2017-01-05 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Processes_created_by_netsh|Processes created by netsh]] + +* [[Documentation:ESSOC:detections:Detections#Processes_launching_netsh|Processes launching netsh]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.004 +| Disable or Modify System Firewall +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://docs.microsoft.com/en-us/previous-versions/tn-archive/bb490939(v=technet.10) + +* https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html + +* http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html + + +''version'': 1 +
+
+ +---- + +===Web Fraud Detection=== +Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1136/ T1136] +* '''Last Updated''': 2018-10-08 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Web_Fraud_-_Account_Harvesting|Web Fraud - Account Harvesting]] + +* [[Documentation:ESSOC:detections:Detections#Web_Fraud_-_Anomalous_User_Clickspeed|Web Fraud - Anomalous User Clickspeed]] + +* [[Documentation:ESSOC:detections:Detections#Web_Fraud_-_Password_Sharing_Across_Accounts|Web Fraud - Password Sharing Across Accounts]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136 +| Create Account +| Persistence +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud + +* https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718 + + +''version'': 1 +
+
+ +---- + + + +==Adversary Tactics== + + +===Baron Samedit CVE-2021-3156=== +Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Research Team, this vulnerability has been found to affect sudo across multiple Linux distributions (Ubuntu 20.04 and prior, Debian 10 and prior, Fedora 33 and prior). As this vulnerability was committed to code in July 2011, there will be many distributions affected. Successful exploitation of this vulnerability allows any unprivileged user to gain root privileges on the vulnerable host. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068] +* '''Last Updated''': 2021-01-27 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Baron_Samedit_CVE-2021-3156|Detect Baron Samedit CVE-2021-3156]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Baron_Samedit_CVE-2021-3156_Segfault|Detect Baron Samedit CVE-2021-3156 Segfault]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Baron_Samedit_CVE-2021-3156_via_OSQuery|Detect Baron Samedit CVE-2021-3156 via OSQuery]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Reference==== + +* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit + + +''version'': 1 +
+
+ +---- + +===Cobalt Strike=== +Cobalt Strike is threat emulation software. Red teams and penetration testers use Cobalt Strike to demonstrate the risk of a breach and evaluate mature security programs. Most recently, Cobalt Strike has become the choice tool by threat groups due to its ease of use and extensibility. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''Last Updated''': 2021-02-16 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_StartW|Suspicious Rundll32 StartW]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_no_CommandLine_Arguments|Suspicious Rundll32 no CommandLine Arguments]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://www.cobaltstrike.com/ + +* https://www.infocyte.com/blog/2020/09/02/cobalt-strike-the-new-favorite-among-thieves/ + +* https://bluescreenofjeff.com/2017-01-24-how-to-write-malleable-c2-profiles-for-cobalt-strike/ + +* https://blog.talosintelligence.com/2020/09/coverage-strikes-back-cobalt-strike-paper.html + +* https://www.fireeye.com/blog/threat-research/2020/12/unauthorized-access-of-fireeye-red-team-tools.html + + +''version'': 1 +
+
+ +---- + +===Collection and Staging=== +Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint, Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036/ T1036], [https://attack.mitre.org/techniques/T1114.001/ T1114.001], [https://attack.mitre.org/techniques/T1114.002/ T1114.002] +* '''Last Updated''': 2020-02-03 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Email_files_written_outside_of_the_Outlook_directory|Email files written outside of the Outlook directory]] + +* [[Documentation:ESSOC:detections:Detections#Email_servers_sending_high_volume_traffic_to_hosts|Email servers sending high volume traffic to hosts]] + +* [[Documentation:ESSOC:detections:Detections#Hosts_receiving_high_volume_of_network_traffic_from_email_server|Hosts receiving high volume of network traffic from email server]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_writes_to_System_Volume_Information|Suspicious writes to System Volume Information]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_writes_to_windows_Recycle_Bin|Suspicious writes to windows Recycle Bin]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.001 +| Local Email Collection +| Collection +|- +| T1114.002 +| Remote Email Collection +| Collection +|- +| T1036 +| Masquerading +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://attack.mitre.org/wiki/Collection + +* https://attack.mitre.org/wiki/Technique/T1074 + + +''version'': 1 +
+
+ +---- + +===Command and Control=== +Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution, Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1071.004/ T1071.004], [https://attack.mitre.org/techniques/T1095/ T1095], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''Last Updated''': 2018-06-01 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Clients_Connecting_to_Multiple_DNS_Servers|Clients Connecting to Multiple DNS Servers]] + +* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_Outliers_-_MLTK|DNS Query Length Outliers - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_With_High_Standard_Deviation|DNS Query Length With High Standard Deviation]] + +* [[Documentation:ESSOC:detections:Detections#DNS_Query_Requests_Resolved_by_Unauthorized_DNS_Servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Large_Outbound_ICMP_Packets|Detect Large Outbound ICMP Packets]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Long_DNS_TXT_Record_Response|Detect Long DNS TXT Record Response]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_blocked_Outbound_Traffic_from_your_AWS|Detect Spike in blocked Outbound Traffic from your AWS]] + +* [[Documentation:ESSOC:detections:Detections#Detect_hosts_connecting_to_dynamic_domain_providers|Detect hosts connecting to dynamic domain providers]] + +* [[Documentation:ESSOC:detections:Detections#Detection_of_DNS_Tunnels|Detection of DNS Tunnels]] + +* [[Documentation:ESSOC:detections:Detections#Excessive_DNS_Failures|Excessive DNS Failures]] + +* [[Documentation:ESSOC:detections:Detections#Prohibited_Network_Traffic_Allowed|Prohibited Network Traffic Allowed]] + +* [[Documentation:ESSOC:detections:Detections#Protocol_or_Port_Mismatch|Protocol or Port Mismatch]] + +* [[Documentation:ESSOC:detections:Detections#TOR_Traffic|TOR Traffic]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|- +| T1071.004 +| DNS +| Command and Control +|- +| T1095 +| Non-Application Layer Protocol +| Command and Control +|- +| T1189 +| Drive-by Compromise +| Initial Access +|- +| T1048 +| Exfiltration Over Alternative Protocol +| Exfiltration +|- +| T1071.001 +| Web Protocols +| Command and Control +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Delivery + + +====Reference==== + +* https://attack.mitre.org/wiki/Command_and_Control + +* https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware + + +''version'': 1 +
+
+ +---- + +===Common Phishing Frameworks=== +Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1566.003/ T1566.003] +* '''Last Updated''': 2019-04-29 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_DNS_requests_to_Phishing_Sites_leveraging_EvilGinx2|Detect DNS requests to Phishing Sites leveraging EvilGinx2]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566.003 +| Spearphishing via Service +| Initial Access +|} + +====Kill Chain Phase==== + +* Command and Control + +* Delivery + + +====Reference==== + +* https://github.com/kgretzky/evilginx2 + +* https://attack.mitre.org/techniques/T1192/ + +* https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/ + + +''version'': 1 +
+
+ +---- + +===Credential Dumping=== +Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003], [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1003.002/ T1003.002], [https://attack.mitre.org/techniques/T1003.003/ T1003.003], [https://attack.mitre.org/techniques/T1059.001/ T1059.001] +* '''Last Updated''': 2020-02-04 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Access_LSASS_Memory_for_Dump_Creation|Access LSASS Memory for Dump Creation]] + +* [[Documentation:ESSOC:detections:Detections#Attempt_To_Set_Default_PowerShell_Execution_Policy_To_Unrestricted_or_Bypass|Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass]] + +* [[Documentation:ESSOC:detections:Detections#Attempted_Credential_Dump_From_Registry_via_Reg_exe|Attempted Credential Dump From Registry via Reg exe]] + +* [[Documentation:ESSOC:detections:Detections#Create_Remote_Thread_into_LSASS|Create Remote Thread into LSASS]] + +* [[Documentation:ESSOC:detections:Detections#Creation_of_Shadow_Copy|Creation of Shadow Copy]] + +* [[Documentation:ESSOC:detections:Detections#Creation_of_Shadow_Copy_with_wmic_and_powershell|Creation of Shadow Copy with wmic and powershell]] + +* [[Documentation:ESSOC:detections:Detections#Creation_of_lsass_Dump_with_Taskmgr|Creation of lsass Dump with Taskmgr]] + +* [[Documentation:ESSOC:detections:Detections#Credential_Dumping_via_Copy_Command_from_Shadow_Copy|Credential Dumping via Copy Command from Shadow Copy]] + +* [[Documentation:ESSOC:detections:Detections#Credential_Dumping_via_Symlink_to_Shadow_Copy|Credential Dumping via Symlink to Shadow Copy]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Credential_Dumping_through_LSASS_access|Detect Credential Dumping through LSASS access]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Dump_LSASS_Memory_using_comsvcs|Detect Dump LSASS Memory using comsvcs]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Mimikatz_Using_Loaded_Images|Detect Mimikatz Using Loaded Images]] + +* [[Documentation:ESSOC:detections:Detections#Dump_LSASS_via_comsvcs_DLL|Dump LSASS via comsvcs DLL]] + +* [[Documentation:ESSOC:detections:Detections#Dump_LSASS_via_procdump|Dump LSASS via procdump]] + +* [[Documentation:ESSOC:detections:Detections#Dump_LSASS_via_procdump_Rename|Dump LSASS via procdump Rename]] + +* [[Documentation:ESSOC:detections:Detections#Ntdsutil_export_ntds|Ntdsutil export ntds]] + +* [[Documentation:ESSOC:detections:Detections#Unsigned_Image_Loaded_by_LSASS|Unsigned Image Loaded by LSASS]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|- +| T1059.001 +| PowerShell +| Execution +|- +| T1003.002 +| Security Account Manager +| Credential Access +|- +| T1003 +| OS Credential Dumping +| Credential Access +|- +| T1003.003 +| NTDS +| Credential Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Installation + + +====Reference==== + +* https://attack.mitre.org/wiki/Technique/T1003 + +* https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html + + +''version'': 3 +
+
+ +---- + +===DNS Hijacking=== +Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.004/ T1071.004], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''Last Updated''': 2020-02-04 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Clients_Connecting_to_Multiple_DNS_Servers|Clients Connecting to Multiple DNS Servers]] + +* [[Documentation:ESSOC:detections:Detections#DNS_Query_Requests_Resolved_by_Unauthorized_DNS_Servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] + +* [[Documentation:ESSOC:detections:Detections#DNS_record_changed|DNS record changed]] + +* [[Documentation:ESSOC:detections:Detections#Detect_hosts_connecting_to_dynamic_domain_providers|Detect hosts connecting to dynamic domain providers]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|- +| T1071.004 +| DNS +| Command and Control +|- +| T1095 +| Non-Application Layer Protocol +| Command and Control +|- +| T1189 +| Drive-by Compromise +| Initial Access +|- +| T1048 +| Exfiltration Over Alternative Protocol +| Exfiltration +|- +| T1071.001 +| Web Protocols +| Command and Control +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Reference==== + +* https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html + +* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ + +* http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ + +* https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html + + +''version'': 1 +
+
+ +---- + +===Data Exfiltration=== +The stealing of data by an adversary. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1041/ T1041] +* '''Last Updated''': 2020-10-21 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_SNICat_SNI_Exfiltration|Detect SNICat SNI Exfiltration]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1041 +| Exfiltration Over C2 Channel +| Exfiltration +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://attack.mitre.org/tactics/TA0010/ + + +''version'': 1 +
+
+ +---- + +===Detect Zerologon Attack=== +Uncover activity related to the execution of Zerologon CVE-2020-11472, a technique wherein attackers target a Microsoft Windows Domain Controller to reset its computer account password. The result from this attack is attackers can now provide themselves high privileges and take over Domain Controller. The included searches in this Analytic Story are designed to identify attempts to reset Domain Controller Computer Account via exploit code remotely or via the use of tool Mimikatz as payload carrier. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1190/ T1190], [https://attack.mitre.org/techniques/T1210/ T1210] +* '''Last Updated''': 2020-09-18 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Computer_Changed_with_Anonymous_Account|Detect Computer Changed with Anonymous Account]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Credential_Dumping_through_LSASS_access|Detect Credential Dumping through LSASS access]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Mimikatz_Using_Loaded_Images|Detect Mimikatz Using Loaded Images]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Zerologon_via_Zeek|Detect Zerologon via Zeek]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1210 +| Exploitation of Remote Services +| Lateral Movement +|- +| T1003.001 +| LSASS Memory +| Credential Access +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Exploitation + + +====Reference==== + +* https://attack.mitre.org/wiki/Technique/T1003 + +* https://github.com/SecuraBV/CVE-2020-1472 + +* https://www.secura.com/blog/zero-logon + +* https://nvd.nist.gov/vuln/detail/CVE-2020-1472 + + +''version'': 1 +
+
+ +---- + +===Disabling Security Tools=== +Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1112/ T1112], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1553.004/ T1553.004], [https://attack.mitre.org/techniques/T1562.001/ T1562.001], [https://attack.mitre.org/techniques/T1562.004/ T1562.004] +* '''Last Updated''': 2020-02-04 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Attempt_To_Add_Certificate_To_Untrusted_Store|Attempt To Add Certificate To Untrusted Store]] + +* [[Documentation:ESSOC:detections:Detections#Attempt_To_Stop_Security_Service|Attempt To Stop Security Service]] + +* [[Documentation:ESSOC:detections:Detections#Processes_launching_netsh|Processes launching netsh]] + +* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Reg_exe_Process|Suspicious Reg exe Process]] + +* [[Documentation:ESSOC:detections:Detections#Unload_Sysmon_Filter_Driver|Unload Sysmon Filter Driver]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1553.004 +| Install Root Certificate +| Defense Evasion +|- +| T1562.001 +| Disable or Modify Tools +| Defense Evasion +|- +| T1562.004 +| Disable or Modify System Firewall +| Defense Evasion +|- +| T1543.003 +| Windows Service +| Persistence, Privilege Escalation +|- +| T1112 +| Modify Registry +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Installation + + +====Reference==== + +* https://attack.mitre.org/wiki/Technique/T1089 + +* https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/ + +* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf + + +''version'': 2 +
+
+ +---- + +===F5 TMUI RCE CVE-2020-5902=== +Uncover activity consistent with CVE-2020-5902. Discovered by Positive Technologies researchers, this vulnerability affects F5 BIG-IP, BIG-IQ. and Traffix SDC devices (vulnerable versions in F5 support link below). This vulnerability allows unauthenticated users, along with authenticated users, who have access to the configuration utility to execute system commands, create/delete files, disable services, and/or execute Java code. This vulnerability can result in full system compromise. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1190/ T1190] +* '''Last Updated''': 2020-08-02 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_F5_TMUI_RCE_CVE-2020-5902|Detect F5 TMUI RCE CVE-2020-5902]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Reference==== + +* https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/ + +* https://support.f5.com/csp/article/K52145254 + +* https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/ + + +''version'': 1 +
+
+ +---- + +===Lateral Movement=== +Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint, Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1550.002/ T1550.002], [https://attack.mitre.org/techniques/T1558.003/ T1558.003] +* '''Last Updated''': 2020-02-04 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Activity_Related_to_Pass_the_Hash_Attacks|Detect Activity Related to Pass the Hash Attacks]] + +* [[Documentation:ESSOC:detections:Detections#Kerberoasting_spn_request_with_RC4_encryption|Kerberoasting spn request with RC4 encryption]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Traffic|Remote Desktop Network Traffic]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Process_Running_On_System|Remote Desktop Process Running On System]] + +* [[Documentation:ESSOC:detections:Detections#Schtasks_scheduling_job_on_remote_system|Schtasks scheduling job on remote system]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1550.002 +| Pass the Hash +| Defense Evasion, Lateral Movement +|- +| T1558.003 +| Kerberoasting +| Credential Access +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html + + +''version'': 2 +
+
+ +---- + +===Malicious PowerShell=== +Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1027/ T1027], [https://attack.mitre.org/techniques/T1059.001/ T1059.001] +* '''Last Updated''': 2017-08-23 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Attempt_To_Set_Default_PowerShell_Execution_Policy_To_Unrestricted_or_Bypass|Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass]] + +* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Connect_To_Internet_With_Hidden_Window|Malicious PowerShell Process - Connect To Internet With Hidden Window]] + +* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Encoded_Command|Malicious PowerShell Process - Encoded Command]] + +* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Multiple_Suspicious_Command-Line_Arguments|Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments]] + +* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_With_Obfuscation_Techniques|Malicious PowerShell Process With Obfuscation Techniques]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|- +| T1027 +| Obfuscated Files or Information +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Installation + + +====Reference==== + +* https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/ + +* https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ + + +''version'': 4 +
+
+ +---- + +===Phishing Payloads=== +Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1566.001/ T1566.001], [https://attack.mitre.org/techniques/T1566.002/ T1566.002] +* '''Last Updated''': 2019-04-29 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Oulook_exe_writing_a__zip_file|Detect Oulook exe writing a zip file]] + +* [[Documentation:ESSOC:detections:Detections#Process_Creating_LNK_file_in_Suspicious_Location|Process Creating LNK file in Suspicious Location]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566.001 +| Spearphishing Attachment +| Initial Access +|- +| T1566.002 +| Spearphishing Link +| Initial Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Installation + + +====Reference==== + +* https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html + + +''version'': 1 +
+
+ +---- + +===Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns=== +Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1547.001/ T1547.001] +* '''Last Updated''': 2020-01-22 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#First_time_seen_command_line_argument|First time seen command line argument]] + +* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Connect_To_Internet_With_Hidden_Window|Malicious PowerShell Process - Connect To Internet With Hidden Window]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line|Unusually Long Command Line]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line_-_MLTK|Unusually Long Command Line - MLTK]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|- +| T1059.003 +| Windows Command Shell +| Execution +|- +| T1547.001 +| Registry Run Keys / Startup Folder +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Reference==== + +* https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/ + +* http://blog.amossys.fr/badflick-is-not-so-bad.html + + +''version'': 1 +
+
+ +---- + +===SQL Injection=== +Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Web +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1190/ T1190] +* '''Last Updated''': 2017-09-19 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#SQL_Injection_with_Long_URLs|SQL Injection with Long URLs]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + +====Kill Chain Phase==== + +* Delivery + + +====Reference==== + +* https://capec.mitre.org/data/definitions/66.html + +* https://www.incapsula.com/web-application-security/sql-injection.html + + +''version'': 1 +
+
+ +---- + +===Sunburst Malware=== +Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and management software. It was discovered by FireEye in December 2020. The actors behind this campaign gained access to numerous public and private organizations around the world. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint, Network_Traffic, Web +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1018/ T1018], [https://attack.mitre.org/techniques/T1027/ T1027], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1071.002/ T1071.002], [https://attack.mitre.org/techniques/T1203/ T1203], [https://attack.mitre.org/techniques/T1505.003/ T1505.003], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1569.002/ T1569.002] +* '''Last Updated''': 2020-12-14 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Outbound_SMB_Traffic|Detect Outbound SMB Traffic]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Prohibited_Applications_Spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] + +* [[Documentation:ESSOC:detections:Detections#First_Time_Seen_Running_Windows_Service|First Time Seen Running Windows Service]] + +* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Encoded_Command|Malicious PowerShell Process - Encoded Command]] + +* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] + +* [[Documentation:ESSOC:detections:Detections#Scheduled_Task_Deleted_Or_Created_via_CMD|Scheduled Task Deleted Or Created via CMD]] + +* [[Documentation:ESSOC:detections:Detections#Schtasks_scheduling_job_on_remote_system|Schtasks scheduling job on remote system]] + +* [[Documentation:ESSOC:detections:Detections#Sunburst_Correlation_DLL_and_Network_Event|Sunburst Correlation DLL and Network Event]] + +* [[Documentation:ESSOC:detections:Detections#Supernova_Webshell|Supernova Webshell]] + +* [[Documentation:ESSOC:detections:Detections#TOR_Traffic|TOR Traffic]] + +* [[Documentation:ESSOC:detections:Detections#Windows_AdFind_Exe|Windows AdFind Exe]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.002 +| File Transfer Protocols +| Command and Control +|- +| T1059.003 +| Windows Command Shell +| Execution +|- +| T1569.002 +| Service Execution +| Execution +|- +| T1027 +| Obfuscated Files or Information +| Defense Evasion +|- +| T1543.003 +| Windows Service +| Persistence, Privilege Escalation +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|- +| T1203 +| Exploitation for Client Execution +| Execution +|- +| T1505.003 +| Web Shell +| Persistence +|- +| T1071.001 +| Web Protocols +| Command and Control +|- +| T1018 +| Remote System Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Exfiltration + +* Exploitation + +* Installation + + +====Reference==== + +* https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html + +* https://msrc-blog.microsoft.com/2020/12/13/customer-guidance-on-recent-nation-state-cyber-attacks/ + + +''version'': 1 +
+
+ +---- + +===Suspicious Command-Line Executions=== +Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003] +* '''Last Updated''': 2020-02-03 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Prohibited_Applications_Spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Use_of_cmd_exe_to_Launch_Script_Interpreters|Detect Use of cmd exe to Launch Script Interpreters]] + +* [[Documentation:ESSOC:detections:Detections#First_time_seen_command_line_argument|First time seen command line argument]] + +* [[Documentation:ESSOC:detections:Detections#System_Processes_Run_From_Unexpected_Locations|System Processes Run From Unexpected Locations]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line|Unusually Long Command Line]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line_-_MLTK|Unusually Long Command Line - MLTK]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.003 +| Windows Command Shell +| Execution +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|- +| T1059.001 +| PowerShell +| Execution +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Exploitation + + +====Reference==== + +* https://attack.mitre.org/wiki/Technique/T1059 + +* https://www.microsoft.com/en-us/wdsi/threats/macro-malware + +* https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf + + +''version'': 2 +
+
+ +---- + +===Suspicious Compiled HTML Activity=== +Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.001/ T1218.001] +* '''Last Updated''': 2021-02-11 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_HTML_Help_Renamed|Detect HTML Help Renamed]] + +* [[Documentation:ESSOC:detections:Detections#Detect_HTML_Help_Spawn_Child_Process|Detect HTML Help Spawn Child Process]] + +* [[Documentation:ESSOC:detections:Detections#Detect_HTML_Help_URL_in_Command_Line|Detect HTML Help URL in Command Line]] + +* [[Documentation:ESSOC:detections:Detections#Detect_HTML_Help_Using_InfoTech_Storage_Handlers|Detect HTML Help Using InfoTech Storage Handlers]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.001 +| Compiled HTML File +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +* https://attack.mitre.org/techniques/T1218/001/ + +* https://docs.microsoft.com/en-us/windows/win32/api/htmlhelp/nf-htmlhelp-htmlhelpa + + +''version'': 1 +
+
+ +---- + +===Suspicious DNS Traffic=== +Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.004/ T1071.004], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''Last Updated''': 2017-09-18 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Clients_Connecting_to_Multiple_DNS_Servers|Clients Connecting to Multiple DNS Servers]] + +* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_Outliers_-_MLTK|DNS Query Length Outliers - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_With_High_Standard_Deviation|DNS Query Length With High Standard Deviation]] + +* [[Documentation:ESSOC:detections:Detections#DNS_Query_Requests_Resolved_by_Unauthorized_DNS_Servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Long_DNS_TXT_Record_Response|Detect Long DNS TXT Record Response]] + +* [[Documentation:ESSOC:detections:Detections#Detect_hosts_connecting_to_dynamic_domain_providers|Detect hosts connecting to dynamic domain providers]] + +* [[Documentation:ESSOC:detections:Detections#Detection_of_DNS_Tunnels|Detection of DNS Tunnels]] + +* [[Documentation:ESSOC:detections:Detections#Excessive_DNS_Failures|Excessive DNS Failures]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|- +| T1071.004 +| DNS +| Command and Control +|- +| T1095 +| Non-Application Layer Protocol +| Command and Control +|- +| T1189 +| Drive-by Compromise +| Initial Access +|- +| T1048 +| Exfiltration Over Alternative Protocol +| Exfiltration +|- +| T1071.001 +| Web Protocols +| Command and Control +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Reference==== + +* http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/ + +* http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680 + +* https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454 + + +''version'': 1 +
+
+ +---- + +===Suspicious Emails=== +Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Email, UEBA +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1566/ T1566], [https://attack.mitre.org/techniques/T1566.001/ T1566.001] +* '''Last Updated''': 2020-01-27 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Email_Attachments_With_Lots_Of_Spaces|Email Attachments With Lots Of Spaces]] + +* [[Documentation:ESSOC:detections:Detections#Monitor_Email_For_Brand_Abuse|Monitor Email For Brand Abuse]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Email_-_UBA_Anomaly|Suspicious Email - UBA Anomaly]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Email_Attachment_Extensions|Suspicious Email Attachment Extensions]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566 +| Phishing +| Initial Access +|- +| T1566.001 +| Spearphishing Attachment +| Initial Access +|} + +====Kill Chain Phase==== + +* Delivery + + +====Reference==== + +* https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/ + + +''version'': 1 +
+
+ +---- + +===Suspicious MSHTA Activity=== +Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1218.005/ T1218.005], [https://attack.mitre.org/techniques/T1547.001/ T1547.001] +* '''Last Updated''': 2021-01-20 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_MSHTA_Url_in_Command_Line|Detect MSHTA Url in Command Line]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Prohibited_Applications_Spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Rundll32_Inline_HTA_Execution|Detect Rundll32 Inline HTA Execution]] + +* [[Documentation:ESSOC:detections:Detections#Detect_mshta_inline_hta_execution|Detect mshta inline hta execution]] + +* [[Documentation:ESSOC:detections:Detections#Detect_mshta_renamed|Detect mshta renamed]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_mshta_child_process|Suspicious mshta child process]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_mshta_spawn|Suspicious mshta spawn]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|- +| T1059.003 +| Windows Command Shell +| Execution +|- +| T1547.001 +| Registry Run Keys / Startup Folder +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Exploitation + + +====Reference==== + +* https://redcanary.com/blog/introducing-atomictestharnesses/ + +* https://redcanary.com/blog/windows-registry-attacks-threat-detection/ + +* https://attack.mitre.org/techniques/T1218/005/ + +* https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5 + + +''version'': 2 +
+
+ +---- + +===Suspicious Okta Activity=== +Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.001/ T1078.001] +* '''Last Updated''': 2020-04-02 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Multiple_Okta_Users_With_Invalid_Credentials_From_The_Same_IP|Multiple Okta Users With Invalid Credentials From The Same IP]] + +* [[Documentation:ESSOC:detections:Detections#Okta_Account_Lockout_Events|Okta Account Lockout Events]] + +* [[Documentation:ESSOC:detections:Detections#Okta_Failed_SSO_Attempts|Okta Failed SSO Attempts]] + +* [[Documentation:ESSOC:detections:Detections#Okta_User_Logins_From_Multiple_Cities|Okta User Logins From Multiple Cities]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Reference==== + +* https://attack.mitre.org/wiki/Technique/T1078 + +* https://owasp.org/www-community/attacks/Credential_stuffing + +* https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work + + +''version'': 1 +
+
+ +---- + +===Suspicious Regsvcs Regasm Activity=== +Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.009/ T1218.009] +* '''Last Updated''': 2021-02-11 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Regasm_Spawning_a_Process|Detect Regasm Spawning a Process]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Regasm_with_Network_Connection|Detect Regasm with Network Connection]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Regasm_with_no_Command_Line_Arguments|Detect Regasm with no Command Line Arguments]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Regsvcs_Spawning_a_Process|Detect Regsvcs Spawning a Process]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Regsvcs_with_Network_Connection|Detect Regsvcs with Network Connection]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Regsvcs_with_No_Command_Line_Arguments|Detect Regsvcs with No Command Line Arguments]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/009/ + +* https://github.com/rapid7/metasploit-framework/blob/master/documentation/modules/evasion/windows/applocker_evasion_regasm_regsvcs.md + +* https://oddvar.moe/2017/12/13/applocker-case-study-how-insecure-is-it-really-part-1/ + + +''version'': 1 +
+
+ +---- + +===Suspicious Regsvr32 Activity=== +Monitor and detect techniques used by attackers who leverage the regsvr32.exe process to execute malicious code. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.010/ T1218.010] +* '''Last Updated''': 2021-01-29 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Regsvr32_Application_Control_Bypass|Detect Regsvr32 Application Control Bypass]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Regsvr32_Register_Suspicious_Path|Suspicious Regsvr32 Register Suspicious Path]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.010 +| Regsvr32 +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/010/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md + +* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ + + +''version'': 1 +
+
+ +---- + +===Suspicious Rundll32 Activity=== +Monitor and detect techniques used by attackers who leverage rundll32.exe to execute arbitrary malicious code. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''Last Updated''': 2021-02-03 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Rundll32_Application_Control_Bypass_-_advpack|Detect Rundll32 Application Control Bypass - advpack]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Rundll32_Application_Control_Bypass_-_setupapi|Detect Rundll32 Application Control Bypass - setupapi]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Rundll32_Application_Control_Bypass_-_syssetup|Detect Rundll32 Application Control Bypass - syssetup]] + +* [[Documentation:ESSOC:detections:Detections#Dump_LSASS_via_comsvcs_DLL|Dump LSASS via comsvcs DLL]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_Rename|Suspicious Rundll32 Rename]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_StartW|Suspicious Rundll32 StartW]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_dllregisterserver|Suspicious Rundll32 dllregisterserver]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_no_CommandLine_Arguments|Suspicious Rundll32 no CommandLine Arguments]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|- +| T1003.001 +| LSASS Memory +| Credential Access +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://attack.mitre.org/techniques/T1218/011/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + +* https://lolbas-project.github.io/lolbas/Binaries/Rundll32 + + +''version'': 1 +
+
+ +---- + +===Suspicious WMI Use=== +Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1047/ T1047], [https://attack.mitre.org/techniques/T1546.003/ T1546.003] +* '''Last Updated''': 2018-10-23 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Process_Execution_via_WMI|Process Execution via WMI]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Process_Instantiation_via_WMI|Remote Process Instantiation via WMI]] + +* [[Documentation:ESSOC:detections:Detections#Remote_WMI_Command_Attempt|Remote WMI Command Attempt]] + +* [[Documentation:ESSOC:detections:Detections#Script_Execution_via_WMI|Script Execution via WMI]] + +* [[Documentation:ESSOC:detections:Detections#WMI_Permanent_Event_Subscription|WMI Permanent Event Subscription]] + +* [[Documentation:ESSOC:detections:Detections#WMI_Permanent_Event_Subscription_-_Sysmon|WMI Permanent Event Subscription - Sysmon]] + +* [[Documentation:ESSOC:detections:Detections#WMI_Temporary_Event_Subscription|WMI Temporary Event Subscription]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|- +| T1546.003 +| Windows Management Instrumentation Event Subscription +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf + +* https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html + + +''version'': 2 +
+
+ +---- + +===Suspicious Windows Registry Activities=== +Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.001/ T1546.001], [https://attack.mitre.org/techniques/T1546.011/ T1546.011], [https://attack.mitre.org/techniques/T1546.012/ T1546.012], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1547.010/ T1547.010], [https://attack.mitre.org/techniques/T1548.002/ T1548.002], [https://attack.mitre.org/techniques/T1564.001/ T1564.001] +* '''Last Updated''': 2018-05-31 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Disabling_Remote_User_Account_Control|Disabling Remote User Account Control]] + +* [[Documentation:ESSOC:detections:Detections#Monitor_Registry_Keys_for_Print_Monitors|Monitor Registry Keys for Print Monitors]] + +* [[Documentation:ESSOC:detections:Detections#Reg_exe_used_to_hide_files_directories_via_registry_keys|Reg exe used to hide files directories via registry keys]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Privilege_Escalation|Registry Keys Used For Privilege Escalation]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_for_Creating_SHIM_Databases|Registry Keys for Creating SHIM Databases]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Registry_Key_modifications|Remote Registry Key modifications]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Changes_to_File_Associations|Suspicious Changes to File Associations]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1548.002 +| Bypass User Account Control +| Defense Evasion, Privilege Escalation +|- +| T1222.001 +| Windows File and Directory Permissions Modification +| Defense Evasion +|- +| T1547.010 +| Port Monitors +| Persistence, Privilege Escalation +|- +| T1564.001 +| Hidden Files and Directories +| Defense Evasion +|- +| T1547.001 +| Registry Run Keys / Startup Folder +| Persistence, Privilege Escalation +|- +| T1546.012 +| Image File Execution Options Injection +| Persistence, Privilege Escalation +|- +| T1546.011 +| Application Shimming +| Persistence, Privilege Escalation +|- +| T1546.001 +| Change Default File Association +| Persistence, Privilege Escalation +|- +| T1112 +| Modify Registry +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://redcanary.com/blog/windows-registry-attacks-threat-detection/ + +* https://attack.mitre.org/wiki/Technique/T1112 + + +''version'': 1 +
+
+ +---- + +===Suspicious Zoom Child Processes=== +Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1068/ T1068] +* '''Last Updated''': 2020-04-13 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Prohibited_Applications_Spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] + +* [[Documentation:ESSOC:detections:Detections#First_Time_Seen_Child_Process_of_Zoom|First Time Seen Child Process of Zoom]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.003 +| Windows Command Shell +| Execution +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|- +| T1059.001 +| PowerShell +| Execution +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Exploitation + + +====Reference==== + +* https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/ + +* https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/ + + +''version'': 1 +
+
+ +---- + +===Trusted Developer Utilities Proxy Execution=== +Monitor and detect behaviors used by attackers who leverage trusted developer utilities to execute malicious code. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1127/ T1127], [https://attack.mitre.org/techniques/T1127, T1036.003/ T1127, T1036.003] +* '''Last Updated''': 2021-01-12 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Suspicious_microsoft_workflow_compiler_rename|Suspicious microsoft workflow compiler rename]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_microsoft_workflow_compiler_usage|Suspicious microsoft workflow compiler usage]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| +| +| +|- +| T1127 +| Trusted Developer Utilities Proxy Execution +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Reference==== + +* https://attack.mitre.org/techniques/T1127/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md + +* https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/ + + +''version'': 1 +
+
+ +---- + +===Trusted Developer Utilities Proxy Execution MSBuild=== +Monitor and detect techniques used by attackers who leverage the msbuild.exe process to execute malicious code. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1127.001/ T1127.001] +* '''Last Updated''': 2021-01-21 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Suspicious_MSBuild_Rename|Suspicious MSBuild Rename]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_MSBuild_Spawn|Suspicious MSBuild Spawn]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_msbuild_path|Suspicious msbuild path]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1127.001 +| MSBuild +| Defense Evasion +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Reference==== + +* https://attack.mitre.org/techniques/T1127/001/ + +* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + +* https://github.com/infosecn1nja/MaliciousMacroMSBuild + +* https://github.com/xorrior/RandomPS-Scripts/blob/master/Invoke-ExecuteMSBuild.ps1 + +* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + +* https://github.com/MHaggis/CBR-Queries/blob/master/msbuild.md + + +''version'': 1 +
+
+ +---- + +===Windows DNS SIGRed CVE-2020-1350=== +Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpoint researchers, this vulnerability affects Windows 2003 to 2019, and is triggered by a malicious DNS response (only affects DNS over TCP). An attacker can use the malicious payload to cause a buffer overflow on the vulnerable system, leading to compromise. The included searches in this Analytic Story are designed to identify the large response payload for SIG and KEY DNS records which can be used for the exploit. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1203/ T1203] +* '''Last Updated''': 2020-07-28 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Windows_DNS_SIGRed_via_Splunk_Stream|Detect Windows DNS SIGRed via Splunk Stream]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Windows_DNS_SIGRed_via_Zeek|Detect Windows DNS SIGRed via Zeek]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1203 +| Exploitation for Client Execution +| Execution +|} + +====Kill Chain Phase==== + +* Exploitation + + +====Reference==== + +* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/ + +* https://support.microsoft.com/en-au/help/4569509/windows-dns-server-remote-code-execution-vulnerability + + +''version'': 1 +
+
+ +---- + +===Windows Defense Evasion Tactics=== +Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1112/ T1112], [https://attack.mitre.org/techniques/T1222.001/ T1222.001], [https://attack.mitre.org/techniques/T1548.002/ T1548.002], [https://attack.mitre.org/techniques/T1564.001/ T1564.001] +* '''Last Updated''': 2018-05-31 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Disabling_Remote_User_Account_Control|Disabling Remote User Account Control]] + +* [[Documentation:ESSOC:detections:Detections#Hiding_Files_And_Directories_With_Attrib_exe|Hiding Files And Directories With Attrib exe]] + +* [[Documentation:ESSOC:detections:Detections#Reg_exe_used_to_hide_files_directories_via_registry_keys|Reg exe used to hide files directories via registry keys]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Registry_Key_modifications|Remote Registry Key modifications]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Reg_exe_Process|Suspicious Reg exe Process]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1548.002 +| Bypass User Account Control +| Defense Evasion, Privilege Escalation +|- +| T1222.001 +| Windows File and Directory Permissions Modification +| Defense Evasion +|- +| T1547.010 +| Port Monitors +| Persistence, Privilege Escalation +|- +| T1564.001 +| Hidden Files and Directories +| Defense Evasion +|- +| T1547.001 +| Registry Run Keys / Startup Folder +| Persistence, Privilege Escalation +|- +| T1546.012 +| Image File Execution Options Injection +| Persistence, Privilege Escalation +|- +| T1546.011 +| Application Shimming +| Persistence, Privilege Escalation +|- +| T1546.001 +| Change Default File Association +| Persistence, Privilege Escalation +|- +| T1112 +| Modify Registry +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://attack.mitre.org/wiki/Defense_Evasion + + +''version'': 1 +
+
+ +---- + +===Windows Log Manipulation=== +Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1070/ T1070], [https://attack.mitre.org/techniques/T1070.001/ T1070.001], [https://attack.mitre.org/techniques/T1490/ T1490] +* '''Last Updated''': 2017-09-12 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Deleting_Shadow_Copies|Deleting Shadow Copies]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_wevtutil_Usage|Suspicious wevtutil Usage]] + +* [[Documentation:ESSOC:detections:Detections#USN_Journal_Deletion|USN Journal Deletion]] + +* [[Documentation:ESSOC:detections:Detections#Windows_Event_Log_Cleared|Windows Event Log Cleared]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1490 +| Inhibit System Recovery +| Impact +|- +| T1070.001 +| Clear Windows Event Logs +| Defense Evasion +|- +| T1070 +| Indicator Removal on Host +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ + +* https://zeltser.com/security-incident-log-review-checklist/ + +* http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html + + +''version'': 2 +
+
+ +---- + +===Windows Persistence Techniques=== +Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1222.001/ T1222.001], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1546.011/ T1546.011], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1547.010/ T1547.010], [https://attack.mitre.org/techniques/T1564.001/ T1564.001], [https://attack.mitre.org/techniques/T1574.009/ T1574.009], [https://attack.mitre.org/techniques/T1574.011/ T1574.011] +* '''Last Updated''': 2018-05-31 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Certutil_exe_certificate_extraction|Certutil exe certificate extraction]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Path_Interception_By_Creation_Of_program_exe|Detect Path Interception By Creation Of program exe]] + +* [[Documentation:ESSOC:detections:Detections#Hiding_Files_And_Directories_With_Attrib_exe|Hiding Files And Directories With Attrib exe]] + +* [[Documentation:ESSOC:detections:Detections#Monitor_Registry_Keys_for_Print_Monitors|Monitor Registry Keys for Print Monitors]] + +* [[Documentation:ESSOC:detections:Detections#Reg_exe_Manipulating_Windows_Services_Registry_Keys|Reg exe Manipulating Windows Services Registry Keys]] + +* [[Documentation:ESSOC:detections:Detections#Reg_exe_used_to_hide_files_directories_via_registry_keys|Reg exe used to hide files directories via registry keys]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_for_Creating_SHIM_Databases|Registry Keys for Creating SHIM Databases]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Registry_Key_modifications|Remote Registry Key modifications]] + +* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] + +* [[Documentation:ESSOC:detections:Detections#Schtasks_used_for_forcing_a_reboot|Schtasks used for forcing a reboot]] + +* [[Documentation:ESSOC:detections:Detections#Shim_Database_File_Creation|Shim Database File Creation]] + +* [[Documentation:ESSOC:detections:Detections#Shim_Database_Installation_With_Suspicious_Parameters|Shim Database Installation With Suspicious Parameters]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1574.009 +| Path Interception by Unquoted Path +| Defense Evasion, Persistence, Privilege Escalation +|- +| T1222.001 +| Windows File and Directory Permissions Modification +| Defense Evasion +|- +| T1547.010 +| Port Monitors +| Persistence, Privilege Escalation +|- +| T1574.011 +| Services Registry Permissions Weakness +| Defense Evasion, Persistence, Privilege Escalation +|- +| T1564.001 +| Hidden Files and Directories +| Defense Evasion +|- +| T1547.001 +| Registry Run Keys / Startup Folder +| Persistence, Privilege Escalation +|- +| T1546.011 +| Application Shimming +| Persistence, Privilege Escalation +|- +| T1543.003 +| Windows Service +| Persistence, Privilege Escalation +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Installation + + +====Reference==== + +* http://www.fuzzysecurity.com/tutorials/19.html + +* https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html + +* http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/ + +* https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html + +* https://www.youtube.com/watch?v=dq2Hv7J9fvk + + +''version'': 2 +
+
+ +---- + +===Windows Privilege Escalation=== +Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1546.008/ T1546.008], [https://attack.mitre.org/techniques/T1546.012/ T1546.012] +* '''Last Updated''': 2020-02-04 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Child_Processes_of_Spoolsv_exe|Child Processes of Spoolsv exe]] + +* [[Documentation:ESSOC:detections:Detections#Overwriting_Accessibility_Binaries|Overwriting Accessibility Binaries]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Privilege_Escalation|Registry Keys Used For Privilege Escalation]] + +* [[Documentation:ESSOC:detections:Detections#Uncommon_Processes_On_Endpoint|Uncommon Processes On Endpoint]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|- +| T1546.008 +| Accessibility Features +| Persistence, Privilege Escalation +|- +| T1546.012 +| Image File Execution Options Injection +| Persistence, Privilege Escalation +|- +| T1204.002 +| Malicious File +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Exploitation + + +====Reference==== + +* https://attack.mitre.org/tactics/TA0004/ + + +''version'': 2 +
+
+ +---- + + + +==Best Practices== + + +===Asset Tracking=== +Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Sessions +* '''ATT&CK''': +* '''Last Updated''': 2017-09-13 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Unauthorized_Assets_by_MAC_address|Detect Unauthorized Assets by MAC address]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Delivery + +* Reconnaissance + + +====Reference==== + +* https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/ + + +''version'': 1 +
+
+ +---- + +===Monitor Backup Solution=== +Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2017-09-12 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Extended_Period_Without_Successful_Netbackup_Backups|Extended Period Without Successful Netbackup Backups]] + +* [[Documentation:ESSOC:detections:Detections#Unsuccessful_Netbackup_backups|Unsuccessful Netbackup backups]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Reference==== + +* https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/ + + +''version'': 1 +
+
+ +---- + +===Monitor for Unauthorized Software=== +Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': +* '''Last Updated''': 2017-09-15 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Prohibited_Software_On_Endpoint|Prohibited Software On Endpoint]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Installation + + +====Reference==== + +* https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ + + +''version'': 1 +
+
+ +---- + +===Monitor for Updates=== +Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Updates +* '''ATT&CK''': +* '''Last Updated''': 2017-09-15 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#No_Windows_Updates_in_a_time_frame|No Windows Updates in a time frame]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Reference==== + +* https://learn.cisecurity.org/20-controls-download + + +''version'': 1 +
+
+ +---- + +===Prohibited Traffic Allowed or Protocol Mismatch=== +Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution, Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''Last Updated''': 2017-09-11 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_hosts_connecting_to_dynamic_domain_providers|Detect hosts connecting to dynamic domain providers]] + +* [[Documentation:ESSOC:detections:Detections#Prohibited_Network_Traffic_Allowed|Prohibited Network Traffic Allowed]] + +* [[Documentation:ESSOC:detections:Detections#Protocol_or_Port_Mismatch|Protocol or Port Mismatch]] + +* [[Documentation:ESSOC:detections:Detections#TOR_Traffic|TOR Traffic]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1189 +| Drive-by Compromise +| Initial Access +|- +| T1071.001 +| Web Protocols +| Command and Control +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|- +| T1048 +| Exfiltration Over Alternative Protocol +| Exfiltration +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Delivery + + +====Reference==== + +* http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/ + + +''version'': 1 +
+
+ +---- + +===Router and Infrastructure Security=== +Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication, Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1020.001/ T1020.001], [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1542.005/ T1542.005], [https://attack.mitre.org/techniques/T1557/ T1557], [https://attack.mitre.org/techniques/T1557.002/ T1557.002] +* '''Last Updated''': 2017-09-12 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_ARP_Poisoning|Detect ARP Poisoning]] + +* [[Documentation:ESSOC:detections:Detections#Detect_IPv6_Network_Infrastructure_Threats|Detect IPv6 Network Infrastructure Threats]] + +* [[Documentation:ESSOC:detections:Detections#Detect_New_Login_Attempts_to_Routers|Detect New Login Attempts to Routers]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Port_Security_Violation|Detect Port Security Violation]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Rogue_DHCP_Server|Detect Rogue DHCP Server]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Software_Download_To_Network_Device|Detect Software Download To Network Device]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Traffic_Mirroring|Detect Traffic Mirroring]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1200 +| Hardware Additions +| Initial Access +|- +| T1498 +| Network Denial of Service +| Impact +|- +| T1557.002 +| ARP Cache Poisoning +| Collection, Credential Access +|- +| T1557 +| Man-in-the-Middle +| Collection, Credential Access +|- +| T1542.005 +| TFTP Boot +| Defense Evasion, Persistence +|- +| T1020.001 +| Traffic Duplication +| Exfiltration +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Delivery + +* Exploitation + +* Reconnaissance + + +====Reference==== + +* https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html + +* https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html + + +''version'': 1 +
+
+ +---- + +===Use of Cleartext Protocols=== +Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Traffic +* '''ATT&CK''': +* '''Last Updated''': 2017-09-15 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Protocols_passing_authentication_in_cleartext|Protocols passing authentication in cleartext]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Reconnaissance + + +====Reference==== + +* https://www.monkey.org/~dugsong/dsniff/ + + +''version'': 1 +
+
+ +---- + + + +==Cloud Security== + + +===AWS Cross Account Activity=== +Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1550/ T1550] +* '''Last Updated''': 2018-06-04 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#aws_detect_attach_to_role_policy|aws detect attach to role policy]] + +* [[Documentation:ESSOC:detections:Detections#aws_detect_permanent_key_creation|aws detect permanent key creation]] + +* [[Documentation:ESSOC:detections:Detections#aws_detect_role_creation|aws detect role creation]] + +* [[Documentation:ESSOC:detections:Detections#aws_detect_sts_assume_role_abuse|aws detect sts assume role abuse]] + +* [[Documentation:ESSOC:detections:Detections#aws_detect_sts_get_session_token_abuse|aws detect sts get session token abuse]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1550 +| Use Alternate Authentication Material +| Defense Evasion, Lateral Movement +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Reference==== + +* https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ + + +''version'': 1 +
+
+ +---- + +===AWS Cryptomining=== +Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004], [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2018-03-08 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Launched_by_User|Abnormally High AWS Instances Launched by User]] + +* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Launched_by_User_-_MLTK|Abnormally High AWS Instances Launched by User - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_In_Previously_Unseen_Region|EC2 Instance Started In Previously Unseen Region]] + +* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_With_Previously_Unseen_AMI|EC2 Instance Started With Previously Unseen AMI]] + +* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_With_Previously_Unseen_Instance_Type|EC2 Instance Started With Previously Unseen Instance Type]] + +* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_With_Previously_Unseen_User|EC2 Instance Started With Previously Unseen User]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +''version'': 1 +
+
+ +---- + +===AWS Network ACL Activity=== +Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1562.007/ T1562.007] +* '''Last Updated''': 2018-05-21 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#AWS_Network_Access_Control_List_Created_with_All_Open_Ports|AWS Network Access Control List Created with All Open Ports]] + +* [[Documentation:ESSOC:detections:Detections#AWS_Network_Access_Control_List_Deleted|AWS Network Access Control List Deleted]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_Network_ACL_Activity|Detect Spike in Network ACL Activity]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_blocked_Outbound_Traffic_from_your_AWS|Detect Spike in blocked Outbound Traffic from your AWS]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Reference==== + +* https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html + +* https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/ + + +''version'': 2 +
+
+ +---- + +===AWS Security Hub Alerts=== +This story is focused around detecting Security Hub alerts generated from AWS + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-08-04 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_AWS_Security_Hub_Alerts_for_EC2_Instance|Detect Spike in AWS Security Hub Alerts for EC2 Instance]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_AWS_Security_Hub_Alerts_for_User|Detect Spike in AWS Security Hub Alerts for User]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Reference==== + +* https://aws.amazon.com/security-hub/features/ + + +''version'': 1 +
+
+ +---- + +===AWS Suspicious Provisioning Activities=== +Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2018-03-16 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#AWS_Cloud_Provisioning_From_Previously_Unseen_City|AWS Cloud Provisioning From Previously Unseen City]] + +* [[Documentation:ESSOC:detections:Detections#AWS_Cloud_Provisioning_From_Previously_Unseen_Country|AWS Cloud Provisioning From Previously Unseen Country]] + +* [[Documentation:ESSOC:detections:Detections#AWS_Cloud_Provisioning_From_Previously_Unseen_IP_Address|AWS Cloud Provisioning From Previously Unseen IP Address]] + +* [[Documentation:ESSOC:detections:Detections#AWS_Cloud_Provisioning_From_Previously_Unseen_Region|AWS Cloud Provisioning From Previously Unseen Region]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + + +====Reference==== + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +''version'': 1 +
+
+ +---- + +===AWS User Monitoring=== +Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2018-03-12 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_API_activity_from_users_without_MFA|Detect API activity from users without MFA]] + +* [[Documentation:ESSOC:detections:Detections#Detect_AWS_API_Activities_From_Unapproved_Accounts|Detect AWS API Activities From Unapproved Accounts]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_AWS_API_Activity|Detect Spike in AWS API Activity]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_Security_Group_Activity|Detect Spike in Security Group Activity]] + +* [[Documentation:ESSOC:detections:Detections#Detect_new_API_calls_from_user_roles|Detect new API calls from user roles]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +* https://redlock.io/blog/cryptojacking-tesla + + +''version'': 1 +
+
+ +---- + +===Cloud Cryptomining=== +Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004], [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2019-10-02 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Abnormally_High_Number_Of_Cloud_Instances_Launched|Abnormally High Number Of Cloud Instances Launched]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_Compute_Instance_Created_By_Previously_Unseen_User|Cloud Compute Instance Created By Previously Unseen User]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_Compute_Instance_Created_In_Previously_Unused_Region|Cloud Compute Instance Created In Previously Unused Region]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_Compute_Instance_Created_With_Previously_Unseen_Image|Cloud Compute Instance Created With Previously Unseen Image]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_Compute_Instance_Created_With_Previously_Unseen_Instance_Type|Cloud Compute Instance Created With Previously Unseen Instance Type]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +''version'': 1 +
+
+ +---- + +===Cloud Federated Credential Abuse=== +This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1136.003/ T1136.003], [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1546.012/ T1546.012], [https://attack.mitre.org/techniques/T1556/ T1556] +* '''Last Updated''': 2021-01-26 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#AWS_SAML_Access_by_Provider_User_and_Principal|AWS SAML Access by Provider User and Principal]] + +* [[Documentation:ESSOC:detections:Detections#AWS_SAML_Update_identity_provider|AWS SAML Update identity provider]] + +* [[Documentation:ESSOC:detections:Detections#Certutil_exe_certificate_extraction|Certutil exe certificate extraction]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Mimikatz_Using_Loaded_Images|Detect Mimikatz Using Loaded Images]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Mimikatz_Via_PowerShell_And_EventCode_4703|Detect Mimikatz Via PowerShell And EventCode 4703]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Rare_Executables|Detect Rare Executables]] + +* [[Documentation:ESSOC:detections:Detections#O365_Add_App_Role_Assignment_Grant_User|O365 Add App Role Assignment Grant User]] + +* [[Documentation:ESSOC:detections:Detections#O365_Added_Service_Principal|O365 Added Service Principal]] + +* [[Documentation:ESSOC:detections:Detections#O365_Excessive_SSO_logon_errors|O365 Excessive SSO logon errors]] + +* [[Documentation:ESSOC:detections:Detections#O365_New_Federated_Domain_Added|O365 New Federated Domain Added]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Privilege_Escalation|Registry Keys Used For Privilege Escalation]] + +* [[Documentation:ESSOC:detections:Detections#Uncommon_Processes_On_Endpoint|Uncommon Processes On Endpoint]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1003.001 +| LSASS Memory +| Credential Access +|- +| T1136.003 +| Cloud Account +| Persistence +|- +| T1556 +| Modify Authentication Process +| Credential Access, Defense Evasion +|- +| T1546.012 +| Image File Execution Options Injection +| Persistence, Privilege Escalation +|- +| T1204.002 +| Malicious File +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objective + +* Actions on Objectives + +* Command and Control + +* Installation + + +====Reference==== + +* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + +* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + +* https://us-cert.cisa.gov/ncas/alerts/aa21-008a + + +''version'': 1 +
+
+ +---- + +===Container Implantation Monitoring and Investigation=== +Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1525/ T1525] +* '''Last Updated''': 2020-02-20 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#GCP_GCR_container_uploaded|GCP GCR container uploaded]] + +* [[Documentation:ESSOC:detections:Detections#New_container_uploaded_to_AWS_ECR|New container uploaded to AWS ECR]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1525 +| Implant Container Image +| Persistence +|} + +====Kill Chain Phase==== + + +====Reference==== + +* https://github.com/splunk/cloud-datamodel-security-research + + +''version'': 1 +
+
+ +---- + +===GCP Cross Account Activity=== +Track when a user assumes an IAM role in another GCP account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2020-09-01 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#GCP_Detect_accounts_with_high_risk_roles_by_project|GCP Detect accounts with high risk roles by project]] + +* [[Documentation:ESSOC:detections:Detections#GCP_Detect_gcploit_framework|GCP Detect gcploit framework]] + +* [[Documentation:ESSOC:detections:Detections#GCP_Detect_high_risk_permissions_by_resource_and_account|GCP Detect high risk permissions by resource and account]] + +* [[Documentation:ESSOC:detections:Detections#gcp_detect_oauth_token_abuse|gcp detect oauth token abuse]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Reference==== + +* https://cloud.google.com/iam/docs/understanding-service-accounts + + +''version'': 1 +
+
+ +---- + +===Kubernetes Scanning Activity=== +This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1526/ T1526] +* '''Last Updated''': 2020-04-15 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Amazon_EKS_Kubernetes_Pod_scan_detection|Amazon EKS Kubernetes Pod scan detection]] + +* [[Documentation:ESSOC:detections:Detections#Amazon_EKS_Kubernetes_cluster_scan_detection|Amazon EKS Kubernetes cluster scan detection]] + +* [[Documentation:ESSOC:detections:Detections#GCP_Kubernetes_cluster_pod_scan_detection|GCP Kubernetes cluster pod scan detection]] + +* [[Documentation:ESSOC:detections:Detections#GCP_Kubernetes_cluster_scan_detection|GCP Kubernetes cluster scan detection]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_pod_scan_fingerprint|Kubernetes Azure pod scan fingerprint]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_scan_fingerprint|Kubernetes Azure scan fingerprint]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Reconnaissance + + +====Reference==== + +* https://github.com/splunk/cloud-datamodel-security-research + + +''version'': 1 +
+
+ +---- + +===Kubernetes Sensitive Object Access Activity=== +This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-05-20 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#AWS_EKS_Kubernetes_cluster_sensitive_object_access|AWS EKS Kubernetes cluster sensitive object access]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_AWS_detect_service_accounts_forbidden_failure_access|Kubernetes AWS detect service accounts forbidden failure access]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_AWS_detect_suspicious_kubectl_calls|Kubernetes AWS detect suspicious kubectl calls]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_sensitive_object_access|Kubernetes Azure detect sensitive object access]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_service_accounts_forbidden_failure_access|Kubernetes Azure detect service accounts forbidden failure access]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_suspicious_kubectl_calls|Kubernetes Azure detect suspicious kubectl calls]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_sensitive_object_access|Kubernetes GCP detect sensitive object access]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_service_accounts_forbidden_failure_access|Kubernetes GCP detect service accounts forbidden failure access]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_suspicious_kubectl_calls|Kubernetes GCP detect suspicious kubectl calls]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Reference==== + +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + + +''version'': 1 +
+
+ +---- + +===Kubernetes Sensitive Role Activity=== +This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2020-05-20 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_AWS_detect_RBAC_authorization_by_account|Kubernetes AWS detect RBAC authorization by account]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_AWS_detect_most_active_service_accounts_by_pod|Kubernetes AWS detect most active service accounts by pod]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_AWS_detect_sensitive_role_access|Kubernetes AWS detect sensitive role access]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_RBAC_authorization_by_account|Kubernetes Azure detect RBAC authorization by account]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_most_active_service_accounts_by_pod_namespace|Kubernetes Azure detect most active service accounts by pod namespace]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_sensitive_role_access|Kubernetes Azure detect sensitive role access]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_RBAC_authorizations_by_account|Kubernetes GCP detect RBAC authorizations by account]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_most_active_service_accounts_by_pod|Kubernetes GCP detect most active service accounts by pod]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_sensitive_role_access|Kubernetes GCP detect sensitive role access]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Lateral Movement + + +====Reference==== + +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + + +''version'': 1 +
+
+ +---- + +===Office 365 Detections=== +This story is focused around detecting Office 365 Attacks. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1110/ T1110], [https://attack.mitre.org/techniques/T1110.001/ T1110.001], [https://attack.mitre.org/techniques/T1114/ T1114], [https://attack.mitre.org/techniques/T1114.002/ T1114.002], [https://attack.mitre.org/techniques/T1114.003/ T1114.003], [https://attack.mitre.org/techniques/T1136.003/ T1136.003], [https://attack.mitre.org/techniques/T1556/ T1556], [https://attack.mitre.org/techniques/T1562.007/ T1562.007] +* '''Last Updated''': 2020-12-16 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#High_Number_of_Login_Failures_from_a_single_source|High Number of Login Failures from a single source]] + +* [[Documentation:ESSOC:detections:Detections#O365_Add_App_Role_Assignment_Grant_User|O365 Add App Role Assignment Grant User]] + +* [[Documentation:ESSOC:detections:Detections#O365_Added_Service_Principal|O365 Added Service Principal]] + +* [[Documentation:ESSOC:detections:Detections#O365_Bypass_MFA_via_Trusted_IP|O365 Bypass MFA via Trusted IP]] + +* [[Documentation:ESSOC:detections:Detections#O365_Disable_MFA|O365 Disable MFA]] + +* [[Documentation:ESSOC:detections:Detections#O365_Excessive_Authentication_Failures_Alert|O365 Excessive Authentication Failures Alert]] + +* [[Documentation:ESSOC:detections:Detections#O365_Excessive_SSO_logon_errors|O365 Excessive SSO logon errors]] + +* [[Documentation:ESSOC:detections:Detections#O365_New_Federated_Domain_Added|O365 New Federated Domain Added]] + +* [[Documentation:ESSOC:detections:Detections#O365_PST_export_alert|O365 PST export alert]] + +* [[Documentation:ESSOC:detections:Detections#O365_Suspicious_Admin_Email_Forwarding|O365 Suspicious Admin Email Forwarding]] + +* [[Documentation:ESSOC:detections:Detections#O365_Suspicious_Rights_Delegation|O365 Suspicious Rights Delegation]] + +* [[Documentation:ESSOC:detections:Detections#O365_Suspicious_User_Email_Forwarding|O365 Suspicious User Email Forwarding]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1110.001 +| Password Guessing +| Credential Access +|- +| T1136.003 +| Cloud Account +| Persistence +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|- +| T1556 +| Modify Authentication Process +| Credential Access, Defense Evasion +|- +| T1110 +| Brute Force +| Credential Access +|- +| T1114 +| Email Collection +| Collection +|- +| T1114.003 +| Email Forwarding Rule +| Collection +|- +| T1114.002 +| Remote Email Collection +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objective + +* Actions on Objectives + +* Not Applicable + + +====Reference==== + +* https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf + + +''version'': 1 +
+
+ +---- + +===Suspicious AWS EC2 Activities=== +Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004], [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2018-02-09 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Launched_by_User|Abnormally High AWS Instances Launched by User]] + +* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Launched_by_User_-_MLTK|Abnormally High AWS Instances Launched by User - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Terminated_by_User|Abnormally High AWS Instances Terminated by User]] + +* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Terminated_by_User_-_MLTK|Abnormally High AWS Instances Terminated by User - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_In_Previously_Unseen_Region|EC2 Instance Started In Previously Unseen Region]] + +* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_With_Previously_Unseen_User|EC2 Instance Started With Previously Unseen User]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +''version'': 1 +
+
+ +---- + +===Suspicious AWS Login Activities=== +Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004], [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2019-05-01 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_City|Detect AWS Console Login by User from New City]] + +* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_Country|Detect AWS Console Login by User from New Country]] + +* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_Region|Detect AWS Console Login by User from New Region]] + +* [[Documentation:ESSOC:detections:Detections#Detect_new_user_AWS_Console_Login|Detect new user AWS Console Login]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html + + +''version'': 1 +
+
+ +---- + +===Suspicious AWS S3 Activities=== +Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2018-07-24 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_New_Open_S3_Buckets_over_AWS_CLI|Detect New Open S3 Buckets over AWS CLI]] + +* [[Documentation:ESSOC:detections:Detections#Detect_New_Open_S3_buckets|Detect New Open S3 buckets]] + +* [[Documentation:ESSOC:detections:Detections#Detect_S3_access_from_a_new_IP|Detect S3 access from a new IP]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_S3_Bucket_deletion|Detect Spike in S3 Bucket deletion]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +* https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/ + + +''version'': 2 +
+
+ +---- + +===Suspicious AWS Traffic=== +Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2018-05-07 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_blocked_Outbound_Traffic_from_your_AWS|Detect Spike in blocked Outbound Traffic from your AWS]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Reference==== + +* https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/ + + +''version'': 1 +
+
+ +---- + +===Suspicious Cloud Authentication Activities=== +Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Authentication +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''Last Updated''': 2020-06-04 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#AWS_Cross_Account_Activity_From_Previously_Unseen_Account|AWS Cross Account Activity From Previously Unseen Account]] + +* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_New_User|Detect AWS Console Login by New User]] + +* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_City|Detect AWS Console Login by User from New City]] + +* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_Country|Detect AWS Console Login by User from New Country]] + +* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_Region|Detect AWS Console Login by User from New Region]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ + +* https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html + + +''version'': 1 +
+
+ +---- + +===Suspicious Cloud Instance Activities=== +Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-08-25 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Abnormally_High_Number_Of_Cloud_Instances_Destroyed|Abnormally High Number Of Cloud Instances Destroyed]] + +* [[Documentation:ESSOC:detections:Detections#Abnormally_High_Number_Of_Cloud_Instances_Launched|Abnormally High Number Of Cloud Instances Launched]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_Instance_Modified_By_Previously_Unseen_User|Cloud Instance Modified By Previously Unseen User]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +''version'': 1 +
+
+ +---- + +===Suspicious Cloud Provisioning Activities=== +Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078] +* '''Last Updated''': 2018-08-20 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Cloud_Provisioning_Activity_From_Previously_Unseen_City|Cloud Provisioning Activity From Previously Unseen City]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_Provisioning_Activity_From_Previously_Unseen_Country|Cloud Provisioning Activity From Previously Unseen Country]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_Provisioning_Activity_From_Previously_Unseen_IP_Address|Cloud Provisioning Activity From Previously Unseen IP Address]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_Provisioning_Activity_From_Previously_Unseen_Region|Cloud Provisioning Activity From Previously Unseen Region]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Reference==== + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +''version'': 1 +
+
+ +---- + +===Suspicious Cloud User Activities=== +Detect and investigate suspicious activities by users and roles in your cloud environments. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Change +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2020-09-04 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Abnormally_High_Number_Of_Cloud_Infrastructure_API_Calls|Abnormally High Number Of Cloud Infrastructure API Calls]] + +* [[Documentation:ESSOC:detections:Detections#Abnormally_High_Number_Of_Cloud_Security_Group_API_Calls|Abnormally High Number Of Cloud Security Group API Calls]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_API_Calls_From_Previously_Unseen_User_Roles|Cloud API Calls From Previously Unseen User Roles]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +* https://redlock.io/blog/cryptojacking-tesla + + +''version'': 1 +
+
+ +---- + +===Suspicious GCP Storage Activities=== +Use the searches in this Analytic Story to monitor your GCP Storage buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open storage buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1530/ T1530] +* '''Last Updated''': 2020-08-05 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_GCP_Storage_access_from_a_new_IP|Detect GCP Storage access from a new IP]] + +* [[Documentation:ESSOC:detections:Detections#Detect_New_Open_GCP_Storage_Buckets|Detect New Open GCP Storage Buckets]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://cloud.google.com/blog/product/gcp/4-steps-for-hardening-your-cloud-storage-buckets-taking-charge-of-your-security + +* https://rhinosecuritylabs.com/gcp/google-cloud-platform-gcp-bucket-enumeration/ + + +''version'': 1 +
+
+ +---- + +===Unusual AWS EC2 Modifications=== +Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''Last Updated''': 2018-04-09 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Modified_With_Previously_Unseen_User|EC2 Instance Modified With Previously Unseen User]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + + +====Reference==== + +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +''version'': 1 +
+
+ +---- + + + +==Malware== + + +===ColdRoot MacOS RAT=== +Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2019-01-09 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Osquery_pack_-_ColdRoot_detection|Osquery pack - ColdRoot detection]] + +* [[Documentation:ESSOC:detections:Detections#Processes_Tapping_Keyboard_Events|Processes Tapping Keyboard Events]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Command and Control + +* Installation + + +====Reference==== + +* https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/ + +* https://objective-see.com/blog/blog_0x2A.html + +* https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/ + + +''version'': 1 +
+
+ +---- + +===DHS Report TA18-074A=== +Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint, Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1071.002/ T1071.002], [https://attack.mitre.org/techniques/T1112/ T1112], [https://attack.mitre.org/techniques/T1136.001/ T1136.001], [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1562.004/ T1562.004] +* '''Last Updated''': 2020-01-22 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Create_local_admin_accounts_using_net_exe|Create local admin accounts using net exe]] + +* [[Documentation:ESSOC:detections:Detections#Detect_New_Local_Admin_account|Detect New Local Admin account]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Outbound_SMB_Traffic|Detect Outbound SMB Traffic]] + +* [[Documentation:ESSOC:detections:Detections#Detect_PsExec_With_accepteula_Flag|Detect PsExec With accepteula Flag]] + +* [[Documentation:ESSOC:detections:Detections#First_time_seen_command_line_argument|First time seen command line argument]] + +* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Execution_Policy_Bypass|Malicious PowerShell Process - Execution Policy Bypass]] + +* [[Documentation:ESSOC:detections:Detections#Processes_launching_netsh|Processes launching netsh]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] + +* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike|SMB Traffic Spike]] + +* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike_-_MLTK|SMB Traffic Spike - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] + +* [[Documentation:ESSOC:detections:Detections#Scheduled_Task_Deleted_Or_Created_via_CMD|Scheduled Task Deleted Or Created via CMD]] + +* [[Documentation:ESSOC:detections:Detections#Single_Letter_Process_On_Endpoint|Single Letter Process On Endpoint]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Reg_exe_Process|Suspicious Reg exe Process]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.001 +| Local Account +| Persistence +|- +| T1071.002 +| File Transfer Protocols +| Command and Control +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|- +| T1059.001 +| PowerShell +| Execution +|- +| T1059.003 +| Windows Command Shell +| Execution +|- +| T1562.004 +| Disable or Modify System Firewall +| Defense Evasion +|- +| T1547.001 +| Registry Run Keys / Startup Folder +| Persistence, Privilege Escalation +|- +| T1543.003 +| Windows Service +| Persistence, Privilege Escalation +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|- +| T1204.002 +| Malicious File +| Execution +|- +| T1112 +| Modify Registry +| Defense Evasion +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Installation + + +====Reference==== + +* https://www.us-cert.gov/ncas/alerts/TA18-074A + + +''version'': 2 +
+
+ +---- + +===Dynamic DNS=== +Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and deny lists. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Network_Resolution, Web +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''Last Updated''': 2018-09-06 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_hosts_connecting_to_dynamic_domain_providers|Detect hosts connecting to dynamic domain providers]] + +* [[Documentation:ESSOC:detections:Detections#Detect_web_traffic_to_dynamic_domain_providers|Detect web traffic to dynamic domain providers]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1189 +| Drive-by Compromise +| Initial Access +|- +| T1071.001 +| Web Protocols +| Command and Control +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|- +| T1048 +| Exfiltration Over Alternative Protocol +| Exfiltration +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Reference==== + +* https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html + +* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ + +* http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ + +* https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html + + +''version'': 2 +
+
+ +---- + +===Emotet Malware DHS Report TA18-201A === +Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Email, Endpoint, Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1072/ T1072], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1566.001/ T1566.001] +* '''Last Updated''': 2020-01-27 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Rare_Executables|Detect Rare Executables]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Use_of_cmd_exe_to_Launch_Script_Interpreters|Detect Use of cmd exe to Launch Script Interpreters]] + +* [[Documentation:ESSOC:detections:Detections#Detection_of_tools_built_by_NirSoft|Detection of tools built by NirSoft]] + +* [[Documentation:ESSOC:detections:Detections#Email_Attachments_With_Lots_Of_Spaces|Email Attachments With Lots Of Spaces]] + +* [[Documentation:ESSOC:detections:Detections#Prohibited_Software_On_Endpoint|Prohibited Software On Endpoint]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] + +* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike|SMB Traffic Spike]] + +* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike_-_MLTK|SMB Traffic Spike - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Email_Attachment_Extensions|Suspicious Email Attachment Extensions]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.003 +| Windows Command Shell +| Execution +|- +| T1072 +| Software Deployment Tools +| Execution, Lateral Movement +|- +| T1547.001 +| Registry Run Keys / Startup Folder +| Persistence, Privilege Escalation +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|- +| T1566.001 +| Spearphishing Attachment +| Initial Access +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Delivery + +* Exploitation + +* Installation + + +====Reference==== + +* https://www.us-cert.gov/ncas/alerts/TA18-201A + +* https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf + +* https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html + + +''version'': 1 +
+
+ +---- + +===Hidden Cobra Malware=== +Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint, Network_Resolution, Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1070.005/ T1070.005], [https://attack.mitre.org/techniques/T1071.002/ T1071.002], [https://attack.mitre.org/techniques/T1071.004/ T1071.004] +* '''Last Updated''': 2020-01-22 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Create_or_delete_windows_shares_using_net_exe|Create or delete windows shares using net exe]] + +* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_Outliers_-_MLTK|DNS Query Length Outliers - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_With_High_Standard_Deviation|DNS Query Length With High Standard Deviation]] + +* [[Documentation:ESSOC:detections:Detections#Detect_Outbound_SMB_Traffic|Detect Outbound SMB Traffic]] + +* [[Documentation:ESSOC:detections:Detections#First_time_seen_command_line_argument|First time seen command line argument]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Traffic|Remote Desktop Network Traffic]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Process_Running_On_System|Remote Desktop Process Running On System]] + +* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike|SMB Traffic Spike]] + +* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike_-_MLTK|SMB Traffic Spike - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_File_Write|Suspicious File Write]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070.005 +| Network Share Connection Removal +| Defense Evasion +|- +| T1071.004 +| DNS +| Command and Control +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|- +| T1071.002 +| File Transfer Protocols +| Command and Control +|- +| T1059.001 +| PowerShell +| Execution +|- +| T1059.003 +| Windows Command Shell +| Execution +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + + +====Reference==== + +* https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity + +* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf + + +''version'': 2 +
+
+ +---- + +===Orangeworm Attack Group=== +Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1569.002/ T1569.002] +* '''Last Updated''': 2020-01-22 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#First_Time_Seen_Running_Windows_Service|First Time Seen Running Windows Service]] + +* [[Documentation:ESSOC:detections:Detections#First_time_seen_command_line_argument|First time seen command line argument]] + +* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1569.002 +| Service Execution +| Execution +|- +| T1059.001 +| PowerShell +| Execution +|- +| T1059.003 +| Windows Command Shell +| Execution +|- +| T1574.011 +| Services Registry Permissions Weakness +| Defense Evasion, Persistence, Privilege Escalation +|- +| T1543.003 +| Windows Service +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Installation + + +====Reference==== + +* https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia + +* https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/ + + +''version'': 2 +
+
+ +---- + +===Ransomware=== +Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint, Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1047/ T1047], [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1070/ T1070], [https://attack.mitre.org/techniques/T1070.001/ T1070.001], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1485/ T1485], [https://attack.mitre.org/techniques/T1490/ T1490], [https://attack.mitre.org/techniques/T1547.001/ T1547.001] +* '''Last Updated''': 2020-02-04 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#BCDEdit_Failure_Recovery_Modification|BCDEdit Failure Recovery Modification]] + +* [[Documentation:ESSOC:detections:Detections#Common_Ransomware_Extensions|Common Ransomware Extensions]] + +* [[Documentation:ESSOC:detections:Detections#Common_Ransomware_Notes|Common Ransomware Notes]] + +* [[Documentation:ESSOC:detections:Detections#Deleting_Shadow_Copies|Deleting Shadow Copies]] + +* [[Documentation:ESSOC:detections:Detections#Prohibited_Network_Traffic_Allowed|Prohibited Network Traffic Allowed]] + +* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Process_Instantiation_via_WMI|Remote Process Instantiation via WMI]] + +* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike|SMB Traffic Spike]] + +* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike_-_MLTK|SMB Traffic Spike - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#Scheduled_tasks_used_in_BadRabbit_ransomware|Scheduled tasks used in BadRabbit ransomware]] + +* [[Documentation:ESSOC:detections:Detections#Schtasks_used_for_forcing_a_reboot|Schtasks used for forcing a reboot]] + +* [[Documentation:ESSOC:detections:Detections#Spike_in_File_Writes|Spike in File Writes]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_wevtutil_Usage|Suspicious wevtutil Usage]] + +* [[Documentation:ESSOC:detections:Detections#System_Processes_Run_From_Unexpected_Locations|System Processes Run From Unexpected Locations]] + +* [[Documentation:ESSOC:detections:Detections#TOR_Traffic|TOR Traffic]] + +* [[Documentation:ESSOC:detections:Detections#USN_Journal_Deletion|USN Journal Deletion]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line|Unusually Long Command Line]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line_-_MLTK|Unusually Long Command Line - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#WBAdmin_Delete_System_Backups|WBAdmin Delete System Backups]] + +* [[Documentation:ESSOC:detections:Detections#Windows_Event_Log_Cleared|Windows Event Log Cleared]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1490 +| Inhibit System Recovery +| Impact +|- +| T1485 +| Data Destruction +| Impact +|- +| T1482 +| Domain Trust Discovery +| Discovery +|- +| T1048 +| Exfiltration Over Alternative Protocol +| Exfiltration +|- +| T1547.001 +| Registry Run Keys / Startup Folder +| Persistence, Privilege Escalation +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|- +| T1047 +| Windows Management Instrumentation +| Execution +|- +| T1486 +| Data Encrypted for Impact +| Impact +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|- +| T1070.001 +| Clear Windows Event Logs +| Defense Evasion +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|- +| T1071.001 +| Web Protocols +| Command and Control +|- +| T1070 +| Indicator Removal on Host +| Defense Evasion +|- +| T1562.001 +| Disable or Modify Tools +| Defense Evasion +|- +| T1489 +| Service Stop +| Impact +|- +| T1059.003 +| Windows Command Shell +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Delivery + + +====Reference==== + +* https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/ + +* https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html + + +''version'': 1 +
+
+ +---- + +===Ransomware Cloud=== +Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware. These searches include cloud related objects that may be targeted by malicious actors via cloud providers own encryption features. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1486/ T1486] +* '''Last Updated''': 2020-10-27 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#AWS_Detect_Users_creating_keys_with_encrypt_policy_without_MFA|AWS Detect Users creating keys with encrypt policy without MFA]] + +* [[Documentation:ESSOC:detections:Detections#AWS_Detect_Users_with_KMS_keys_performing_encryption_S3|AWS Detect Users with KMS keys performing encryption S3]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1486 +| Data Encrypted for Impact +| Impact +|} + +====Kill Chain Phase==== + + +====Reference==== + +* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ + +* https://github.com/d1vious/git-wild-hunt + +* https://www.youtube.com/watch?v=PgzNib37g0M + + +''version'': 1 +
+
+ +---- + +===Ryuk Ransomware=== +Leverage searches that allow you to detect and investigate unusual activities that might relate to the Ryuk ransomware, including looking for file writes associated with Ryuk, Stopping Security Access Manager, DisableAntiSpyware registry key modification, suspicious psexec use, and more. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint, Network_Traffic +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1482/ T1482], [https://attack.mitre.org/techniques/T1485/ T1485], [https://attack.mitre.org/techniques/T1486/ T1486], [https://attack.mitre.org/techniques/T1489/ T1489], [https://attack.mitre.org/techniques/T1490/ T1490], [https://attack.mitre.org/techniques/T1562.001/ T1562.001] +* '''Last Updated''': 2020-11-06 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#BCDEdit_Failure_Recovery_Modification|BCDEdit Failure Recovery Modification]] + +* [[Documentation:ESSOC:detections:Detections#Common_Ransomware_Notes|Common Ransomware Notes]] + +* [[Documentation:ESSOC:detections:Detections#NLTest_Domain_Trust_Discovery|NLTest Domain Trust Discovery]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Bruteforce|Remote Desktop Network Bruteforce]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Traffic|Remote Desktop Network Traffic]] + +* [[Documentation:ESSOC:detections:Detections#Ryuk_Test_Files_Detected|Ryuk Test Files Detected]] + +* [[Documentation:ESSOC:detections:Detections#Spike_in_File_Writes|Spike in File Writes]] + +* [[Documentation:ESSOC:detections:Detections#WBAdmin_Delete_System_Backups|WBAdmin Delete System Backups]] + +* [[Documentation:ESSOC:detections:Detections#Windows_DisableAntiSpyware_Registry|Windows DisableAntiSpyware Registry]] + +* [[Documentation:ESSOC:detections:Detections#Windows_Security_Account_Manager_Stopped|Windows Security Account Manager Stopped]] + +* [[Documentation:ESSOC:detections:Detections#Windows_connhost_exe_started_forcefully|Windows connhost exe started forcefully]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1490 +| Inhibit System Recovery +| Impact +|- +| T1485 +| Data Destruction +| Impact +|- +| T1482 +| Domain Trust Discovery +| Discovery +|- +| T1048 +| Exfiltration Over Alternative Protocol +| Exfiltration +|- +| T1547.001 +| Registry Run Keys / Startup Folder +| Persistence, Privilege Escalation +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|- +| T1047 +| Windows Management Instrumentation +| Execution +|- +| T1486 +| Data Encrypted for Impact +| Impact +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|- +| T1070.001 +| Clear Windows Event Logs +| Defense Evasion +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|- +| T1071.001 +| Web Protocols +| Command and Control +|- +| T1070 +| Indicator Removal on Host +| Defense Evasion +|- +| T1562.001 +| Disable or Modify Tools +| Defense Evasion +|- +| T1489 +| Service Stop +| Impact +|- +| T1059.003 +| Windows Command Shell +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Delivery + +* Exploitation + +* Reconnaissance + + +====Reference==== + +* https://www.splunk.com/en_us/blog/security/detecting-ryuk-using-splunk-attack-range.html + +* https://www.crowdstrike.com/blog/big-game-hunting-with-ryuk-another-lucrative-targeted-ransomware/ + +* https://us-cert.cisa.gov/ncas/alerts/aa20-302a + + +''version'': 1 +
+
+ +---- + +===SamSam Ransomware=== +Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint, Network_Traffic, Web +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1082/ T1082], [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1485/ T1485], [https://attack.mitre.org/techniques/T1486/ T1486], [https://attack.mitre.org/techniques/T1490/ T1490] +* '''Last Updated''': 2018-12-13 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Batch_File_Write_to_System32|Batch File Write to System32]] + +* [[Documentation:ESSOC:detections:Detections#Common_Ransomware_Extensions|Common Ransomware Extensions]] + +* [[Documentation:ESSOC:detections:Detections#Common_Ransomware_Notes|Common Ransomware Notes]] + +* [[Documentation:ESSOC:detections:Detections#Deleting_Shadow_Copies|Deleting Shadow Copies]] + +* [[Documentation:ESSOC:detections:Detections#Detect_PsExec_With_accepteula_Flag|Detect PsExec With accepteula Flag]] + +* [[Documentation:ESSOC:detections:Detections#Detect_attackers_scanning_for_vulnerable_JBoss_servers|Detect attackers scanning for vulnerable JBoss servers]] + +* [[Documentation:ESSOC:detections:Detections#Detect_malicious_requests_to_exploit_JBoss_servers|Detect malicious requests to exploit JBoss servers]] + +* [[Documentation:ESSOC:detections:Detections#File_with_Samsam_Extension|File with Samsam Extension]] + +* [[Documentation:ESSOC:detections:Detections#Prohibited_Software_On_Endpoint|Prohibited Software On Endpoint]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Bruteforce|Remote Desktop Network Bruteforce]] + +* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Traffic|Remote Desktop Network Traffic]] + +* [[Documentation:ESSOC:detections:Detections#Samsam_Test_File_Write|Samsam Test File Write]] + +* [[Documentation:ESSOC:detections:Detections#Spike_in_File_Writes|Spike in File Writes]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1204.002 +| Malicious File +| Execution +|- +| T1485 +| Data Destruction +| Impact +|- +| T1490 +| Inhibit System Recovery +| Impact +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|- +| T1082 +| System Information Discovery +| Discovery +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|- +| T1486 +| Data Encrypted for Impact +| Impact +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Delivery + +* Installation + +* Reconnaissance + + +====Reference==== + +* https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/ + +* https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/ + +* https://thehackernews.com/2018/07/samsam-ransomware-attacks.html + + +''version'': 1 +
+
+ +---- + +===Unusual Processes=== +Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1016/ T1016], [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''Last Updated''': 2020-02-04 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_Rare_Executables|Detect Rare Executables]] + +* [[Documentation:ESSOC:detections:Detections#Detect_processes_used_for_System_Network_Configuration_Discovery|Detect processes used for System Network Configuration Discovery]] + +* [[Documentation:ESSOC:detections:Detections#RunDLL_Loading_DLL_By_Ordinal|RunDLL Loading DLL By Ordinal]] + +* [[Documentation:ESSOC:detections:Detections#System_Processes_Run_From_Unexpected_Locations|System Processes Run From Unexpected Locations]] + +* [[Documentation:ESSOC:detections:Detections#Uncommon_Processes_On_Endpoint|Uncommon Processes On Endpoint]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line|Unusually Long Command Line]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line_-_MLTK|Unusually Long Command Line - MLTK]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1016 +| System Network Configuration Discovery +| Discovery +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|- +| T1204.002 +| Malicious File +| Execution +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Command and Control + +* Installation + + +====Reference==== + +* https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html + +* https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf + +* https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262 + + +''version'': 2 +
+
+ +---- + +===Windows File Extension and Association Abuse=== +Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1546.001/ T1546.001] +* '''Last Updated''': 2018-01-26 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Execution_of_File_With_Spaces_Before_Extension|Execution of File With Spaces Before Extension]] + +* [[Documentation:ESSOC:detections:Detections#Execution_of_File_with_Multiple_Extensions|Execution of File with Multiple Extensions]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Changes_to_File_Associations|Suspicious Changes to File Associations]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|- +| T1546.001 +| Change Default File Association +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Reference==== + +* https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/ + +* https://attack.mitre.org/wiki/Technique/T1042 + + +''version'': 1 +
+
+ +---- + +===Windows Service Abuse=== +Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1569.002/ T1569.002], [https://attack.mitre.org/techniques/T1574.011/ T1574.011] +* '''Last Updated''': 2017-11-02 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#First_Time_Seen_Running_Windows_Service|First Time Seen Running Windows Service]] + +* [[Documentation:ESSOC:detections:Detections#Reg_exe_Manipulating_Windows_Services_Registry_Keys|Reg exe Manipulating Windows Services Registry Keys]] + +* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1569.002 +| Service Execution +| Execution +|- +| T1059.001 +| PowerShell +| Execution +|- +| T1059.003 +| Windows Command Shell +| Execution +|- +| T1574.011 +| Services Registry Permissions Weakness +| Defense Evasion, Persistence, Privilege Escalation +|- +| T1543.003 +| Windows Service +| Persistence, Privilege Escalation +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Installation + + +====Reference==== + +* https://attack.mitre.org/wiki/Technique/T1050 + +* https://attack.mitre.org/wiki/Technique/T1031 + + +''version'': 3 +
+
+ +---- + + + +==Vulnerability== + + +===Apache Struts Vulnerability=== +Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1082/ T1082] +* '''Last Updated''': 2018-12-06 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Suspicious_Java_Classes|Suspicious Java Classes]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Content-Type_Length|Unusually Long Content-Type Length]] + +* [[Documentation:ESSOC:detections:Detections#Web_Servers_Executing_Suspicious_Processes|Web Servers Executing Suspicious Processes]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1082 +| System Information Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Actions on Objectives + +* Delivery + +* Exploitation + + +====Reference==== + +* https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf + + +''version'': 1 +
+
+ +---- + +===JBoss Vulnerability=== +In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Web +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1082/ T1082] +* '''Last Updated''': 2017-09-14 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Detect_attackers_scanning_for_vulnerable_JBoss_servers|Detect attackers scanning for vulnerable JBoss servers]] + +* [[Documentation:ESSOC:detections:Detections#Detect_malicious_requests_to_exploit_JBoss_servers|Detect malicious requests to exploit JBoss servers]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1082 +| System Information Discovery +| Discovery +|} + +====Kill Chain Phase==== + +* Delivery + +* Reconnaissance + + +====Reference==== + +* http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html + + +''version'': 1 +
+
+ +---- + +===Spectre And Meltdown Vulnerabilities=== +Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Vulnerabilities +* '''ATT&CK''': +* '''Last Updated''': 2018-01-08 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Spectre_and_Meltdown_Vulnerable_Systems|Spectre and Meltdown Vulnerable Systems]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + + +====Reference==== + +* https://meltdownattack.com/ + + +''version'': 1 +
+
+ +---- + +===Splunk Enterprise Vulnerability=== +Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2017-09-19 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Open_Redirect_in_Splunk_Web|Open Redirect in Splunk Web]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Delivery + + +====Reference==== + +* http://www.splunk.com/view/SP-CAAAPQ6#announce + +* https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859 + + +''version'': 1 +
+
+ +---- + +===Splunk Enterprise Vulnerability CVE-2018-11409=== +Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': +* '''ATT&CK''': +* '''Last Updated''': 2018-06-14 + +
+
+ +====Detection Profile==== + +* [[Documentation:ESSOC:detections:Detections#Splunk_Enterprise_Information_Disclosure|Splunk Enterprise Information Disclosure]] + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|} + +====Kill Chain Phase==== + +* Delivery + + +====Reference==== + +* https://nvd.nist.gov/vuln/detail/CVE-2018-11409 + +* https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings + +* https://www.exploit-db.com/exploits/44865/ + + +''version'': 1 +
+
+ +---- + + + + +[[Category:V:ESSOC:drafts]] \ No newline at end of file From d685e444f982a6d40fa981e6648b22d5d18b8603 Mon Sep 17 00:00:00 2001 From: divious1 Date: Thu, 4 Mar 2021 10:47:27 -0500 Subject: [PATCH 15/62] working story --- bin/doc_gen.py | 3 + .../doc_detections_markdown.j2 | 2 + bin/jinja2_templates/doc_detections_wiki.j2 | 13 +- bin/jinja2_templates/doc_stories_wiki.j2 | 15 +- ...ous_microsoft_workflow_compiler_rename.yml | 3 +- docs/detections.md | 802 +++- docs/detections.wiki | 4103 +++-------------- docs/stories.md | 4 +- docs/stories.wiki | 1578 +++---- 9 files changed, 2097 insertions(+), 4426 deletions(-) diff --git a/bin/doc_gen.py b/bin/doc_gen.py index 7cb43264cd..55057556c1 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -35,6 +35,7 @@ def get_mitre_enrichment_new(attack, mitre_attack_id): elif mitre_attack_id == technique.id: mitre_attack = mitre_attack_object(technique, attack) return mitre_attack + return [] def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE): manifest_files = [] @@ -65,6 +66,7 @@ def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_de for mitre_technique_id in story_yaml['tags']['mitre_attack_id']: mitre_attack = get_mitre_enrichment_new(attack, mitre_technique_id) mitre_attacks.append(mitre_attack) + # story_yaml['mitre_attacks'] = sorted(mitre_attacks, key = lambda i: i['tactic']) story_yaml['mitre_attacks'] = mitre_attacks stories.append(story_yaml) @@ -195,6 +197,7 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messag mitre_attack = get_mitre_enrichment_new(attack, mitre_technique_id) mitre_attacks.append(mitre_attack) detection_yaml['mitre_attacks'] = mitre_attacks + #detection_yaml['mitre_attacks'] = sorted(mitre_attacks, key = lambda i: i['tactic']) detection_yaml['kind'] = manifest_file.split('/')[-2] detections.append(detection_yaml) diff --git a/bin/jinja2_templates/doc_detections_markdown.j2 b/bin/jinja2_templates/doc_detections_markdown.j2 index eb843c3b62..88db5af004 100644 --- a/bin/jinja2_templates/doc_detections_markdown.j2 +++ b/bin/jinja2_templates/doc_detections_markdown.j2 @@ -89,6 +89,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by * {{ field }} {% endfor %} +{% if detection.mitre_attacks %} #### ATT&CK | ID | Technique | Tactic | @@ -96,6 +97,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by {%- for attack in detection.mitre_attacks %} | {{ attack.technique_id }} | {{ attack.technique }} | {{ attack.tactic|join(', ') }} | {%- endfor %} +{% endif %} #### Kill Chain Phase {% for phase in detection.tags.kill_chain_phases %} diff --git a/bin/jinja2_templates/doc_detections_wiki.j2 b/bin/jinja2_templates/doc_detections_wiki.j2 index 46681600db..93f00b2a1c 100644 --- a/bin/jinja2_templates/doc_detections_wiki.j2 +++ b/bin/jinja2_templates/doc_detections_wiki.j2 @@ -6,12 +6,12 @@ All the detections shipped to different Splunk products. Below is a breakdown by =={{ kind.name|capitalize }}== {% for detection in kind.detections %} -==={{ detection.name}}=== +==={{ detection.name|capitalize}}=== {{ detection.description }} * '''Product''': {{ detection.tags.product|join(', ') }} * '''Datamodel''': {{ detection.datamodel|join(', ') }} -* '''ATT&CK''': {% for mitre_attack_id in detection.tags.mitre_attack_id %}[https://attack.mitre.org/techniques/{{ mitre_attack_id }}/ {{ mitre_attack_id }}]{% if not loop.last %}, {% endif %}{% endfor %} +* '''ATT&CK''': {% for attack in detection.mitre_attacks %}[https://attack.mitre.org/techniques/{{ attack.technique_id }}/ {{ attack.technique_id }}]{% if not loop.last %}, {% endif %}{% endfor %} * '''Last Updated''': {{ detection.date }}
@@ -33,6 +33,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by * {{ field }} {% endfor %} +{% if detection.mitre_attacks|length > 1 %} ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -45,6 +46,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by | {{ attack.tactic|join(', ') }} {%- endfor %} |} +{% endif %} ====Kill Chain Phase==== {% for phase in detection.tags.kill_chain_phases %} @@ -73,4 +75,11 @@ All the detections shipped to different Splunk products. Below is a breakdown by {% endfor %} +''#############'' +''# Automatically generated by doc_gen.py in https://github.com/splunk/security_content'' +''# On Date: {{ time }} UTC'' +''# Author: Splunk Security Research'' +''# Contact: research@splunk.com'' +''#############'' + [[Category:V:ESSOC:drafts]] diff --git a/bin/jinja2_templates/doc_stories_wiki.j2 b/bin/jinja2_templates/doc_stories_wiki.j2 index ca074e92ba..18b9688a22 100644 --- a/bin/jinja2_templates/doc_stories_wiki.j2 +++ b/bin/jinja2_templates/doc_stories_wiki.j2 @@ -6,12 +6,12 @@ All the Analytic Stories shipped to different Splunk products. Below is a breakd =={{ category.name }}== {% for story in category.stories %} -==={{ story.name}}=== +==={{ story.name|capitalize }}=== {{ story.description }} * '''Product''': {{ story.tags.product|join(', ') }} * '''Datamodel''': {{ story.data_models|join(', ') }} -* '''ATT&CK''': {% for mitre_attack_id in story.mitre_attack_ids %}[https://attack.mitre.org/techniques/{{ mitre_attack_id }}/ {{ mitre_attack_id }}]{% if not loop.last %}, {% endif %}{% endfor %} +* '''ATT&CK''': {% for attack in story.mitre_attacks %}[https://attack.mitre.org/techniques/{{ attack.technique_id }}/ {{ attack.technique_id }}]{% if not loop.last %}, {% endif %}{% endfor %} * '''Last Updated''': {{ story.date }}
@@ -19,9 +19,10 @@ All the Analytic Stories shipped to different Splunk products. Below is a breakd ====Detection Profile==== {% for detection in story.detections %} -* [[Documentation:ESSOC:detections:Detections#{{ detection|replace(" ", "_") }}|{{ detection }}]] +* [[Documentation:ESSOC:detections:Detections#{{ detection|replace(" ", "_")|capitalize }}|{{ detection }}]] {% endfor %} +{% if story.mitre_attacks|length > 1 %} ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -34,6 +35,7 @@ All the Analytic Stories shipped to different Splunk products. Below is a breakd | {{ attack.tactic|join(', ') }} {%- endfor %} |} +{% endif %} ====Kill Chain Phase==== {% for phase in story.kill_chain_phases %} @@ -54,4 +56,11 @@ All the Analytic Stories shipped to different Splunk products. Below is a breakd {% endfor %} +''#############'' +''# Automatically generated by doc_gen.py in https://github.com/splunk/security_content'' +''# On Date: {{ time }} UTC'' +''# Author: Splunk Security Research'' +''# Contact: research@splunk.com'' +''#############'' + [[Category:V:ESSOC:drafts]] diff --git a/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml b/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml index 4252a742a7..b25a262a74 100644 --- a/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml +++ b/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml @@ -37,7 +37,8 @@ tags: kill_chain_phases: - Exploitation mitre_attack_id: - - T1127, T1036.003 + - T1127 + - T1036.003 nist: - PR.PT - DE.CM diff --git a/docs/detections.md b/docs/detections.md index 494ab90ce8..d7d3480a81 100644 --- a/docs/detections.md +++ b/docs/detections.md @@ -4377,12 +4377,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + #### Kill Chain Phase @@ -4443,12 +4445,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + #### Kill Chain Phase @@ -4507,10 +4511,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -4572,12 +4573,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + #### Kill Chain Phase @@ -4633,10 +4636,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -4696,12 +4696,14 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1486 | Data Encrypted for Impact | Impact | + #### Kill Chain Phase @@ -4758,12 +4760,14 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1486 | Data Encrypted for Impact | Impact | + #### Kill Chain Phase @@ -4818,10 +4822,7 @@ You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. T #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -4876,12 +4877,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -4934,12 +4937,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -4991,12 +4996,14 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -5054,12 +5061,14 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -5124,12 +5133,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -5182,12 +5193,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -5242,12 +5255,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -5298,12 +5313,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -5365,12 +5382,14 @@ You must be ingesting your cloud infrastructure logs. You also must run the base #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -5433,12 +5452,14 @@ You must be ingesting your cloud infrastructure logs. You also must run the base #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -5501,12 +5522,14 @@ You must be ingesting your cloud infrastructure logs. You also must run the base #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -5568,12 +5591,14 @@ You must be ingesting your cloud infrastructure logs. You also must run the base #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -5626,12 +5651,14 @@ This search requires Sysmon Logs and a Sysmon configuration, which includes Even #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -5686,12 +5713,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1526 | Cloud Service Discovery | Discovery | + #### Kill Chain Phase * Reconnaissance @@ -5742,12 +5771,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1526 | Cloud Service Discovery | Discovery | + #### Kill Chain Phase * Reconnaissance @@ -5806,6 +5837,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -5822,6 +5854,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1556 | Modify Authentication Process | Credential Access, Defense Evasion | | T1558 | Steal or Forge Kerberos Tickets | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -5884,6 +5917,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -5900,6 +5934,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1556 | Modify Authentication Process | Credential Access, Defense Evasion | | T1558 | Steal or Forge Kerberos Tickets | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -5960,6 +5995,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -5971,6 +6007,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1552 | Unsecured Credentials | Credential Access | | T1555 | Credentials from Password Stores | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -6023,12 +6060,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1553.004 | Install Root Certificate | Defense Evasion | + #### Kill Chain Phase * Installation @@ -6085,12 +6124,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1059.001 | PowerShell | Execution | + #### Kill Chain Phase * Installation @@ -6147,12 +6188,14 @@ You must be ingesting data that records the file-system activity from your hosts #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1562.001 | Disable or Modify Tools | Defense Evasion | + #### Kill Chain Phase * Installation @@ -6207,12 +6250,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.002 | Security Account Manager | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -6277,12 +6322,14 @@ You must be ingesting windows endpoint data that tracks process activity, includ * process + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003 | OS Credential Dumping | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -6337,12 +6384,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1490 | Inhibit System Recovery | Impact | + #### Kill Chain Phase * Actions on Objectives @@ -6399,12 +6448,14 @@ You must be ingesting data that records the file-system activity from your hosts #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1204.002 | Malicious File | Execution | + #### Kill Chain Phase * Delivery @@ -6459,10 +6510,7 @@ This search looks for arguments to certutil.exe indicating the manipulation or e #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -6516,12 +6564,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1068 | Exploitation for Privilege Escalation | Privilege Escalation | + #### Kill Chain Phase * Exploitation @@ -6579,12 +6629,14 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | + #### Kill Chain Phase * Command and Control @@ -6641,12 +6693,14 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -6702,12 +6756,14 @@ You must be ingesting the appropriate cloud-infrastructure logs Run the "Previou #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -6763,12 +6819,14 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -6828,10 +6886,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -6890,10 +6945,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -6950,12 +7002,14 @@ This search has a dependency on other searches to create and update a baseline o #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -7006,10 +7060,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -7068,12 +7119,14 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -7132,12 +7185,14 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -7194,12 +7249,14 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -7258,12 +7315,14 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -7325,12 +7384,14 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1485 | Data Destruction | Impact | + #### Kill Chain Phase * Actions on Objectives @@ -7388,12 +7449,14 @@ You must be ingesting data that records file-system activity from your hosts to #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1485 | Data Destruction | Impact | + #### Kill Chain Phase * Actions on Objectives @@ -7446,12 +7509,14 @@ This search needs Sysmon Logs with a Sysmon configuration, which includes EventC #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -7506,12 +7571,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1136.001 | Local Account | Persistence | + #### Kill Chain Phase * Actions on Objectives @@ -7569,12 +7636,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1070.005 | Network Share Connection Removal | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -7629,12 +7698,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.003 | NTDS | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -7689,12 +7760,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.003 | NTDS | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -7749,12 +7822,14 @@ This search requires Sysmon Logs and a Sysmon configuration, which includes Even #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -7813,12 +7888,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.003 | NTDS | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -7873,12 +7950,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.003 | NTDS | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -7947,12 +8026,14 @@ You must be ingesting Windows Security logs from devices of interest, including * process + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003 | OS Credential Dumping | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -8015,12 +8096,14 @@ You must be ingesting Windows Security logs from devices of interest, including * process + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003 | OS Credential Dumping | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -8079,6 +8162,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -8086,6 +8170,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1003 | OS Credential Dumping | Credential Access | | T1555 | Credentials from Password Stores | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -8150,12 +8235,14 @@ You must be ingesting Windows Security logs from devices of interest, including * process + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003 | OS Credential Dumping | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -8222,12 +8309,14 @@ You must be ingesting Windows Security logs from devices of interest, including * process + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003 | OS Credential Dumping | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -8288,12 +8377,14 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003 | OS Credential Dumping | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -8354,12 +8445,14 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003 | OS Credential Dumping | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -8424,12 +8517,14 @@ You must be ingesting Windows Security logs from devices of interest, including * process + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003 | OS Credential Dumping | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -8492,12 +8587,14 @@ You must be ingesting Windows Security logs from devices of interest, including * process + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003 | OS Credential Dumping | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -8557,12 +8654,14 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003 | OS Credential Dumping | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -8630,12 +8729,14 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1071.004 | DNS | Command and Control | + #### Kill Chain Phase * Command and Control @@ -8693,12 +8794,14 @@ To successfully implement this search, you will need to ensure that DNS data is #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | + #### Kill Chain Phase * Command and Control @@ -8755,12 +8858,14 @@ To successfully implement this search you will need to ensure that DNS data is p #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1071.004 | DNS | Command and Control | + #### Kill Chain Phase * Command and Control @@ -8828,12 +8933,14 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "DNS #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1071.004 | DNS | Command and Control | + #### Kill Chain Phase * Command and Control @@ -8888,12 +8995,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1490 | Inhibit System Recovery | Impact | + #### Kill Chain Phase * Actions on Objectives @@ -8955,10 +9064,7 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -9008,6 +9114,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne #### Required field + #### ATT&CK | ID | Technique | Tactic | @@ -9016,6 +9123,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | T1498 | Network Denial of Service | Impact | | T1557.002 | ARP Cache Poisoning | Collection, Credential Access | + #### Kill Chain Phase * Reconnaissance @@ -9083,12 +9191,14 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -9143,10 +9253,7 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -9212,12 +9319,14 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -9282,12 +9391,14 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -9352,12 +9463,14 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -9410,12 +9523,14 @@ To successfully implement this search, you must ingest your Windows Security Eve #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1550.002 | Pass the Hash | Defense Evasion, Lateral Movement | + #### Kill Chain Phase * Actions on Objectives @@ -9465,12 +9580,14 @@ Splunk Universal Forwarder running on Linux systems, capturing logs from the /va #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1068 | Exploitation for Privilege Escalation | Privilege Escalation | + #### Kill Chain Phase * Exploitation @@ -9522,12 +9639,14 @@ Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1068 | Exploitation for Privilege Escalation | Privilege Escalation | + #### Kill Chain Phase * Exploitation @@ -9577,12 +9696,14 @@ OSQuery installed and configured to pick up process events (info at https://osqu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1068 | Exploitation for Privilege Escalation | Privilege Escalation | + #### Kill Chain Phase * Exploitation @@ -9632,12 +9753,14 @@ This search requires audit computer account management to be enabled on the syst #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1210 | Exploitation of Remote Services | Lateral Movement | + #### Kill Chain Phase * Actions on Objectives @@ -9692,12 +9815,14 @@ This search needs Sysmon Logs and a sysmon configuration, which includes EventCo #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -9765,12 +9890,14 @@ If Splunk>Phantom is also configured in your environment, a Playbook called `Let #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1566.003 | Spearphishing via Service | Initial Access | + #### Kill Chain Phase * Delivery @@ -9833,12 +9960,14 @@ You must be ingesting endpoint data that tracks process activity, including Wind * process + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.003 | NTDS | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -9897,12 +10026,14 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "Exc #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.002 | Domain Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -9957,12 +10088,14 @@ ou must ingest your Windows security event logs in the `Change` datamodel under #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.003 | Local Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -10013,12 +10146,14 @@ To consistently detect exploit attempts on F5 devices using the vulnerabilities #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1190 | Exploit Public-Facing Application | Initial Access | + #### Kill Chain Phase * Exploitation @@ -10088,12 +10223,14 @@ This search relies on the Splunk Add-on for Google Cloud Platform, setting up a #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1530 | Data from Cloud Storage Object | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -10144,12 +10281,14 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.001 | Compiled HTML File | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -10208,12 +10347,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.001 | Compiled HTML File | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -10276,12 +10417,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.001 | Compiled HTML File | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -10346,12 +10489,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.001 | Compiled HTML File | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -10418,6 +10563,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne #### Required field + #### ATT&CK | ID | Technique | Tactic | @@ -10426,6 +10572,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | T1498 | Network Denial of Service | Impact | | T1557.002 | ARP Cache Poisoning | Collection, Credential Access | + #### Kill Chain Phase * Reconnaissance @@ -10509,12 +10656,14 @@ The test data is converted from Windows Security Event logs generated from Attac * ticket_options + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1558.003 | Kerberoasting | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -10568,12 +10717,14 @@ In order to run this search effectively, we highly recommend that you leverage t #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1095 | Non-Application Layer Protocol | Command and Control | + #### Kill Chain Phase * Command and Control @@ -10630,12 +10781,14 @@ To successfully implement this search you need to ingest data from your DNS logs #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | + #### Kill Chain Phase * Command and Control @@ -10686,12 +10839,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.005 | Mshta | Defense Evasion | + #### Kill Chain Phase * Exploitation @@ -10755,12 +10910,14 @@ This search needs Sysmon Logs and a sysmon configuration, which includes EventCo #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -10816,12 +10973,14 @@ You must be ingesting Windows Security logs. You must also enable the account ch #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -10873,12 +11032,14 @@ You must be ingesting Windows event logs using the Splunk Windows TA and collect #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1136.001 | Local Account | Persistence | + #### Kill Chain Phase * Actions on Objectives @@ -10939,10 +11100,7 @@ To successfully implement this search, you must ensure the network router device #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -10999,12 +11157,14 @@ This search relies on the Splunk Add-on for Google Cloud Platform, setting up a #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1530 | Data from Cloud Storage Object | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -11056,12 +11216,14 @@ This search looks for CloudTrail events where a user has created an open/public #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1530 | Data from Cloud Storage Object | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -11122,12 +11284,14 @@ You must install the AWS App for Splunk. #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1530 | Data from Cloud Storage Object | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -11191,12 +11355,14 @@ You must be ingesting data that records filesystem and process activity from you #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1566.001 | Spearphishing Attachment | Initial Access | + #### Kill Chain Phase * Installation @@ -11253,12 +11419,14 @@ In order to run this search effectively, we highly recommend that you leverage t #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1071.002 | File Transfer Protocols | Command and Control | + #### Kill Chain Phase * Actions on Objectives @@ -11324,12 +11492,14 @@ The test data is converted from Windows Security Event logs generated from Attac * logon_type + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1550.002 | Pass the Hash | Defense Evasion, Lateral Movement | + #### Kill Chain Phase * Actions on Objectives @@ -11389,12 +11559,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1574.009 | Path Interception by Unquoted Path | Defense Evasion, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -11449,6 +11621,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne #### Required field + #### ATT&CK | ID | Technique | Tactic | @@ -11457,6 +11630,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | T1498 | Network Denial of Service | Impact | | T1557.002 | ARP Cache Poisoning | Collection, Credential Access | + #### Kill Chain Phase * Reconnaissance @@ -11520,12 +11694,14 @@ You must be ingesting data that records process activity from your hosts and pop #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1059.003 | Windows Command Shell | Execution | + #### Kill Chain Phase * Exploitation @@ -11592,12 +11768,14 @@ You must be ingesting sysmon logs. This search has been modified to process raw * dest_user_id + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1059 | Command and Scripting Interpreter | Execution | + #### Kill Chain Phase * Exploitation @@ -11650,12 +11828,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1021.002 | SMB/Windows Admin Shares | Lateral Movement | + #### Kill Chain Phase * Actions on Objectives @@ -11719,10 +11899,7 @@ To successfully implement this search, you must be ingesting data that records p #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -11778,12 +11955,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.009 | Regsvcs/Regasm | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -11844,12 +12023,14 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.009 | Regsvcs/Regasm | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -11909,12 +12090,14 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.009 | Regsvcs/Regasm | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -11973,12 +12156,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.009 | Regsvcs/Regasm | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -12037,12 +12222,14 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.009 | Regsvcs/Regasm | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -12102,12 +12289,14 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.009 | Regsvcs/Regasm | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -12167,12 +12356,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.010 | Regsvr32 | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -12232,6 +12423,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne #### Required field + #### ATT&CK | ID | Technique | Tactic | @@ -12240,6 +12432,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | T1498 | Network Denial of Service | Impact | | T1557 | Man-in-the-Middle | Collection, Credential Access | + #### Kill Chain Phase * Reconnaissance @@ -12294,12 +12487,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.011 | Rundll32 | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -12362,12 +12557,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.011 | Rundll32 | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -12430,12 +12627,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.011 | Rundll32 | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -12498,12 +12697,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.005 | Mshta | Defense Evasion | + #### Kill Chain Phase * Exploitation @@ -12570,12 +12771,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1530 | Data from Cloud Storage Object | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -12636,12 +12839,14 @@ You must be ingesting Zeek SSL data into Splunk. Zeek data should also be gettin #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1041 | Exfiltration Over C2 Channel | Exfiltration | + #### Kill Chain Phase * Actions on Objectives @@ -12698,12 +12903,14 @@ This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1542.005 | TFTP Boot | Defense Evasion, Persistence | + #### Kill Chain Phase * Delivery @@ -12774,12 +12981,14 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -12833,10 +13042,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -12892,10 +13098,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -12959,12 +13162,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -13030,12 +13235,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1530 | Data from Cloud Storage Object | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -13100,12 +13307,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -13171,10 +13380,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -13227,6 +13433,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne #### Required field + #### ATT&CK | ID | Technique | Tactic | @@ -13235,6 +13442,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | T1498 | Network Denial of Service | Impact | | T1020.001 | Traffic Duplication | Exfiltration | + #### Kill Chain Phase * Delivery @@ -13287,10 +13495,7 @@ To successfully implement this search, you must ingest Windows Security Event lo #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -13348,10 +13553,7 @@ This search uses the Network_Sessions data model shipped with Enterprise Securit #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -13409,12 +13611,14 @@ To successfully implement this search, you must be ingesting data that records p #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1059.003 | Windows Command Shell | Execution | + #### Kill Chain Phase * Exploitation @@ -13471,12 +13675,14 @@ You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1203 | Exploitation for Client Execution | Execution | + #### Kill Chain Phase * Exploitation @@ -13533,12 +13739,14 @@ You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1203 | Exploitation for Client Execution | Execution | + #### Kill Chain Phase * Exploitation @@ -13590,12 +13798,14 @@ You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be ge #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1190 | Exploit Public-Facing Application | Initial Access | + #### Kill Chain Phase * Exploitation @@ -13654,12 +13864,14 @@ You must be ingesting data from the web server or network traffic that contains #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1082 | System Information Discovery | Discovery | + #### Kill Chain Phase * Reconnaissance @@ -13726,12 +13938,14 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1189 | Drive-by Compromise | Initial Access | + #### Kill Chain Phase * Command and Control @@ -13790,10 +14004,7 @@ You must ingest data from the web server or capture network data that contains w #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -13845,12 +14056,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.005 | Mshta | Defense Evasion | + #### Kill Chain Phase * Exploitation @@ -13909,12 +14122,14 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.005 | Mshta | Defense Evasion | + #### Kill Chain Phase * Exploitation @@ -13980,12 +14195,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -14038,12 +14255,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -14098,12 +14317,14 @@ You must be ingesting data that records registry activity from your hosts to pop #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1016 | System Network Configuration Discovery | Discovery | + #### Kill Chain Phase * Installation @@ -14162,12 +14383,14 @@ Detailed documentation on how to create a new field within Incident Review may b #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1071.001 | Web Protocols | Command and Control | + #### Kill Chain Phase * Command and Control @@ -14231,12 +14454,14 @@ To successfully implement this search, we must ensure that DNS data is being ing #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | + #### Kill Chain Phase * Command and Control @@ -14289,12 +14514,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1072 | Software Deployment Tools | Execution, Lateral Movement | + #### Kill Chain Phase * Installation @@ -14347,12 +14574,14 @@ To successfully implement this search, you must be ingesting data that records r #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1548.002 | Bypass User Account Control | Defense Evasion, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -14407,12 +14636,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -14470,12 +14701,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -14535,12 +14768,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -14609,12 +14844,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -14669,12 +14906,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1535 | Unused/Unsupported Cloud Regions | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -14734,10 +14973,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -14798,10 +15034,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -14862,12 +15095,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.004 | Cloud Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -14923,10 +15158,7 @@ If Splunk Phantom is also configured in your environment, a playbook called "Sus #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -14978,12 +15210,14 @@ To successfully implement this search, you must be ingesting data that records t #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1114.001 | Local Email Collection | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -15038,12 +15272,14 @@ This search requires you to be ingesting your network traffic and populating the #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1114.002 | Remote Email Collection | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -15101,12 +15337,14 @@ To successfully implement this search you must ensure that DNS data is populatin #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1071.004 | DNS | Command and Control | + #### Kill Chain Phase * Command and Control @@ -15157,12 +15395,14 @@ To successfully implement this search, you must be ingesting data that records p #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1036.003 | Rename System Utilities | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -15213,12 +15453,14 @@ To successfully implement this search, you must be ingesting data that records p #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1036.003 | Rename System Utilities | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -15273,10 +15515,7 @@ To successfully implement this search you need to first obtain data from your ba #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -15328,10 +15567,7 @@ You must be ingesting data that records file-system activity from your hosts to #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -15387,12 +15623,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1068 | Exploitation for Privilege Escalation | Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -15450,12 +15688,14 @@ While this search does not require you to adhere to Splunk CIM, you must be inge #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1569.002 | Service Execution | Execution | + #### Kill Chain Phase * Installation @@ -15522,6 +15762,7 @@ You must be populating the endpoint data model for SSA and specifically the proc * process + #### ATT&CK | ID | Technique | Tactic | @@ -15530,6 +15771,7 @@ You must be populating the endpoint data model for SSA and specifically the proc | | | | | T1202 | Indirect Command Execution | Defense Evasion | + #### Kill Chain Phase * Command and Control @@ -15601,6 +15843,7 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | @@ -15608,6 +15851,7 @@ You must be ingesting data that records process activity from your hosts to popu | T1059.001 | PowerShell | Execution | | T1059.003 | Windows Command Shell | Execution | + #### Kill Chain Phase * Command and Control @@ -15657,12 +15901,14 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Lateral Movement @@ -15716,12 +15962,14 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Lateral Movement @@ -15773,12 +16021,14 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Lateral Movement @@ -15833,12 +16083,14 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1525 | Implant Container Image | Persistence | + #### Kill Chain Phase @@ -15886,12 +16138,14 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1526 | Cloud Service Discovery | Discovery | + #### Kill Chain Phase * Reconnaissance @@ -15943,12 +16197,14 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1526 | Cloud Service Discovery | Discovery | + #### Kill Chain Phase * Reconnaissance @@ -16001,12 +16257,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1222.001 | Windows File and Directory Permissions Modification | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -16057,12 +16315,14 @@ This search will detect more than 5 login failures in Office365 Azure Active Dir #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1110.001 | Password Guessing | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -16117,12 +16377,14 @@ This search requires you to be ingesting your network traffic and populating the #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1114.002 | Remote Email Collection | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -16175,12 +16437,14 @@ To successfully implement this search, you need to be populating the Enterprise #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.002 | Domain Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -16237,6 +16501,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -16246,6 +16511,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1123 | Audio Capture | Collection | | T1563 | Remote Service Session Hijacking | Lateral Movement | + #### Kill Chain Phase * Actions on Objectives @@ -16306,12 +16572,14 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1585 | Establish Accounts | Resource Development | + #### Kill Chain Phase * Actions on Objectives @@ -16372,12 +16640,14 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1070 | Indicator Removal on Host | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -16438,6 +16708,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -16445,6 +16716,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | | T1098 | Account Manipulation | Persistence | + #### Kill Chain Phase * Actions on Objectives @@ -16505,6 +16777,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -16513,6 +16786,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1207 | Rogue Domain Controller | Defense Evasion | | T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -16574,6 +16848,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -16582,6 +16857,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1207 | Rogue Domain Controller | Defense Evasion | | T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -16642,6 +16918,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -16650,6 +16927,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1134 | Access Token Manipulation | Defense Evasion, Privilege Escalation | | T1548 | Abuse Elevation Control Mechanism | Defense Evasion, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -16710,6 +16988,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -16717,6 +16996,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1134 | Access Token Manipulation | Defense Evasion, Privilege Escalation | | T1548 | Abuse Elevation Control Mechanism | Defense Evasion, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -16777,6 +17057,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -16785,6 +17066,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1106 | Native API | Execution | | T1569 | System Services | Execution | + #### Kill Chain Phase * Actions on Objectives @@ -16846,6 +17128,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -16854,6 +17137,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1106 | Native API | Execution | | T1569 | System Services | Execution | + #### Kill Chain Phase * Actions on Objectives @@ -16905,12 +17189,14 @@ You must be ingesting endpoint data that tracks process activity, and include th #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1558.003 | Kerberoasting | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -16966,10 +17252,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17019,10 +17302,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17072,10 +17352,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17124,10 +17401,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17177,10 +17451,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17233,10 +17504,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17288,10 +17556,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17343,10 +17608,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17398,10 +17660,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17452,10 +17711,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17508,10 +17764,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17562,10 +17815,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17616,12 +17866,14 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1526 | Cloud Service Discovery | Discovery | + #### Kill Chain Phase * Reconnaissance @@ -17670,10 +17922,7 @@ You must install splunk AWS add on for GCP. This search works with pubsub messag #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17723,10 +17972,7 @@ You must install splunk GCP add on. This search works with pubsub messaging serv #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17776,10 +18022,7 @@ You must install splunk add on for GCP . This search works with pubsub messaging #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17829,10 +18072,7 @@ You must install splunk add on for GCP. This search works with pubsub messaging #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17882,10 +18122,7 @@ You must install splunk add on for GCP. This search works with pubsub messaging #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17935,10 +18172,7 @@ You must install splunk add on for GCP. This search works with pubsub messaging #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -17989,12 +18223,14 @@ To successfully implement this search you must ensure that DNS data is populatin #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1498.002 | Reflection Amplification | Impact | + #### Kill Chain Phase * Actions on Objectives @@ -18043,10 +18279,7 @@ In order to properly run this search, Splunk needs to ingest process data from y #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -18102,12 +18335,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1059.001 | PowerShell | Execution | + #### Kill Chain Phase * Command and Control @@ -18164,12 +18399,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1027 | Obfuscated Files or Information | Defense Evasion | + #### Kill Chain Phase * Command and Control @@ -18224,12 +18461,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1059.001 | PowerShell | Execution | + #### Kill Chain Phase * Command and Control @@ -18285,12 +18524,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1059.001 | PowerShell | Execution | + #### Kill Chain Phase * Command and Control @@ -18345,12 +18586,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1059.001 | PowerShell | Execution | + #### Kill Chain Phase * Command and Control @@ -18405,10 +18648,7 @@ You need to ingest data from your DNS logs. Specifically you must ingest the dom #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -18469,10 +18709,7 @@ You need to ingest email header data. Specifically the sender's address (src_use #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -18524,12 +18761,14 @@ To successfully implement this search, you must be ingesting data that records r #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1547.010 | Port Monitors | Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -18582,10 +18821,7 @@ You need to ingest data from your web traffic. This can be accomplished by index #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -18646,6 +18882,7 @@ Collect endpoint data such as sysmon or 4688 events. * process_name + #### ATT&CK | ID | Technique | Tactic | @@ -18653,6 +18890,7 @@ Collect endpoint data such as sysmon or 4688 events. | T1059 | Command and Scripting Interpreter | Execution | | T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation | + #### Kill Chain Phase * Exploitation @@ -18707,12 +18945,14 @@ This search is specific to Okta and requires Okta logs are being ingested in you #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.001 | Default Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -18761,12 +19001,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1482 | Domain Trust Discovery | Discovery | + #### Kill Chain Phase * Exploitation @@ -18831,12 +19073,14 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1525 | Implant Container Image | Persistence | + #### Kill Chain Phase @@ -18890,10 +19134,7 @@ To successfully implement this search, it requires that the 'Update' data model #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -18945,12 +19186,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.003 | NTDS | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -19012,12 +19255,14 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1136.003 | Cloud Account | Persistence | + #### Kill Chain Phase * Actions on Objective @@ -19075,12 +19320,14 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1136.003 | Cloud Account | Persistence | + #### Kill Chain Phase * Actions on Objective @@ -19145,12 +19392,14 @@ You must install Splunk Microsoft Office 365 add-on. This search works with o365 #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion | + #### Kill Chain Phase * Actions on Objective @@ -19206,12 +19455,14 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1556 | Modify Authentication Process | Credential Access, Defense Evasion | + #### Kill Chain Phase * Actions on Objective @@ -19266,12 +19517,14 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1110 | Brute Force | Credential Access | + #### Kill Chain Phase * Not Applicable @@ -19328,12 +19581,14 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1556 | Modify Authentication Process | Credential Access, Defense Evasion | + #### Kill Chain Phase * Actions on Objective @@ -19389,12 +19644,14 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1136.003 | Cloud Account | Persistence | + #### Kill Chain Phase * Actions on Objective @@ -19456,12 +19713,14 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1114 | Email Collection | Collection | + #### Kill Chain Phase * Actions on Objective @@ -19519,12 +19778,14 @@ This search detects when an admin configured a forwarding rule for multiple mail #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1114.003 | Email Forwarding Rule | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -19579,12 +19840,14 @@ This search detects the assignment of rights to accesss content from another mai #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1114.002 | Remote Email Collection | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -19640,12 +19903,14 @@ This search detects when multiple user configured a forwarding rule to the same #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1114.003 | Email Forwarding Rule | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -19696,12 +19961,14 @@ This search is specific to Okta and requires Okta logs are being ingested in you #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.001 | Default Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -19749,12 +20016,14 @@ This search is specific to Okta and requires Okta logs are being ingested in you #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.001 | Default Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -19803,12 +20072,14 @@ This search is specific to Okta and requires Okta logs are being ingested in you #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078.001 | Default Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase @@ -19853,10 +20124,7 @@ No extra steps needed to implement this search. #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -19909,10 +20177,7 @@ In order to properly run this search, Splunk needs to ingest data from your osqu #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -19966,12 +20231,14 @@ You must be ingesting data that records the filesystem activity from your hosts #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1546.008 | Accessibility Features | Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -20028,12 +20295,14 @@ Events are fed to DSP contains at least email's sender, subject and its message #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1566 | Phishing | Initial Access | + #### Kill Chain Phase * Actions on Objectives @@ -20092,6 +20361,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_device_id + #### ATT&CK | ID | Technique | Tactic | @@ -20099,6 +20369,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | | T1098 | Account Manipulation | Persistence | + #### Kill Chain Phase * Actions on Objectives @@ -20158,12 +20429,14 @@ You must be ingesting data that records filesystem and process activity from you #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1566.002 | Spearphishing Link | Initial Access | + #### Kill Chain Phase * Installation @@ -20222,12 +20495,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1047 | Windows Management Instrumentation | Execution | + #### Kill Chain Phase * Actions on Objectives @@ -20281,10 +20556,7 @@ In order to properly run this search, Splunk needs to ingest data from your osqu #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -20336,12 +20608,14 @@ To successfully implement this search, you must be ingesting logs with the proce #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1562.004 | Disable or Modify System Firewall | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -20396,12 +20670,14 @@ To successfully implement this search, you must be ingesting data that records p #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1562.004 | Disable or Modify System Firewall | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -20460,12 +20736,14 @@ In order to properly run this search, Splunk needs to ingest data from firewalls #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1048 | Exfiltration Over Alternative Protocol | Exfiltration | + #### Kill Chain Phase * Delivery @@ -20523,10 +20801,7 @@ To successfully implement this search, you must be ingesting data that records p #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -20584,12 +20859,14 @@ Running this search properly requires a technology that can inspect network traf #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration | + #### Kill Chain Phase * Command and Control @@ -20640,10 +20917,7 @@ This search requires you to be ingesting your network traffic, and populating th #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -20713,6 +20987,7 @@ Collect endpoint data such as sysmon or 4688 events. * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -20722,6 +20997,7 @@ Collect endpoint data such as sysmon or 4688 events. | T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation | | T1072 | Software Deployment Tools | Execution, Lateral Movement | + #### Kill Chain Phase * Exploitation @@ -20781,6 +21057,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -20789,6 +21066,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1087 | Account Discovery | Discovery | | T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -20849,6 +21127,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -20857,6 +21136,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1087 | Account Discovery | Discovery | | T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -20917,6 +21197,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -20927,6 +21208,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1591 | Gather Victim Org Information | Reconnaissance | | T1595 | Active Scanning | Reconnaissance | + #### Kill Chain Phase * Actions on Objectives @@ -20987,6 +21269,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -20995,6 +21278,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1590 | Gather Victim Network Information | Reconnaissance | | T1087 | Account Discovery | Discovery | + #### Kill Chain Phase * Actions on Objectives @@ -21055,12 +21339,14 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1592 | Gather Victim Host Information | Reconnaissance | + #### Kill Chain Phase * Actions on Objectives @@ -21121,6 +21407,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -21134,6 +21421,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1518 | Software Discovery | Discovery | | T1592.002 | Software | Reconnaissance | + #### Kill Chain Phase * Actions on Objectives @@ -21194,6 +21482,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -21202,6 +21491,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1046 | Network Service Scanning | Discovery | | T1057 | Process Discovery | Discovery | + #### Kill Chain Phase * Actions on Objectives @@ -21262,6 +21552,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -21270,6 +21561,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1135 | Network Share Discovery | Discovery | | T1039 | Data from Network Shared Drive | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -21330,6 +21622,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -21338,6 +21631,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1135 | Network Share Discovery | Discovery | | T1039 | Data from Network Shared Drive | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -21398,6 +21692,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -21409,6 +21704,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1547 | Boot or Logon Autostart Execution | Persistence, Privilege Escalation | | T1574 | Hijack Execution Flow | Defense Evasion, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -21469,6 +21765,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -21477,6 +21774,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1135 | Network Share Discovery | Discovery | | T1039 | Data from Network Shared Drive | Collection | + #### Kill Chain Phase * Actions on Objectives @@ -21537,6 +21835,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -21548,6 +21847,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | | T1098 | Account Manipulation | Persistence | + #### Kill Chain Phase * Actions on Objectives @@ -21608,6 +21908,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -21615,6 +21916,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1595.002 | Vulnerability Scanning | Reconnaissance | | T1592.002 | Software | Reconnaissance | + #### Kill Chain Phase * Actions on Objectives @@ -21675,6 +21977,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -21683,6 +21986,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | | T1098 | Account Manipulation | Persistence | + #### Kill Chain Phase * Actions on Objectives @@ -21743,6 +22047,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + #### ATT&CK | ID | Technique | Tactic | @@ -21751,6 +22056,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1055 | Process Injection | Defense Evasion, Privilege Escalation | | T1574 | Hijack Execution Flow | Defense Evasion, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -21807,12 +22113,14 @@ To successfully implement this search, you must be ingesting data that records r #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1574.011 | Services Registry Permissions Weakness | Defense Evasion, Persistence, Privilege Escalation | + #### Kill Chain Phase * Installation @@ -21870,12 +22178,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1564.001 | Hidden Files and Directories | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -21938,12 +22248,14 @@ To successfully implement this search, you must be ingesting data that records r #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -22000,12 +22312,14 @@ To successfully implement this search, you must be ingesting data that records r #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1546.012 | Image File Execution Options Injection | Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -22062,12 +22376,14 @@ To successfully implement this search, you must populate the Change_Analysis dat #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1546.011 | Application Shimming | Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -22123,12 +22439,14 @@ You must ensure that your network traffic data is populating the Network_Traffic #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1021.001 | Remote Desktop Protocol | Lateral Movement | + #### Kill Chain Phase * Reconnaissance @@ -22187,12 +22505,14 @@ To successfully implement this search you need to identify systems that commonly #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1021.001 | Remote Desktop Protocol | Lateral Movement | + #### Kill Chain Phase * Actions on Objectives @@ -22245,12 +22565,14 @@ To successfully implement this search, you must be ingesting data that records p #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1021.001 | Remote Desktop Protocol | Lateral Movement | + #### Kill Chain Phase * Actions on Objectives @@ -22303,12 +22625,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1047 | Windows Management Instrumentation | Execution | + #### Kill Chain Phase * Actions on Objectives @@ -22365,10 +22689,7 @@ To successfully implement this search, you must populate the `Endpoint` data mod #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -22420,12 +22741,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1047 | Windows Management Instrumentation | Execution | + #### Kill Chain Phase * Actions on Objectives @@ -22476,12 +22799,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.011 | Rundll32 | Defense Evasion | + #### Kill Chain Phase * Installation @@ -22534,12 +22859,14 @@ You must be ingesting data that records the filesystem activity from your hosts #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1486 | Data Encrypted for Impact | Impact | + #### Kill Chain Phase * Delivery @@ -22601,12 +22928,14 @@ This search requires you to be ingesting your network traffic logs and populatin #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1021.002 | SMB/Windows Admin Shares | Lateral Movement | + #### Kill Chain Phase * Actions on Objectives @@ -22671,12 +23000,14 @@ Detailed documentation on how to create a new field within Incident Review is fo #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1021.002 | SMB/Windows Admin Shares | Lateral Movement | + #### Kill Chain Phase * Actions on Objectives @@ -22727,12 +23058,14 @@ To successfully implement this search, you need to be monitoring network communi #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1190 | Exploit Public-Facing Application | Initial Access | + #### Kill Chain Phase * Delivery @@ -22783,12 +23116,14 @@ You must be ingesting data that records the file-system activity from your hosts #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1486 | Data Encrypted for Impact | Impact | + #### Kill Chain Phase * Delivery @@ -22851,12 +23186,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1543.003 | Windows Service | Persistence, Privilege Escalation | + #### Kill Chain Phase * Installation @@ -22911,12 +23248,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -22970,12 +23309,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -23028,12 +23369,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -23088,12 +23431,14 @@ To successfully implement this search you need to be ingesting logs with both th #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -23146,12 +23491,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1047 | Windows Management Instrumentation | Execution | + #### Kill Chain Phase * Actions on Objectives @@ -23218,6 +23565,7 @@ You must be ingesting Windows Security logs from devices of interest, including * process + #### ATT&CK | ID | Technique | Tactic | @@ -23226,6 +23574,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | | T1098 | Account Manipulation | Persistence | + #### Kill Chain Phase * Actions on Objectives @@ -23286,6 +23635,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -23294,6 +23644,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | | T1098 | Account Manipulation | Persistence | + #### Kill Chain Phase * Actions on Objectives @@ -23354,6 +23705,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + #### ATT&CK | ID | Technique | Tactic | @@ -23362,6 +23714,7 @@ You must be ingesting Windows Security logs from devices of interest, including | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | | T1098 | Account Manipulation | Persistence | + #### Kill Chain Phase * Actions on Objectives @@ -23414,12 +23767,14 @@ You must be ingesting data that records the filesystem activity from your hosts #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1546.011 | Application Shimming | Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -23472,12 +23827,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1546.011 | Application Shimming | Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -23533,12 +23890,14 @@ This search requires you to have enabled your Group Management Audit Logs in you #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1136.001 | Local Account | Persistence | + #### Kill Chain Phase @@ -23596,12 +23955,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1204.002 | Malicious File | Execution | + #### Kill Chain Phase * Actions on Objectives @@ -23654,10 +24015,7 @@ The search requires that you are ingesting your vulnerability-scanner data and t #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -23713,10 +24071,7 @@ In order to implement this search, you must populate the Endpoint file-system da #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -23769,10 +24124,7 @@ The REST endpoint that exposes system information is also necessary for the prop #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -23826,12 +24178,14 @@ This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1203 | Exploitation for Client Execution | Execution | + #### Kill Chain Phase * Actions on Objectives @@ -23881,12 +24235,14 @@ To successfully implement this search, you need to be monitoring web traffic to #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1505.003 | Web Shell | Persistence | + #### Kill Chain Phase * Exfiltration @@ -23947,12 +24303,14 @@ To successfully implement this search you need to be ingesting information on re #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1546.001 | Change Default File Association | Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -24004,12 +24362,14 @@ You must be ingesting data from email logs and have Splunk integrated with UBA. #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1566 | Phishing | Initial Access | + #### Kill Chain Phase * Delivery @@ -24065,12 +24425,14 @@ If Splunk Phantom is also configured in your environment, a Playbook called "Sus #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1566.001 | Spearphishing Attachment | Initial Access | + #### Kill Chain Phase * Delivery @@ -24122,10 +24484,7 @@ You must be ingesting data that records the filesystem activity from your hosts #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -24179,10 +24538,7 @@ In order to properly run this search, Splunk needs to ingest data from your web- #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -24234,6 +24590,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | @@ -24241,6 +24598,7 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1127.001 | MSBuild | Defense Evasion | | T1036.003 | Rename System Utilities | Defense Evasion | + #### Kill Chain Phase * Exploitation @@ -24299,12 +24657,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1127.001 | MSBuild | Defense Evasion | + #### Kill Chain Phase * Exploitation @@ -24373,12 +24733,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1112 | Modify Registry | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -24433,12 +24795,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.010 | Regsvr32 | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -24501,6 +24865,7 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | @@ -24508,6 +24873,7 @@ To successfully implement this search, you need to be ingesting logs with the pr | T1218.011 | Rundll32 | Defense Evasion | | T1036.003 | Rename System Utilities | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -24568,12 +24934,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.011 | Rundll32 | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -24636,12 +25004,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.011 | Rundll32 | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -24711,12 +25081,14 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.011 | Rundll32 | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -24751,7 +25123,7 @@ The following analytic identifies a renamed instance of microsoft.workflow.compi - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: -- **ATT&CK**: [T1127, T1036.003](https://attack.mitre.org/techniques/T1127, T1036.003/) +- **ATT&CK**: [T1127](https://attack.mitre.org/techniques/T1127/), [T1036.003](https://attack.mitre.org/techniques/T1036.003/) - **Last Updated**: 2021-01-12
@@ -24777,11 +25149,14 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| -| | | | +| T1127 | Trusted Developer Utilities Proxy Execution | Defense Evasion | +| T1036.003 | Rename System Utilities | Defense Evasion | + #### Kill Chain Phase @@ -24839,12 +25214,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1127 | Trusted Developer Utilities Proxy Execution | Defense Evasion | + #### Kill Chain Phase * Exploitation @@ -24901,6 +25278,7 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | @@ -24908,6 +25286,7 @@ To successfully implement this search you need to be ingesting information on pr | T1127.001 | MSBuild | Defense Evasion | | T1036.003 | Rename System Utilities | Defense Evasion | + #### Kill Chain Phase * Exploitation @@ -24964,12 +25343,14 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.005 | Mshta | Defense Evasion | + #### Kill Chain Phase * Exploitation @@ -25026,12 +25407,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1218.005 | Mshta | Defense Evasion | + #### Kill Chain Phase * Exploitation @@ -25092,12 +25475,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1070.001 | Clear Windows Event Logs | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -25149,12 +25534,14 @@ You need to be ingesting logs with both the process name and command-line from y #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1036 | Masquerading | Defense Evasion | + #### Kill Chain Phase @@ -25205,12 +25592,14 @@ To successfully implement this search you need to be ingesting information on fi #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1036 | Masquerading | Defense Evasion | + #### Kill Chain Phase @@ -25264,12 +25653,14 @@ To successfully implement this search you need to be ingesting information on pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1082 | System Information Discovery | Discovery | + #### Kill Chain Phase * Actions on Objectives @@ -25357,12 +25748,14 @@ Collect endpoint data such as sysmon or 4688 events. * process_path + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1036 | Masquerading | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -25418,12 +25811,14 @@ To successfully implement this search you need to ingest details about process e #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1036.003 | Rename System Utilities | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -25482,12 +25877,14 @@ In order to properly run this search, Splunk needs to ingest data from firewalls #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1071.001 | Web Protocols | Command and Control | + #### Kill Chain Phase * Command and Control @@ -25541,12 +25938,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1070 | Indicator Removal on Host | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -25604,12 +26003,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1204.002 | Malicious File | Execution | + #### Kill Chain Phase * Actions on Objectives @@ -25661,12 +26062,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1562.001 | Disable or Modify Tools | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -25719,12 +26122,14 @@ This search needs Sysmon Logs with a sysmon configuration, which includes EventC #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | + #### Kill Chain Phase * Actions on Objectives @@ -25778,10 +26183,7 @@ To successfully implement this search you need to obtain data from your backup s #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -25847,10 +26249,7 @@ You must be ingesting sysmon endpoint data that monitors command lines. * process -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -25913,10 +26312,7 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -25982,10 +26378,7 @@ You must be ingesting endpoint data that monitors command lines and populates th #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -26036,10 +26429,7 @@ This particular search leverages data extracted from Stream:HTTP. You must confi #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -26093,12 +26483,14 @@ You must be ingesting endpoint data that tracks process activity, including pare #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1490 | Inhibit System Recovery | Impact | + #### Kill Chain Phase * Actions on Objectives @@ -26162,12 +26554,14 @@ To successfully implement this search, you must be ingesting the Windows WMI act #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1047 | Windows Management Instrumentation | Execution | + #### Kill Chain Phase * Actions on Objectives @@ -26216,12 +26610,14 @@ To successfully implement this search, you must be collecting Sysmon data using #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1546.003 | Windows Management Instrumentation Event Subscription | Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -26276,12 +26672,14 @@ To successfully implement this search, you must be ingesting the Windows WMI act #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1047 | Windows Management Instrumentation | Execution | + #### Kill Chain Phase * Actions on Objectives @@ -26335,12 +26733,14 @@ We start with a dataset that provides visibility into the email address used for #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1136 | Create Account | Persistence | + #### Kill Chain Phase * Actions on Objectives @@ -26396,12 +26796,14 @@ Start with a dataset that allows you to see clickstream data for each user click #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Actions on Objectives @@ -26462,10 +26864,7 @@ We need to start with a dataset that allows us to see the values of usernames an #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -26523,12 +26922,14 @@ You must be ingesting data that records process activity from your hosts to popu #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1082 | System Information Discovery | Discovery | + #### Kill Chain Phase * Actions on Objectives @@ -26579,12 +26980,14 @@ To successfully implement this search, you need to be ingesting logs with the pr #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1018 | Remote System Discovery | Discovery | + #### Kill Chain Phase * Exploitation @@ -26641,12 +27044,14 @@ You must be ingesting data that records the process-system activity from your ho #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1562.001 | Disable or Modify Tools | Defense Evasion | + #### Kill Chain Phase * Delivery @@ -26698,12 +27103,14 @@ To successfully implement this search, you need to be ingesting Windows event lo #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1070.001 | Clear Windows Event Logs | Defense Evasion | + #### Kill Chain Phase * Actions on Objectives @@ -26758,12 +27165,14 @@ You must be ingesting data that records the process-system activity from your ho #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1489 | Service Stop | Impact | + #### Kill Chain Phase * Delivery @@ -26816,12 +27225,14 @@ You must be ingesting data that records the process-system activity from your ho #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1059.003 | Windows Command Shell | Execution | + #### Kill Chain Phase * Delivery @@ -26873,10 +27284,7 @@ To successfully implement this search, you must be ingesting data that records t #### Required field -#### ATT&CK -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| #### Kill Chain Phase @@ -26926,12 +27334,14 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Lateral Movement @@ -26981,12 +27391,14 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Lateral Movement @@ -27034,12 +27446,14 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Lateral Movement @@ -27087,12 +27501,14 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Lateral Movement @@ -27142,12 +27558,14 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1550 | Use Alternate Authentication Material | Defense Evasion, Lateral Movement | + #### Kill Chain Phase * Lateral Movement @@ -27195,12 +27613,14 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo #### Required field + #### ATT&CK | ID | Technique | Tactic | | ----------- | ----------- |--------------| | T1078 | Valid Accounts | Defense Evasion, Initial Access, Persistence, Privilege Escalation | + #### Kill Chain Phase * Lateral Movement diff --git a/docs/detections.wiki b/docs/detections.wiki index d80f91a6c7..08c3e6fc82 100644 --- a/docs/detections.wiki +++ b/docs/detections.wiki @@ -6,7 +6,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by ==Application== -===Detect New Login Attempts to Routers=== +===Detect new login attempts to routers=== The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -38,12 +38,7 @@ To successfully implement this search, you must ensure the network router device ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -65,7 +60,7 @@ Legitimate router connections may appear as new connections ---- -===Email Attachments With Lots Of Spaces=== +===Email attachments with lots of spaces=== Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -102,12 +97,7 @@ If Splunk Phantom is also configured in your environment, a playbook called "Sus ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -129,7 +119,7 @@ None at this time ---- -===Email files written outside of the Outlook directory=== +===Email files written outside of the outlook directory=== The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -159,16 +149,7 @@ To successfully implement this search, you must be ingesting data that records t ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114.001 -| Local Email Collection -| Collection -|} + ====Kill Chain Phase==== @@ -224,16 +205,7 @@ This search requires you to be ingesting your network traffic and populating the ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114.002 -| Remote Email Collection -| Collection -|} + ====Kill Chain Phase==== @@ -255,7 +227,7 @@ The false-positive rate will vary based on how you set the deviation_threshold a ---- -===Monitor Email For Brand Abuse=== +===Monitor email for brand abuse=== This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -292,12 +264,7 @@ You need to ingest email header data. Specifically the sender's address (src_use ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -319,7 +286,7 @@ None at this time ---- -===Multiple Okta Users With Invalid Credentials From The Same IP=== +===Multiple okta users with invalid credentials from the same ip=== This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -350,16 +317,7 @@ This search is specific to Okta and requires Okta logs are being ingested in you ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.001 -| Default Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -379,7 +337,7 @@ A single public IP address servicing multiple legitmate users may trigger this s ---- -===No Windows Updates in a time frame=== +===No windows updates in a time frame=== This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -414,12 +372,7 @@ To successfully implement this search, it requires that the 'Update' data model ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -439,7 +392,7 @@ None identified ---- -===Okta Account Lockout Events=== +===Okta account lockout events=== Detect Okta user lockout events * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -467,16 +420,7 @@ This search is specific to Okta and requires Okta logs are being ingested in you ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.001 -| Default Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -496,7 +440,7 @@ None. Account lockouts should be followed up on to determine if the actual user ---- -===Okta Failed SSO Attempts=== +===Okta failed sso attempts=== Detect failed Okta SSO events * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -525,16 +469,7 @@ This search is specific to Okta and requires Okta logs are being ingested in you ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.001 -| Default Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -554,7 +489,7 @@ There may be a faulty config preventing legitmate users from accessing apps they ---- -===Okta User Logins From Multiple Cities=== +===Okta user logins from multiple cities=== This search detects logins from the same user from different cities in a 24 hour period. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -584,16 +519,7 @@ This search is specific to Okta and requires Okta logs are being ingested in you ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.001 -| Default Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -613,7 +539,7 @@ Users in your enviornment may legitmately be travelling and loggin in from diffe ---- -===Phishing Email Detection by Machine Learning Method - SSA=== +===Phishing email detection by machine learning method - ssa=== Malicious mails can conduct phishing that induces readers to open attachment, click links or trigger third party service. This detect uses Natural Language Processing (NLP) approach to analyze an email message's content (Sender, Subject and Body) and judge whether it is a phishing email. The detection adopts a deep learning (neural network) model that employs character level embeddings plus LSTM layers to perform classification. The model is pre-trained and then published as ONNX format. Current sample model is trained using the dataset published at https://github.com/splunk/attack_data/tree/master/datasets/T1566_Phishing_Email/splunk_train.json User are expected to re-train the model by combining with their own training data for better accuracy using the provided model file (SMLE notebook). DSP pipeline then processes the email message and passes it as an event to Apply ML Models function, which returns the probability of a phishing email. Current implementation assumes the email is fed to DSP in JSON format contains at least email's sender, subject and its message body, including reply content, if any. * '''Product''': UEBA for Security Cloud @@ -647,16 +573,7 @@ Events are fed to DSP contains at least email's sender, subject and its message ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1566 -| Phishing -| Initial Access -|} + ====Kill Chain Phase==== @@ -678,7 +595,7 @@ Because of imbalance of anomaly data in training, the model will less likely rep ---- -===Spectre and Meltdown Vulnerable Systems=== +===Spectre and meltdown vulnerable systems=== The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -708,12 +625,7 @@ The search requires that you are ingesting your vulnerability-scanner data and t ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -733,7 +645,7 @@ It is possible that your vulnerability scanner is not detecting that the patches ---- -===Suspicious Email - UBA Anomaly=== +===Suspicious email - uba anomaly=== This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -764,16 +676,7 @@ You must be ingesting data from email logs and have Splunk integrated with UBA. ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1566 -| Phishing -| Initial Access -|} + ====Kill Chain Phase==== @@ -795,7 +698,7 @@ This detection model will alert on any sender domain that is seen for the first ---- -===Suspicious Email Attachment Extensions=== +===Suspicious email attachment extensions=== This search looks for emails that have attachments with suspicious file extensions. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -830,16 +733,7 @@ If Splunk Phantom is also configured in your environment, a Playbook called "Sus ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1566.001 -| Spearphishing Attachment -| Initial Access -|} + ====Kill Chain Phase==== @@ -861,7 +755,7 @@ None identified ---- -===Suspicious Java Classes=== +===Suspicious java classes=== This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -893,12 +787,7 @@ In order to properly run this search, Splunk needs to ingest data from your web- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -920,7 +809,7 @@ There are no known false positives. ---- -===Web Servers Executing Suspicious Processes=== +===Web servers executing suspicious processes=== This search looks for suspicious processes on all systems labeled as web servers. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -950,16 +839,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1082 -| System Information Discovery -| Discovery -|} + ====Kill Chain Phase==== @@ -986,7 +866,7 @@ Some of these processes may be used legitimately on web servers during maintenan ==Cloud== -===AWS Cross Account Activity From Previously Unseen Account=== +===Aws cross account activity from previously unseen account=== This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. This search is deprecated and have been translated to use the latest Authentication Datamodel. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1022,12 +902,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -1051,7 +926,7 @@ Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicio ---- -===AWS Detect Users creating keys with encrypt policy without MFA=== +===Aws detect users creating keys with encrypt policy without mfa=== This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1087,16 +962,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1486 -| Data Encrypted for Impact -| Impact -|} + ====Kill Chain Phase==== @@ -1124,7 +990,7 @@ unknown ---- -===AWS Detect Users with KMS keys performing encryption S3=== +===Aws detect users with kms keys performing encryption s3=== This search provides detection of users with KMS keys performing encryption specifically against S3 buckets. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1154,16 +1020,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1486 -| Data Encrypted for Impact -| Impact -|} + ====Kill Chain Phase==== @@ -1191,7 +1048,7 @@ bucket with S3 encryption ---- -===AWS EKS Kubernetes cluster sensitive object access=== +===Aws eks kubernetes cluster sensitive object access=== This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1219,12 +1076,7 @@ You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. T ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -1246,7 +1098,7 @@ Sensitive object access is not necessarily malicious but user and object context ---- -===AWS Network Access Control List Created with All Open Ports=== +===Aws network access control list created with all open ports=== The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1279,16 +1131,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.007 -| Disable or Modify Cloud Firewall -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -1312,7 +1155,7 @@ It's possible that an admin has created this ACL with all ports open for some le ---- -===AWS Network Access Control List Deleted=== +===Aws network access control list deleted=== Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1342,16 +1185,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.007 -| Disable or Modify Cloud Firewall -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -1375,7 +1209,7 @@ It's possible that a user has legitimately deleted a network ACL. ---- -===AWS SAML Access by Provider User and Principal=== +===Aws saml access by provider user and principal=== This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1404,16 +1238,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -1443,7 +1268,7 @@ Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very diff ---- -===AWS SAML Update identity provider=== +===Aws saml update identity provider=== This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1472,16 +1297,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -1511,7 +1327,7 @@ Updating a SAML provider or creating a new one may not necessarily be malicious ---- -===Abnormally High Number Of Cloud Infrastructure API Calls=== +===Abnormally high number of cloud infrastructure api calls=== This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1552,16 +1368,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -1585,7 +1392,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base ---- -===Abnormally High Number Of Cloud Instances Destroyed=== +===Abnormally high number of cloud instances destroyed=== This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1625,16 +1432,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -1656,7 +1454,7 @@ Many service accounts configured within a cloud infrastructure are known to exhi ---- -===Abnormally High Number Of Cloud Instances Launched=== +===Abnormally high number of cloud instances launched=== This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1698,16 +1496,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -1729,7 +1518,7 @@ Many service accounts configured within an AWS infrastructure are known to exhib ---- -===Abnormally High Number Of Cloud Security Group API Calls=== +===Abnormally high number of cloud security group api calls=== This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1770,16 +1559,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -1803,7 +1583,7 @@ You must be ingesting your cloud infrastructure logs. You also must run the base ---- -===Amazon EKS Kubernetes Pod scan detection=== +===Amazon eks kubernetes pod scan detection=== This search provides detection information on unauthenticated requests against Kubernetes' Pods API * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1833,16 +1613,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1526 -| Cloud Service Discovery -| Discovery -|} + ====Kill Chain Phase==== @@ -1864,7 +1635,7 @@ Not all unauthenticated requests are malicious, but frequency, UA and source IPs ---- -===Amazon EKS Kubernetes cluster scan detection=== +===Amazon eks kubernetes cluster scan detection=== This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1894,16 +1665,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1526 -| Cloud Service Discovery -| Discovery -|} + ====Kill Chain Phase==== @@ -1925,7 +1687,7 @@ Not all unauthenticated requests are malicious, but frequency, UA and source IPs ---- -===Cloud API Calls From Previously Unseen User Roles=== +===Cloud api calls from previously unseen user roles=== This search looks for new commands from each user role. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1961,16 +1723,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -1992,7 +1745,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. ---- -===Cloud Compute Instance Created By Previously Unseen User=== +===Cloud compute instance created by previously unseen user=== This search looks for cloud compute instances created by users who have not created them before. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2027,16 +1780,7 @@ You must be ingesting the appropriate cloud-infrastructure logs Run the "Previou ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -2058,7 +1802,7 @@ It's possible that a user will start to create compute instances for the first t ---- -===Cloud Compute Instance Created In Previously Unused Region=== +===Cloud compute instance created in previously unused region=== This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2093,16 +1837,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -2126,7 +1861,7 @@ It's possible that a user has unknowingly started an instance in a new region. P ---- -===Cloud Compute Instance Created With Previously Unseen Image=== +===Cloud compute instance created with previously unseen image=== This search looks for cloud compute instances being created with previously unseen image IDs. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2163,12 +1898,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -2190,7 +1920,7 @@ After a new image is created, the first systems created with that image will cau ---- -===Cloud Compute Instance Created With Previously Unseen Instance Type=== +===Cloud compute instance created with previously unseen instance type=== Find EC2 instances being created with previously unseen instance types. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2227,12 +1957,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -2254,7 +1979,7 @@ It is possible that an admin will create a new system using a new instance type ---- -===Cloud Instance Modified By Previously Unseen User=== +===Cloud instance modified by previously unseen user=== This search looks for cloud instances being modified by users who have not previously modified them. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2289,16 +2014,7 @@ This search has a dependency on other searches to create and update a baseline o ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -2320,7 +2036,7 @@ It's possible that a new user will start to modify EC2 instances when they haven ---- -===Cloud Provisioning Activity From Previously Unseen City=== +===Cloud provisioning activity from previously unseen city=== This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2357,16 +2073,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -2389,7 +2096,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif ---- -===Cloud Provisioning Activity From Previously Unseen Country=== +===Cloud provisioning activity from previously unseen country=== This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2426,16 +2133,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -2458,7 +2156,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif ---- -===Cloud Provisioning Activity From Previously Unseen IP Address=== +===Cloud provisioning activity from previously unseen ip address=== This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2493,16 +2191,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -2525,7 +2214,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif ---- -===Cloud Provisioning Activity From Previously Unseen Region=== +===Cloud provisioning activity from previously unseen region=== This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2562,16 +2251,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -2594,7 +2274,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif ---- -===Detect AWS Console Login by New User=== +===Detect aws console login by new user=== This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2628,12 +2308,7 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -2657,7 +2332,7 @@ When a legitimate new user logins for the first time, this activity will be dete ---- -===Detect AWS Console Login by User from New City=== +===Detect aws console login by user from new city=== This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2699,16 +2374,7 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -2732,7 +2398,7 @@ When a legitimate new user logins for the first time, this activity will be dete ---- -===Detect AWS Console Login by User from New Country=== +===Detect aws console login by user from new country=== This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2774,16 +2440,7 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -2807,7 +2464,7 @@ When a legitimate new user logins for the first time, this activity will be dete ---- -===Detect AWS Console Login by User from New Region=== +===Detect aws console login by user from new region=== This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour * '''Product''': Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2849,16 +2506,7 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -2882,7 +2530,7 @@ When a legitimate new user logins for the first time, this activity will be dete ---- -===Detect GCP Storage access from a new IP=== +===Detect gcp storage access from a new ip=== This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2925,16 +2573,7 @@ This search relies on the Splunk Add-on for Google Cloud Platform, setting up a ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} + ====Kill Chain Phase==== @@ -2956,7 +2595,7 @@ GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow i ---- -===Detect New Open GCP Storage Buckets=== +===Detect new open gcp storage buckets=== This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2991,16 +2630,7 @@ This search relies on the Splunk Add-on for Google Cloud Platform, setting up a ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} + ====Kill Chain Phase==== @@ -3022,7 +2652,7 @@ While this search has no known false positives, it is possible that a GCP admin ---- -===Detect New Open S3 Buckets over AWS CLI=== +===Detect new open s3 buckets over aws cli=== This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3053,16 +2683,7 @@ This search looks for CloudTrail events where a user has created an open/public ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} + ====Kill Chain Phase==== @@ -3086,7 +2707,7 @@ While this search has no known false positives, it is possible that an AWS admin ---- -===Detect New Open S3 buckets=== +===Detect new open s3 buckets=== This search looks for CloudTrail events where a user has created an open/public S3 bucket. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3124,16 +2745,7 @@ You must install the AWS App for Splunk. ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} + ====Kill Chain Phase==== @@ -3157,7 +2769,7 @@ While this search has no known false positives, it is possible that an AWS admin ---- -===Detect S3 access from a new IP=== +===Detect s3 access from a new ip=== This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3195,16 +2807,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} + ====Kill Chain Phase==== @@ -3226,7 +2829,7 @@ S3 buckets can be accessed from any IP, as long as it can make a successful conn ---- -===Detect Spike in AWS Security Hub Alerts for EC2 Instance=== +===Detect spike in aws security hub alerts for ec2 instance=== This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3259,12 +2862,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -3286,7 +2884,7 @@ None ---- -===Detect Spike in AWS Security Hub Alerts for User=== +===Detect spike in aws security hub alerts for user=== This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3320,12 +2918,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -3345,7 +2938,7 @@ None ---- -===Detect Spike in S3 Bucket deletion=== +===Detect spike in s3 bucket deletion=== This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3390,16 +2983,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} + ====Kill Chain Phase==== @@ -3421,7 +3005,7 @@ Based on the values of`dataPointThreshold` and `deviationThreshold`, the false p ---- -===Detect Spike in blocked Outbound Traffic from your AWS=== +===Detect spike in blocked outbound traffic from your aws=== This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3466,12 +3050,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -3495,7 +3074,7 @@ The false-positive rate may vary based on the values of`dataPointThreshold` and ---- -===GCP Detect accounts with high risk roles by project=== +===Gcp detect accounts with high risk roles by project=== This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3522,16 +3101,7 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -3559,7 +3129,7 @@ Accounts with high risk roles should be reduced to the minimum number needed, ho ---- -===GCP Detect gcploit framework=== +===Gcp detect gcploit framework=== This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3586,16 +3156,7 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -3621,7 +3182,7 @@ Payload.request.function.timeout value can possibly be match with other function ---- -===GCP Detect high risk permissions by resource and account=== +===Gcp detect high risk permissions by resource and account=== This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3648,16 +3209,7 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -3685,7 +3237,7 @@ High risk permissions are part of any GCP environment, however it is important t ---- -===GCP Kubernetes cluster pod scan detection=== +===Gcp kubernetes cluster pod scan detection=== This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3714,16 +3266,7 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1526 -| Cloud Service Discovery -| Discovery -|} + ====Kill Chain Phase==== @@ -3745,7 +3288,7 @@ Not all unauthenticated requests are malicious, but frequency, User Agent, sourc ---- -===GCP Kubernetes cluster scan detection=== +===Gcp kubernetes cluster scan detection=== This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3776,16 +3319,7 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1526 -| Cloud Service Discovery -| Discovery -|} + ====Kill Chain Phase==== @@ -3807,7 +3341,7 @@ Not all unauthenticated requests are malicious, but frequency, User Agent and so ---- -===High Number of Login Failures from a single source=== +===High number of login failures from a single source=== This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3835,16 +3369,7 @@ This search will detect more than 5 login failures in Office365 Azure Active Dir ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1110.001 -| Password Guessing -| Credential Access -|} + ====Kill Chain Phase==== @@ -3866,7 +3391,7 @@ unknown ---- -===Kubernetes AWS detect RBAC authorization by account=== +===Kubernetes aws detect rbac authorization by account=== This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3895,12 +3420,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -3922,7 +3442,7 @@ Not all RBAC Authorications are malicious. RBAC authorizations can uncover malic ---- -===Kubernetes AWS detect most active service accounts by pod=== +===Kubernetes aws detect most active service accounts by pod=== This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3950,12 +3470,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -3977,7 +3492,7 @@ Not all service accounts interactions are malicious. Analyst must consider IP, v ---- -===Kubernetes AWS detect sensitive role access=== +===Kubernetes aws detect sensitive role access=== This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4005,12 +3520,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4032,7 +3542,7 @@ Sensitive role resource access is necessary for cluster operation, however sourc ---- -===Kubernetes AWS detect service accounts forbidden failure access=== +===Kubernetes aws detect service accounts forbidden failure access=== This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4059,12 +3569,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4086,7 +3591,7 @@ This search can give false positives as there might be inherent issues with auth ---- -===Kubernetes AWS detect suspicious kubectl calls=== +===Kubernetes aws detect suspicious kubectl calls=== This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4114,12 +3619,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4141,7 +3641,7 @@ Kubectl calls are not malicious by nature. However source IP, verb and Object ca ---- -===Kubernetes Azure detect RBAC authorization by account=== +===Kubernetes azure detect rbac authorization by account=== This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4172,12 +3672,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4199,7 +3694,7 @@ Not all RBAC Authorications are malicious. RBAC authorizations can uncover malic ---- -===Kubernetes Azure detect most active service accounts by pod namespace=== +===Kubernetes azure detect most active service accounts by pod namespace=== This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4229,12 +3724,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4256,7 +3746,7 @@ Not all service accounts interactions are malicious. Analyst must consider IP an ---- -===Kubernetes Azure detect sensitive object access=== +===Kubernetes azure detect sensitive object access=== This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4286,12 +3776,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4313,7 +3798,7 @@ Sensitive object access is not necessarily malicious but user and object context ---- -===Kubernetes Azure detect sensitive role access=== +===Kubernetes azure detect sensitive role access=== This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4343,12 +3828,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4370,7 +3850,7 @@ Sensitive role resource access is necessary for cluster operation, however sourc ---- -===Kubernetes Azure detect service accounts forbidden failure access=== +===Kubernetes azure detect service accounts forbidden failure access=== This search provides information on Kubernetes service accounts with failure or forbidden access status * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4399,12 +3879,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4426,7 +3901,7 @@ This search can give false positives as there might be inherent issues with auth ---- -===Kubernetes Azure detect suspicious kubectl calls=== +===Kubernetes azure detect suspicious kubectl calls=== This search provides information on rare Kubectl calls with IP, verb namespace and object access context * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4457,12 +3932,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4484,7 +3954,7 @@ Kubectl calls are not malicious by nature. However source IP, verb and Object ca ---- -===Kubernetes Azure pod scan fingerprint=== +===Kubernetes azure pod scan fingerprint=== This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4513,12 +3983,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4540,7 +4005,7 @@ Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, ---- -===Kubernetes Azure scan fingerprint=== +===Kubernetes azure scan fingerprint=== This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4569,16 +4034,7 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1526 -| Cloud Service Discovery -| Discovery -|} + ====Kill Chain Phase==== @@ -4600,7 +4056,7 @@ Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, ---- -===Kubernetes GCP detect RBAC authorizations by account=== +===Kubernetes gcp detect rbac authorizations by account=== This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4628,12 +4084,7 @@ You must install splunk AWS add on for GCP. This search works with pubsub messag ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4655,7 +4106,7 @@ Not all RBAC Authorications are malicious. RBAC authorizations can uncover malic ---- -===Kubernetes GCP detect most active service accounts by pod=== +===Kubernetes gcp detect most active service accounts by pod=== This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4683,12 +4134,7 @@ You must install splunk GCP add on. This search works with pubsub messaging serv ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4710,7 +4156,7 @@ Not all service accounts interactions are malicious. Analyst must consider IP, v ---- -===Kubernetes GCP detect sensitive object access=== +===Kubernetes gcp detect sensitive object access=== This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4738,12 +4184,7 @@ You must install splunk add on for GCP . This search works with pubsub messaging ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4765,7 +4206,7 @@ Sensitive object access is not necessarily malicious but user and object context ---- -===Kubernetes GCP detect sensitive role access=== +===Kubernetes gcp detect sensitive role access=== This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4793,12 +4234,7 @@ You must install splunk add on for GCP. This search works with pubsub messaging ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4820,7 +4256,7 @@ Sensitive role resource access is necessary for cluster operation, however sourc ---- -===Kubernetes GCP detect service accounts forbidden failure access=== +===Kubernetes gcp detect service accounts forbidden failure access=== This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4848,12 +4284,7 @@ You must install splunk add on for GCP. This search works with pubsub messaging ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4875,7 +4306,7 @@ This search can give false positives as there might be inherent issues with auth ---- -===Kubernetes GCP detect suspicious kubectl calls=== +===Kubernetes gcp detect suspicious kubectl calls=== This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4903,12 +4334,7 @@ You must install splunk add on for GCP. This search works with pubsub messaging ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -4930,7 +4356,7 @@ Kubectl calls are not malicious by nature. However source IP, source user, user ---- -===New container uploaded to AWS ECR=== +===New container uploaded to aws ecr=== This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4958,16 +4384,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1525 -| Implant Container Image -| Persistence -|} + ====Kill Chain Phase==== @@ -4987,7 +4404,7 @@ Uploading container is a normal behavior from developers or users with access to ---- -===O365 Add App Role Assignment Grant User=== +===O365 add app role assignment grant user=== This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5018,16 +4435,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1136.003 -| Cloud Account -| Persistence -|} + ====Kill Chain Phase==== @@ -5055,7 +4463,7 @@ The creation of a new Federation is not necessarily malicious, however this even ---- -===O365 Added Service Principal=== +===O365 added service principal=== This search detects the creation of a new Federation setting by alerting about an specific event related to its creation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5086,16 +4494,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1136.003 -| Cloud Account -| Persistence -|} + ====Kill Chain Phase==== @@ -5127,7 +4526,7 @@ The creation of a new Federation is not necessarily malicious, however these eve ---- -===O365 Bypass MFA via Trusted IP=== +===O365 bypass mfa via trusted ip=== This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5161,16 +4560,7 @@ You must install Splunk Microsoft Office 365 add-on. This search works with o365 ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.007 -| Disable or Modify Cloud Firewall -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -5198,7 +4588,7 @@ Unless it is a special case, it is uncommon to continually update Trusted IPs to ---- -===O365 Disable MFA=== +===O365 disable mfa=== This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5227,16 +4617,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1556 -| Modify Authentication Process -| Credential Access, Defense Evasion -|} + ====Kill Chain Phase==== @@ -5262,7 +4643,7 @@ Unless it is a special case, it is uncommon to disable MFA or Strong Authenticat ---- -===O365 Excessive Authentication Failures Alert=== +===O365 excessive authentication failures alert=== This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5292,16 +4673,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1110 -| Brute Force -| Credential Access -|} + ====Kill Chain Phase==== @@ -5327,7 +4699,7 @@ The threshold for alert is above 10 attempts and this should reduce the number o ---- -===O365 Excessive SSO logon errors=== +===O365 excessive sso logon errors=== This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5359,16 +4731,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1556 -| Modify Authentication Process -| Credential Access, Defense Evasion -|} + ====Kill Chain Phase==== @@ -5394,7 +4757,7 @@ Logon errors may not be malicious in nature however it may indicate attempts to ---- -===O365 New Federated Domain Added=== +===O365 new federated domain added=== This search detects the addition of a new Federated domain. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5425,16 +4788,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1136.003 -| Cloud Account -| Persistence -|} + ====Kill Chain Phase==== @@ -5468,7 +4822,7 @@ The creation of a new Federated domain is not necessarily malicious, however the ---- -===O365 PST export alert=== +===O365 pst export alert=== This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5497,16 +4851,7 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114 -| Email Collection -| Collection -|} + ====Kill Chain Phase==== @@ -5532,7 +4877,7 @@ PST export can be done for legitimate purposes but due to the sensitive nature o ---- -===O365 Suspicious Admin Email Forwarding=== +===O365 suspicious admin email forwarding=== This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5565,16 +4910,7 @@ This search detects when an admin configured a forwarding rule for multiple mail ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114.003 -| Email Forwarding Rule -| Collection -|} + ====Kill Chain Phase==== @@ -5598,7 +4934,7 @@ unknown ---- -===O365 Suspicious Rights Delegation=== +===O365 suspicious rights delegation=== This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5630,16 +4966,7 @@ This search detects the assignment of rights to accesss content from another mai ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114.002 -| Remote Email Collection -| Collection -|} + ====Kill Chain Phase==== @@ -5663,7 +4990,7 @@ Service Accounts ---- -===O365 Suspicious User Email Forwarding=== +===O365 suspicious user email forwarding=== This search detects when multiple user configured a forwarding rule to the same destination. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5696,16 +5023,7 @@ This search detects when multiple user configured a forwarding rule to the same ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114.003 -| Email Forwarding Rule -| Collection -|} + ====Kill Chain Phase==== @@ -5729,7 +5047,7 @@ unknown ---- -===aws detect attach to role policy=== +===Aws detect attach to role policy=== This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5757,16 +5075,7 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -5788,7 +5097,7 @@ Attach to policy can create a lot of noise. This search can be adjusted to provi ---- -===aws detect permanent key creation=== +===Aws detect permanent key creation=== This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5817,16 +5126,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -5848,7 +5148,7 @@ Not all permanent key creations are malicious. If there is a policy of rotating ---- -===aws detect role creation=== +===Aws detect role creation=== This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5875,16 +5175,7 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -5906,7 +5197,7 @@ CreateRole is not very common in common users. This search can be adjusted to pr ---- -===aws detect sts assume role abuse=== +===Aws detect sts assume role abuse=== This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5933,16 +5224,7 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -5964,7 +5246,7 @@ Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross ---- -===aws detect sts get session token abuse=== +===Aws detect sts get session token abuse=== This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5993,16 +5275,7 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1550 -| Use Alternate Authentication Material -| Defense Evasion, Lateral Movement -|} + ====Kill Chain Phase==== @@ -6024,7 +5297,7 @@ Sts:GetSessionToken can be very noisy as in certain environments numerous calls ---- -===gcp detect oauth token abuse=== +===Gcp detect oauth token abuse=== This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6051,16 +5324,7 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -6091,7 +5355,7 @@ GCP Oauth token abuse detection will only work if there are access policies in p ==Deprecated== -===AWS Cloud Provisioning From Previously Unseen City=== +===Aws cloud provisioning from previously unseen city=== This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6132,16 +5396,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -6162,7 +5417,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif ---- -===AWS Cloud Provisioning From Previously Unseen Country=== +===Aws cloud provisioning from previously unseen country=== This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6203,16 +5458,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -6233,7 +5479,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif ---- -===AWS Cloud Provisioning From Previously Unseen IP Address=== +===Aws cloud provisioning from previously unseen ip address=== This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6272,12 +5518,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -6298,7 +5539,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif ---- -===AWS Cloud Provisioning From Previously Unseen Region=== +===Aws cloud provisioning from previously unseen region=== This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6339,16 +5580,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -6369,7 +5601,7 @@ This is a strictly behavioral search, so we define "false positive" slightly dif ---- -===Abnormally High AWS Instances Launched by User=== +===Abnormally high aws instances launched by user=== This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6405,16 +5637,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -6436,7 +5659,7 @@ Many service accounts configured within an AWS infrastructure are known to exhib ---- -===Abnormally High AWS Instances Launched by User - MLTK=== +===Abnormally high aws instances launched by user - mltk=== This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6468,16 +5691,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -6499,7 +5713,7 @@ Many service accounts configured within an AWS infrastructure are known to exhib ---- -===Abnormally High AWS Instances Terminated by User=== +===Abnormally high aws instances terminated by user=== This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6533,16 +5747,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -6564,7 +5769,7 @@ Many service accounts configured with your AWS infrastructure are known to exhib ---- -===Abnormally High AWS Instances Terminated by User - MLTK=== +===Abnormally high aws instances terminated by user - mltk=== This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6594,16 +5799,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -6625,7 +5821,7 @@ Many service accounts configured within an AWS infrastructure are known to exhib ---- -===Clients Connecting to Multiple DNS Servers=== +===Clients connecting to multiple dns servers=== This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6662,16 +5858,7 @@ Detailed documentation on how to create a new field within Incident Review may b ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1048.003 -| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol -| Exfiltration -|} + ====Kill Chain Phase==== @@ -6693,7 +5880,7 @@ It's possible that an enterprise has more than five DNS servers that are configu ---- -===Cloud Network Access Control List Deleted=== +===Cloud network access control list deleted=== Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6723,12 +5910,7 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -6750,7 +5932,7 @@ It's possible that a user has legitimately deleted a network ACL. ---- -===DNS Query Requests Resolved by Unauthorized DNS Servers=== +===Dns query requests resolved by unauthorized dns servers=== This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6784,16 +5966,7 @@ To successfully implement this search you will need to ensure that DNS data is p ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1071.004 -| DNS -| Command and Control -|} + ====Kill Chain Phase==== @@ -6815,7 +5988,7 @@ Legitimate DNS activity can be detected in this search. Investigate, verify and ---- -===Detect API activity from users without MFA=== +===Detect api activity from users without mfa=== This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6854,12 +6027,7 @@ Detailed documentation on how to create a new field within Incident Review may b ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -6879,7 +6047,7 @@ Many service accounts configured within an AWS infrastructure do not have multi ---- -===Detect AWS API Activities From Unapproved Accounts=== +===Detect aws api activities from unapproved accounts=== This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6922,16 +6090,7 @@ Detailed documentation on how to create a new field within Incident Review may b ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -6953,7 +6112,7 @@ It's likely that you'll find activity detected by users/service accounts that ar ---- -===Detect DNS requests to Phishing Sites leveraging EvilGinx2=== +===Detect dns requests to phishing sites leveraging evilginx2=== This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -6998,16 +6157,7 @@ If Splunk>Phantom is also configured in your environment, a Playbook called `Let ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1566.003 -| Spearphishing via Service -| Initial Access -|} + ====Kill Chain Phase==== @@ -7031,7 +6181,7 @@ If a known good domain is not listed in the legit_domains.csv file, then the sea ---- -===Detect Long DNS TXT Record Response=== +===Detect long dns txt record response=== This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7067,16 +6217,7 @@ To successfully implement this search you need to ingest data from your DNS logs ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1048.003 -| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol -| Exfiltration -|} + ====Kill Chain Phase==== @@ -7098,7 +6239,7 @@ It's possible that legitimate TXT record responses can be long enough to trigger ---- -===Detect Mimikatz Using Loaded Images=== +===Detect mimikatz using loaded images=== This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7133,16 +6274,7 @@ This search needs Sysmon Logs and a sysmon configuration, which includes EventCo ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.001 -| LSASS Memory -| Credential Access -|} + ====Kill Chain Phase==== @@ -7166,7 +6298,7 @@ Other tools can import the same DLLs. These tools should be part of a whitelist. ---- -===Detect Mimikatz Via PowerShell And EventCode 4703=== +===Detect mimikatz via powershell and eventcode 4703=== This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7199,16 +6331,7 @@ You must be ingesting Windows Security logs. You must also enable the account ch ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.001 -| LSASS Memory -| Credential Access -|} + ====Kill Chain Phase==== @@ -7230,7 +6353,7 @@ The activity may be legitimate. PowerShell is often used by administrators to pe ---- -===Detect Spike in AWS API Activity=== +===Detect spike in aws api activity=== This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7280,16 +6403,7 @@ Detailed documentation on how to create a new field within Incident Review may b ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -7311,7 +6425,7 @@ Detailed documentation on how to create a new field within Incident Review may b ---- -===Detect Spike in Network ACL Activity=== +===Detect spike in network acl activity=== This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7355,16 +6469,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.007 -| Disable or Modify Cloud Firewall -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -7386,7 +6491,7 @@ The false-positive rate may vary based on the values of`dataPointThreshold` and ---- -===Detect Spike in Security Group Activity=== +===Detect spike in security group activity=== This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7430,16 +6535,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -7461,7 +6557,7 @@ Based on the values of`dataPointThreshold` and `deviationThreshold`, the false p ---- -===Detect USB device insertion=== +===Detect usb device insertion=== The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7491,12 +6587,7 @@ To successfully implement this search, you must ingest Windows Security Event lo ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -7520,7 +6611,7 @@ Legitimate USB activity will also be detected. Please verify and investigate as ---- -===Detect new API calls from user roles=== +===Detect new api calls from user roles=== This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7559,16 +6650,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -7588,7 +6670,7 @@ It is possible that there are legitimate user roles making new or infrequently u ---- -===Detect new user AWS Console Login=== +===Detect new user aws console login=== This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7622,16 +6704,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -7685,16 +6758,7 @@ Detailed documentation on how to create a new field within Incident Review may b ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1071.001 -| Web Protocols -| Command and Control -|} + ====Kill Chain Phase==== @@ -7718,7 +6782,7 @@ It is possible that list of dynamic DNS providers is outdated and/or that the UR ---- -===Detection of DNS Tunnels=== +===Detection of dns tunnels=== This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7759,16 +6823,7 @@ To successfully implement this search, we must ensure that DNS data is being ing ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1048.003 -| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol -| Exfiltration -|} + ====Kill Chain Phase==== @@ -7792,7 +6847,7 @@ It's possible that normal DNS traffic will exhibit this behavior. If an alert is ---- -===EC2 Instance Modified With Previously Unseen User=== +===Ec2 instance modified with previously unseen user=== This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7832,16 +6887,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -7861,7 +6907,7 @@ It's possible that a new user will start to modify EC2 instances when they haven ---- -===EC2 Instance Started In Previously Unseen Region=== +===Ec2 instance started in previously unseen region=== This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7897,16 +6943,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -7928,7 +6965,7 @@ It's possible that a user has unknowingly started an instance in a new region. P ---- -===EC2 Instance Started With Previously Unseen AMI=== +===Ec2 instance started with previously unseen ami=== This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -7967,12 +7004,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -7992,7 +7024,7 @@ After a new AMI is created, the first systems created with that AMI will cause t ---- -===EC2 Instance Started With Previously Unseen Instance Type=== +===Ec2 instance started with previously unseen instance type=== This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8033,12 +7065,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -8058,7 +7085,7 @@ It is possible that an admin will create a new system using a new instance type ---- -===EC2 Instance Started With Previously Unseen User=== +===Ec2 instance started with previously unseen user=== This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8099,16 +7126,7 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -8128,7 +7146,7 @@ It's possible that a user will start to create EC2 instances when they haven't b ---- -===Execution of File With Spaces Before Extension=== +===Execution of file with spaces before extension=== This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8158,16 +7176,7 @@ To successfully implement this search, you must be ingesting data that records p ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1036.003 -| Rename System Utilities -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -8189,7 +7198,7 @@ None identified. ---- -===Extended Period Without Successful Netbackup Backups=== +===Extended period without successful netbackup backups=== This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8221,12 +7230,7 @@ To successfully implement this search you need to first obtain data from your ba ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -8295,6 +7299,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -8310,6 +7315,7 @@ You must be ingesting data that records process activity from your hosts to popu | Execution |} + ====Kill Chain Phase==== * Command and Control @@ -8332,7 +7338,7 @@ Legitimate programs can also use command-line arguments to execute. Please verif ---- -===GCP GCR container uploaded=== +===Gcp gcr container uploaded=== This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8360,16 +7366,7 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1525 -| Implant Container Image -| Persistence -|} + ====Kill Chain Phase==== @@ -8389,7 +7386,7 @@ Uploading container is a normal behavior from developers or users with access to ---- -===Identify New User Accounts=== +===Identify new user accounts=== This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8421,16 +7418,7 @@ To successfully implement this search, you need to be populating the Enterprise ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.002 -| Domain Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -8450,7 +7438,7 @@ If the Identity_Management data model is not updated regularly, this search coul ---- -===Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments=== +===Malicious powershell process - multiple suspicious command-line arguments=== This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8481,16 +7469,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1059.001 -| PowerShell -| Execution -|} + ====Kill Chain Phase==== @@ -8514,7 +7493,7 @@ Legitimate process can have this combination of command-line options, but it's n ---- -===Monitor DNS For Brand Abuse=== +===Monitor dns for brand abuse=== This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8544,12 +7523,7 @@ You need to ingest data from your DNS logs. Specifically you must ingest the dom ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -8573,7 +7547,7 @@ None at this time ---- -===Open Redirect in Splunk Web=== +===Open redirect in splunk web=== This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8599,12 +7573,7 @@ No extra steps needed to implement this search. ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -8626,7 +7595,7 @@ None identified ---- -===Osquery pack - ColdRoot detection=== +===Osquery pack - coldroot detection=== This search looks for ColdRoot events from the osx-attacks osquery pack. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8657,12 +7626,7 @@ In order to properly run this search, Splunk needs to ingest data from your osqu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -8716,16 +7680,7 @@ To successfully implement this search, you must be ingesting logs with the proce ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.004 -| Disable or Modify System Firewall -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -8747,7 +7702,7 @@ It is unusual for netsh.exe to have any child processes in most environments. It ---- -===Prohibited Software On Endpoint=== +===Prohibited software on endpoint=== This search looks for applications on the endpoint that you have marked as prohibited. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8782,12 +7737,7 @@ To successfully implement this search, you must be ingesting data that records p ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -8848,16 +7798,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1564.001 -| Hidden Files and Directories -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -8879,7 +7820,7 @@ None at the moment ---- -===Remote Registry Key modifications=== +===Remote registry key modifications=== This search monitors for remote modifications to registry keys. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8913,12 +7854,7 @@ To successfully implement this search, you must populate the `Endpoint` data mod ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -8940,7 +7876,7 @@ This technique may be legitimately used by administrators to modify remote regis ---- -===Remote WMI Command Attempt=== +===Remote wmi command attempt=== This search looks for wmic.exe being launched with parameters to operate on remote systems. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -8970,16 +7906,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1047 -| Windows Management Instrumentation -| Execution -|} + ====Kill Chain Phase==== @@ -9001,7 +7928,7 @@ Administrators may use this legitimately to gather info from remote systems. ---- -===Scheduled tasks used in BadRabbit ransomware=== +===Scheduled tasks used in badrabbit ransomware=== This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9032,16 +7959,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1053.005 -| Scheduled Task -| Execution, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -9063,7 +7981,7 @@ No known false positives ---- -===Splunk Enterprise Information Disclosure=== +===Splunk enterprise information disclosure=== This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9094,12 +8012,7 @@ The REST endpoint that exposes system information is also necessary for the prop ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -9121,7 +8034,7 @@ Retrieving server information may be a legitimate API request. Verify that the a ---- -===Suspicious Changes to File Associations=== +===Suspicious changes to file associations=== This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9157,16 +8070,7 @@ To successfully implement this search you need to be ingesting information on re ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1546.001 -| Change Default File Association -| Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -9188,7 +8092,7 @@ There may be other processes in your environment that users may legitimately use ---- -===Suspicious File Write=== +===Suspicious file write=== The search looks for files created with names that have been linked to malicious activity. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9219,12 +8123,7 @@ You must be ingesting data that records the filesystem activity from your hosts ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -9246,7 +8145,7 @@ It's possible for a legitimate file to be created with the same name as one note ---- -===Suspicious writes to System Volume Information=== +===Suspicious writes to system volume information=== This search detects writes to the 'System Volume Information' folder by something other than the System process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9275,16 +8174,7 @@ You need to be ingesting logs with both the process name and command-line from y ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1036 -| Masquerading -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -9304,7 +8194,7 @@ It is possible that other utilities or system processes may legitimately write t ---- -===Uncommon Processes On Endpoint=== +===Uncommon processes on endpoint=== This search looks for applications on the endpoint that you have marked as uncommon. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9339,16 +8229,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1204.002 -| Malicious File -| Execution -|} + ====Kill Chain Phase==== @@ -9370,7 +8251,7 @@ None identified ---- -===Unsigned Image Loaded by LSASS=== +===Unsigned image loaded by lsass=== This search detects loading of unsigned images by LSASS. Deprecated because too noisy. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9400,16 +8281,7 @@ This search needs Sysmon Logs with a sysmon configuration, which includes EventC ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.001 -| LSASS Memory -| Credential Access -|} + ====Kill Chain Phase==== @@ -9433,7 +8305,7 @@ Other tools could load images into LSASS for legitimate reason. But enterprise t ---- -===Unsuccessful Netbackup backups=== +===Unsuccessful netbackup backups=== This search gives you the hosts where a backup was attempted and then failed. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9464,12 +8336,7 @@ To successfully implement this search you need to obtain data from your backup s ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -9489,7 +8356,7 @@ None identified ---- -===Windows DisableAntiSpyware Registry=== +===Windows disableantispyware registry=== The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9519,16 +8386,7 @@ You must be ingesting data that records the process-system activity from your ho ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.001 -| Disable or Modify Tools -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -9580,16 +8438,7 @@ You must be ingesting data that records the process-system activity from your ho ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1059.003 -| Windows Command Shell -| Execution -|} + ====Kill Chain Phase==== @@ -9642,12 +8491,7 @@ To successfully implement this search, you must be ingesting data that records t ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -9674,7 +8518,7 @@ There may be legitimate reasons for system administrators to add entries to this ==Endpoint== -===Access LSASS Memory for Dump Creation=== +===Access lsass memory for dump creation=== Detect memory dumping of the LSASS process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -9704,16 +8548,7 @@ This search requires Sysmon Logs and a Sysmon configuration, which includes Even ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.001 -| LSASS Memory -| Credential Access -|} + ====Kill Chain Phase==== @@ -9739,7 +8574,7 @@ Administrators can create memory dumps for debugging purposes, but memory dumps ---- -===Applying Stolen Credentials via Mimikatz modules=== +===Applying stolen credentials via mimikatz modules=== This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack. * '''Product''': UEBA for Security Cloud @@ -9777,6 +8612,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -9828,6 +8664,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Credential Access |} + ====Kill Chain Phase==== * Actions on Objectives @@ -9852,7 +8689,7 @@ None identified. ---- -===Applying Stolen Credentials via PowerSploit modules=== +===Applying stolen credentials via powersploit modules=== Stolen credentials are applied by methods such as user impersonation, credential injection, spoofing of authentication processes or getting hold of critical accounts. This detection indicates such activities carried out by PowerSploit exploit kit APIs. * '''Product''': UEBA for Security Cloud @@ -9890,6 +8727,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -9941,6 +8779,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Credential Access |} + ====Kill Chain Phase==== * Actions on Objectives @@ -9963,7 +8802,7 @@ None identified. ---- -===Assessment of Credential Strength via DSInternals modules=== +===Assessment of credential strength via dsinternals modules=== This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised. * '''Product''': UEBA for Security Cloud @@ -10001,6 +8840,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -10032,6 +8872,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Credential Access |} + ====Kill Chain Phase==== * Actions on Objectives @@ -10054,7 +8895,7 @@ None identified. ---- -===Attempt To Add Certificate To Untrusted Store=== +===Attempt to add certificate to untrusted store=== Attempt to add a certificate to the certificate store * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10084,16 +8925,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1553.004 -| Install Root Certificate -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -10119,7 +8951,7 @@ There may be legitimate reasons for administrators to add a certificate to the u ---- -===Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass=== +===Attempt to set default powershell execution policy to unrestricted or bypass=== Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10151,16 +8983,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1059.001 -| PowerShell -| Execution -|} + ====Kill Chain Phase==== @@ -10186,7 +9009,7 @@ Administrators may attempt to change the default execution policy on a system fo ---- -===Attempt To Stop Security Service=== +===Attempt to stop security service=== This search looks for attempts to stop security-related services on the endpoint. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10218,16 +9041,7 @@ You must be ingesting data that records the file-system activity from your hosts ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.001 -| Disable or Modify Tools -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -10253,7 +9067,7 @@ None identified. Attempts to disable security-related services should be identif ---- -===Attempted Credential Dump From Registry via Reg exe=== +===Attempted credential dump from registry via reg exe=== Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10283,16 +9097,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.002 -| Security Account Manager -| Credential Access -|} + ====Kill Chain Phase==== @@ -10316,7 +9121,7 @@ None identified. ---- -===Attempted Credential Dump From Registry via Reg exe=== +===Attempted credential dump from registry via reg exe=== Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. * '''Product''': UEBA for Security Cloud @@ -10358,16 +9163,7 @@ You must be ingesting windows endpoint data that tracks process activity, includ * process -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003 -| OS Credential Dumping -| Credential Access -|} + ====Kill Chain Phase==== @@ -10391,7 +9187,7 @@ None identified. ---- -===BCDEdit Failure Recovery Modification=== +===Bcdedit failure recovery modification=== This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10423,16 +9219,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1490 -| Inhibit System Recovery -| Impact -|} + ====Kill Chain Phase==== @@ -10458,7 +9245,7 @@ Administrators may modify the boot configuration. ---- -===Batch File Write to System32=== +===Batch file write to system32=== The search looks for a batch file (.bat) written to the Windows system directory tree. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10490,16 +9277,7 @@ You must be ingesting data that records the file-system activity from your hosts ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1204.002 -| Malicious File -| Execution -|} + ====Kill Chain Phase==== @@ -10555,12 +9333,7 @@ This search looks for arguments to certutil.exe indicating the manipulation or e ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -10584,7 +9357,7 @@ Unless there are specific use cases, manipulating or exporting certificates usin ---- -===Child Processes of Spoolsv exe=== +===Child processes of spoolsv exe=== This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10614,16 +9387,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1068 -| Exploitation for Privilege Escalation -| Privilege Escalation -|} + ====Kill Chain Phase==== @@ -10645,7 +9409,7 @@ Some legitimate printer-related processes may show up as children of spoolsv.exe ---- -===Common Ransomware Extensions=== +===Common ransomware extensions=== The search looks for file modifications with extensions commonly used by Ransomware * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10685,16 +9449,7 @@ Detailed documentation on how to create a new field within Incident Review may b ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1485 -| Data Destruction -| Impact -|} + ====Kill Chain Phase==== @@ -10718,7 +9473,7 @@ It is possible for a legitimate file with these extensions to be created. If thi ---- -===Common Ransomware Notes=== +===Common ransomware notes=== The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10753,16 +9508,7 @@ You must be ingesting data that records file-system activity from your hosts to ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1485 -| Data Destruction -| Impact -|} + ====Kill Chain Phase==== @@ -10786,7 +9532,7 @@ It's possible that a legitimate file could be created with the same name used by ---- -===Create Remote Thread into LSASS=== +===Create remote thread into lsass=== Detect remote thread creation into LSASS consistent with credential dumping. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -10816,16 +9562,7 @@ This search needs Sysmon Logs with a Sysmon configuration, which includes EventC ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.001 -| LSASS Memory -| Credential Access -|} + ====Kill Chain Phase==== @@ -10881,16 +9618,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1136.001 -| Local Account -| Persistence -|} + ====Kill Chain Phase==== @@ -10949,16 +9677,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1070.005 -| Network Share Connection Removal -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -10984,7 +9703,7 @@ Administrators often leverage net.exe to create or delete network shares. You sh ---- -===Creation of Shadow Copy=== +===Creation of shadow copy=== Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -11014,16 +9733,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.003 -| NTDS -| Credential Access -|} + ====Kill Chain Phase==== @@ -11049,7 +9759,7 @@ Legitimate administrator usage of Vssadmin or Wmic will create false positives. ---- -===Creation of Shadow Copy with wmic and powershell=== +===Creation of shadow copy with wmic and powershell=== This search detects the use of wmic and Powershell to create a shadow copy. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -11079,16 +9789,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.003 -| NTDS -| Credential Access -|} + ====Kill Chain Phase==== @@ -11114,7 +9815,7 @@ Legtimate administrator usage of wmic to create a shadow copy. ---- -===Creation of lsass Dump with Taskmgr=== +===Creation of lsass dump with taskmgr=== Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -11144,16 +9845,7 @@ This search requires Sysmon Logs and a Sysmon configuration, which includes Even ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.001 -| LSASS Memory -| Credential Access -|} + ====Kill Chain Phase==== @@ -11183,7 +9875,7 @@ Administrators can create memory dumps for debugging purposes, but memory dumps ---- -===Credential Dumping via Copy Command from Shadow Copy=== +===Credential dumping via copy command from shadow copy=== This search detects credential dumping using copy command from a shadow copy. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -11213,16 +9905,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.003 -| NTDS -| Credential Access -|} + ====Kill Chain Phase==== @@ -11248,7 +9931,7 @@ unknown ---- -===Credential Dumping via Symlink to Shadow Copy=== +===Credential dumping via symlink to shadow copy=== This search detects the creation of a symlink to a shadow copy. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -11278,16 +9961,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.003 -| NTDS -| Credential Access -|} + ====Kill Chain Phase==== @@ -11313,7 +9987,7 @@ unknown ---- -===Credential Extraction indicative of FGDump and CacheDump with s option=== +===Credential extraction indicative of fgdump and cachedump with s option=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. * '''Product''': UEBA for Security Cloud @@ -11357,16 +10031,7 @@ You must be ingesting Windows Security logs from devices of interest, including * process -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003 -| OS Credential Dumping -| Credential Access -|} + ====Kill Chain Phase==== @@ -11388,7 +10053,7 @@ None identified. ---- -===Credential Extraction indicative of FGDump and CacheDump with v option=== +===Credential extraction indicative of fgdump and cachedump with v option=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry. * '''Product''': UEBA for Security Cloud @@ -11430,16 +10095,7 @@ You must be ingesting Windows Security logs from devices of interest, including * process -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003 -| OS Credential Dumping -| Credential Access -|} + ====Kill Chain Phase==== @@ -11461,7 +10117,7 @@ None identified. ---- -===Credential Extraction indicative of Lazagne command line options=== +===Credential extraction indicative of lazagne command line options=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc. * '''Product''': UEBA for Security Cloud @@ -11499,6 +10155,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -11514,6 +10171,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Credential Access |} + ====Kill Chain Phase==== * Actions on Objectives @@ -11534,7 +10192,7 @@ None identified. ---- -===Credential Extraction indicative of use of DSInternals credential conversion modules=== +===Credential extraction indicative of use of dsinternals credential conversion modules=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. * '''Product''': UEBA for Security Cloud @@ -11578,16 +10236,7 @@ You must be ingesting Windows Security logs from devices of interest, including * process -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003 -| OS Credential Dumping -| Credential Access -|} + ====Kill Chain Phase==== @@ -11611,7 +10260,7 @@ None identified. ---- -===Credential Extraction indicative of use of DSInternals modules=== +===Credential extraction indicative of use of dsinternals modules=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits. * '''Product''': UEBA for Security Cloud @@ -11655,16 +10304,7 @@ You must be ingesting Windows Security logs from devices of interest, including * process -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003 -| OS Credential Dumping -| Credential Access -|} + ====Kill Chain Phase==== @@ -11688,7 +10328,7 @@ None identified. ---- -===Credential Extraction indicative of use of Mimikatz modules=== +===Credential extraction indicative of use of mimikatz modules=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits. * '''Product''': UEBA for Security Cloud @@ -11726,16 +10366,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003 -| OS Credential Dumping -| Credential Access -|} + ====Kill Chain Phase==== @@ -11759,7 +10390,7 @@ None identified. ---- -===Credential Extraction indicative of use of PowerSploit modules=== +===Credential extraction indicative of use of powersploit modules=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits. * '''Product''': UEBA for Security Cloud @@ -11797,16 +10428,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003 -| OS Credential Dumping -| Credential Access -|} + ====Kill Chain Phase==== @@ -11830,7 +10452,7 @@ None identified. ---- -===Credential Extraction native Microsoft debuggers peek into the kernel=== +===Credential extraction native microsoft debuggers peek into the kernel=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. * '''Product''': UEBA for Security Cloud @@ -11872,16 +10494,7 @@ You must be ingesting Windows Security logs from devices of interest, including * process -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003 -| OS Credential Dumping -| Credential Access -|} + ====Kill Chain Phase==== @@ -11905,7 +10518,7 @@ Although unlikely, using debuggers this way may be indicative of developers anal ---- -===Credential Extraction native Microsoft debuggers via z command line option=== +===Credential extraction native microsoft debuggers via z command line option=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps. * '''Product''': UEBA for Security Cloud @@ -11945,16 +10558,7 @@ You must be ingesting Windows Security logs from devices of interest, including * process -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003 -| OS Credential Dumping -| Credential Access -|} + ====Kill Chain Phase==== @@ -11976,7 +10580,7 @@ Although unlikely, using debuggers this way may be indicative of developers anal ---- -===Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals=== +===Credential extraction via get-addbaccount module present in powersploit and dsinternals=== Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments. * '''Product''': UEBA for Security Cloud @@ -12015,16 +10619,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003 -| OS Credential Dumping -| Credential Access -|} + ====Kill Chain Phase==== @@ -12046,7 +10641,7 @@ None identified. ---- -===Deleting Shadow Copies=== +===Deleting shadow copies=== The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12080,16 +10675,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1490 -| Inhibit System Recovery -| Impact -|} + ====Kill Chain Phase==== @@ -12113,7 +10699,7 @@ vssadmin.exe and wmic.exe are standard applications shipped with modern versions ---- -===Detect Activity Related to Pass the Hash Attacks=== +===Detect activity related to pass the hash attacks=== This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12143,16 +10729,7 @@ To successfully implement this search, you must ingest your Windows Security Eve ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1550.002 -| Pass the Hash -| Defense Evasion, Lateral Movement -|} + ====Kill Chain Phase==== @@ -12176,7 +10753,7 @@ Legitimate logon activity by authorized NTLM systems may be detected by this sea ---- -===Detect Baron Samedit CVE-2021-3156=== +===Detect baron samedit cve-2021-3156=== This search detects the heap-based buffer overflow of sudoedit * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12203,16 +10780,7 @@ Splunk Universal Forwarder running on Linux systems, capturing logs from the /va ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1068 -| Exploitation for Privilege Escalation -| Privilege Escalation -|} + ====Kill Chain Phase==== @@ -12236,7 +10804,7 @@ unknown ---- -===Detect Baron Samedit CVE-2021-3156 Segfault=== +===Detect baron samedit cve-2021-3156 segfault=== This search detects the heap-based buffer overflow of sudoedit * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12265,16 +10833,7 @@ Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1068 -| Exploitation for Privilege Escalation -| Privilege Escalation -|} + ====Kill Chain Phase==== @@ -12298,7 +10857,7 @@ If sudoedit is throwing segfaults for other reasons this will pick those up too. ---- -===Detect Baron Samedit CVE-2021-3156 via OSQuery=== +===Detect baron samedit cve-2021-3156 via osquery=== This search detects the heap-based buffer overflow of sudoedit * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12325,16 +10884,7 @@ OSQuery installed and configured to pick up process events (info at https://osqu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1068 -| Exploitation for Privilege Escalation -| Privilege Escalation -|} + ====Kill Chain Phase==== @@ -12358,7 +10908,7 @@ unknown ---- -===Detect Computer Changed with Anonymous Account=== +===Detect computer changed with anonymous account=== This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12385,16 +10935,7 @@ This search requires audit computer account management to be enabled on the syst ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1210 -| Exploitation of Remote Services -| Lateral Movement -|} + ====Kill Chain Phase==== @@ -12418,7 +10959,7 @@ None thus far found ---- -===Detect Credential Dumping through LSASS access=== +===Detect credential dumping through lsass access=== This search looks for reading lsass memory consistent with credential dumping. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12450,16 +10991,7 @@ This search needs Sysmon Logs and a sysmon configuration, which includes EventCo ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.001 -| LSASS Memory -| Credential Access -|} + ====Kill Chain Phase==== @@ -12483,7 +11015,7 @@ The activity may be legitimate. Other tools can access lsass for legitimate reas ---- -===Detect Dump LSASS Memory using comsvcs=== +===Detect dump lsass memory using comsvcs=== This search detects the memory of lsass.exe being dumped for offline credential theft attack. * '''Product''': UEBA for Security Cloud @@ -12523,16 +11055,7 @@ You must be ingesting endpoint data that tracks process activity, including Wind * process -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.003 -| NTDS -| Credential Access -|} + ====Kill Chain Phase==== @@ -12556,7 +11079,7 @@ None identified. ---- -===Detect Excessive Account Lockouts From Endpoint=== +===Detect excessive account lockouts from endpoint=== This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12592,16 +11115,7 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "Exc ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.002 -| Domain Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -12625,7 +11139,7 @@ It's possible that a widely used system, such as a kiosk, could cause a large nu ---- -===Detect Excessive User Account Lockouts=== +===Detect excessive user account lockouts=== This search detects user accounts that have been locked out a relatively high number of times in a short period. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12657,16 +11171,7 @@ ou must ingest your Windows security event logs in the `Change` datamodel under ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.003 -| Local Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -12690,7 +11195,7 @@ It is possible that a legitimate user is experiencing an issue causing multiple ---- -===Detect HTML Help Renamed=== +===Detect html help renamed=== The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12720,16 +11225,7 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.001 -| Compiled HTML File -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -12759,7 +11255,7 @@ Although unlikely a renamed instance of hh.exe will be used legitimately, filter ---- -===Detect HTML Help Spawn Child Process=== +===Detect html help spawn child process=== The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12789,16 +11285,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.001 -| Compiled HTML File -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -12832,7 +11319,7 @@ Although unlikely, some legitimate applications (ex. web browsers) may spawn a c ---- -===Detect HTML Help URL in Command Line=== +===Detect html help url in command line=== The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12862,16 +11349,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.001 -| Compiled HTML File -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -12907,7 +11385,7 @@ Although unlikely, some legitimate applications may retrieve a CHM remotely, fil ---- -===Detect HTML Help Using InfoTech Storage Handlers=== +===Detect html help using infotech storage handlers=== The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -12937,16 +11415,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.001 -| Compiled HTML File -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -12982,7 +11451,7 @@ It is rare to see instances of InfoTech Storage Handlers being used, but it does ---- -===Detect Kerberoasting=== +===Detect kerberoasting=== This search detects a potential kerberoasting attack via service principal name requests * '''Product''': UEBA for Security Cloud @@ -13025,16 +11494,7 @@ The test data is converted from Windows Security Event logs generated from Attac * ticket_options -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1558.003 -| Kerberoasting -| Credential Access -|} + ====Kill Chain Phase==== @@ -13058,7 +11518,7 @@ Older systems that support kerberos RC4 by default NetApp may generate false pos ---- -===Detect MSHTA Url in Command Line=== +===Detect mshta url in command line=== This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13088,16 +11548,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.005 -| Mshta -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -13127,7 +11578,7 @@ It is possible legitimate applications may perform this behavior and will need t ---- -===Detect New Local Admin account=== +===Detect new local admin account=== This search looks for newly created accounts that have been elevated to local administrators. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13158,16 +11609,7 @@ You must be ingesting Windows event logs using the Splunk Windows TA and collect ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1136.001 -| Local Account -| Persistence -|} + ====Kill Chain Phase==== @@ -13197,7 +11639,7 @@ The activity may be legitimate. For this reason, it's best to verify the account ---- -===Detect Oulook exe writing a zip file=== +===Detect oulook exe writing a zip file=== This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13238,16 +11680,7 @@ You must be ingesting data that records filesystem and process activity from you ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1566.001 -| Spearphishing Attachment -| Initial Access -|} + ====Kill Chain Phase==== @@ -13271,7 +11704,7 @@ It is not uncommon for outlook to write legitimate zip files to the disk. ---- -===Detect Pass the Hash=== +===Detect pass the hash=== This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique. * '''Product''': UEBA for Security Cloud @@ -13314,16 +11747,7 @@ The test data is converted from Windows Security Event logs generated from Attac * logon_type -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1550.002 -| Pass the Hash -| Defense Evasion, Lateral Movement -|} + ====Kill Chain Phase==== @@ -13347,7 +11771,7 @@ Legitimate logon activity by authorized NTLM systems may be detected by this sea ---- -===Detect Path Interception By Creation Of program exe=== +===Detect path interception by creation of program exe=== The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13384,16 +11808,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1574.009 -| Path Interception by Unquoted Path -| Defense Evasion, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -13419,7 +11834,7 @@ unknown ---- -===Detect Prohibited Applications Spawning cmd exe=== +===Detect prohibited applications spawning cmd exe=== This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13456,16 +11871,7 @@ You must be ingesting data that records process activity from your hosts and pop ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1059.003 -| Windows Command Shell -| Execution -|} + ====Kill Chain Phase==== @@ -13489,7 +11895,7 @@ There are circumstances where an application may legitimately execute and intera ---- -===Detect Prohibited Applications Spawning cmd exe=== +===Detect prohibited applications spawning cmd exe=== This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. This is a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe` by @bpatel. * '''Product''': UEBA for Security Cloud @@ -13533,16 +11939,7 @@ You must be ingesting sysmon logs. This search has been modified to process raw * dest_user_id -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1059 -| Command and Scripting Interpreter -| Execution -|} + ====Kill Chain Phase==== @@ -13564,7 +11961,7 @@ There are circumstances where an application may legitimately execute and intera ---- -===Detect PsExec With accepteula Flag=== +===Detect psexec with accepteula flag=== This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13596,16 +11993,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1021.002 -| SMB/Windows Admin Shares -| Lateral Movement -|} + ====Kill Chain Phase==== @@ -13629,7 +12017,7 @@ Administrators can leverage PsExec for accessing remote systems and might pass ` ---- -===Detect Rare Executables=== +===Detect rare executables=== This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13670,12 +12058,7 @@ To successfully implement this search, you must be ingesting data that records p ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -13701,7 +12084,7 @@ Some legitimate processes may be only rarely executed in your environment. As th ---- -===Detect Regasm Spawning a Process=== +===Detect regasm spawning a process=== The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13731,16 +12114,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.009 -| Regsvcs/Regasm -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -13772,7 +12146,7 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa ---- -===Detect Regasm with Network Connection=== +===Detect regasm with network connection=== The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13802,16 +12176,7 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.009 -| Regsvcs/Regasm -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -13841,7 +12206,7 @@ Although unlikely, limited instances of regasm.exe with a network connection may ---- -===Detect Regasm with no Command Line Arguments=== +===Detect regasm with no command line arguments=== The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13872,16 +12237,7 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.009 -| Regsvcs/Regasm -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -13911,7 +12267,7 @@ Although unlikely, limited instances of regasm.exe or may cause a false positive ---- -===Detect Regsvcs Spawning a Process=== +===Detect regsvcs spawning a process=== The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -13941,16 +12297,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.009 -| Regsvcs/Regasm -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -13980,7 +12327,7 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa ---- -===Detect Regsvcs with Network Connection=== +===Detect regsvcs with network connection=== The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14010,16 +12357,7 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.009 -| Regsvcs/Regasm -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -14049,7 +12387,7 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive. ---- -===Detect Regsvcs with No Command Line Arguments=== +===Detect regsvcs with no command line arguments=== The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14080,16 +12418,7 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.009 -| Regsvcs/Regasm -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -14119,7 +12448,7 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive. ---- -===Detect Regsvr32 Application Control Bypass=== +===Detect regsvr32 application control bypass=== Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. @@ -14150,16 +12479,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.010 -| Regsvr32 -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -14191,7 +12511,7 @@ Limited false positives related to third party software registering .DLL's. ---- -===Detect Rundll32 Application Control Bypass - advpack=== +===Detect rundll32 application control bypass - advpack=== The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14221,16 +12541,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.011 -| Rundll32 -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -14264,7 +12575,7 @@ Although unlikely, some legitimate applications may use advpack.dll or ieadvpack ---- -===Detect Rundll32 Application Control Bypass - setupapi=== +===Detect rundll32 application control bypass - setupapi=== The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14294,16 +12605,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.011 -| Rundll32 -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -14337,7 +12639,7 @@ Although unlikely, some legitimate applications may use setupapi triggering a fa ---- -===Detect Rundll32 Application Control Bypass - syssetup=== +===Detect rundll32 application control bypass - syssetup=== The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14367,16 +12669,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.011 -| Rundll32 -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -14410,7 +12703,7 @@ Although unlikely, some legitimate applications may use syssetup.dll, triggering ---- -===Detect Rundll32 Inline HTA Execution=== +===Detect rundll32 inline hta execution=== The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14440,16 +12733,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.005 -| Mshta -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -14479,7 +12763,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg ---- -===Detect Use of cmd exe to Launch Script Interpreters=== +===Detect use of cmd exe to launch script interpreters=== This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14511,16 +12795,7 @@ To successfully implement this search, you must be ingesting data that records p ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1059.003 -| Windows Command Shell -| Execution -|} + ====Kill Chain Phase==== @@ -14574,16 +12849,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.005 -| Mshta -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -14643,16 +12909,7 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.005 -| Mshta -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -14680,7 +12937,7 @@ Although unlikely, some legitimate applications may use a moved copy of mshta.ex ---- -===Detect processes used for System Network Configuration Discovery=== +===Detect processes used for system network configuration discovery=== This search looks for fast execution of processes used for system network configuration discovery on the endpoint. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14714,16 +12971,7 @@ You must be ingesting data that records registry activity from your hosts to pop ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1016 -| System Network Configuration Discovery -| Discovery -|} + ====Kill Chain Phase==== @@ -14751,7 +12999,7 @@ It is uncommon for normal users to execute a series of commands used for network ---- -===Detection of tools built by NirSoft=== +===Detection of tools built by nirsoft=== This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14781,16 +13029,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1072 -| Software Deployment Tools -| Execution, Lateral Movement -|} + ====Kill Chain Phase==== @@ -14814,7 +13053,7 @@ While legitimate, these NirSoft tools are prone to abuse. You should verfiy that ---- -===Disabling Remote User Account Control=== +===Disabling remote user account control=== The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14844,16 +13083,7 @@ To successfully implement this search, you must be ingesting data that records r ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1548.002 -| Bypass User Account Control -| Defense Evasion, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -14877,7 +13107,7 @@ This registry key may be modified via administrators to implement a change in sy ---- -===Dump LSASS via comsvcs DLL=== +===Dump lsass via comsvcs dll=== Detect the usage of comsvcs.dll for dumping the lsass process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -14909,16 +13139,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.001 -| LSASS Memory -| Credential Access -|} + ====Kill Chain Phase==== @@ -14946,7 +13167,7 @@ None identified. ---- -===Dump LSASS via procdump=== +===Dump lsass via procdump=== Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\ During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. @@ -14977,16 +13198,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.001 -| LSASS Memory -| Credential Access -|} + ====Kill Chain Phase==== @@ -15016,7 +13228,7 @@ None identified. ---- -===Dump LSASS via procdump Rename=== +===Dump lsass via procdump rename=== Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\ During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. @@ -15047,16 +13259,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.001 -| LSASS Memory -| Credential Access -|} + ====Kill Chain Phase==== @@ -15086,7 +13289,7 @@ None identified. ---- -===Execution of File with Multiple Extensions=== +===Execution of file with multiple extensions=== This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15116,16 +13319,7 @@ To successfully implement this search, you must be ingesting data that records p ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1036.003 -| Rename System Utilities -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -15149,7 +13343,7 @@ None identified. ---- -===File with Samsam Extension=== +===File with samsam extension=== The search looks for file writes with extensions consistent with a SamSam ransomware attack. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15181,12 +13375,7 @@ You must be ingesting data that records file-system activity from your hosts to ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -15210,7 +13399,7 @@ Because these extensions are not typically used in normal operations, you should ---- -===First Time Seen Child Process of Zoom=== +===First time seen child process of zoom=== This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15242,16 +13431,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1068 -| Exploitation for Privilege Escalation -| Privilege Escalation -|} + ====Kill Chain Phase==== @@ -15275,7 +13455,7 @@ A new child process of zoom isn't malicious by that fact alone. Further investig ---- -===First Time Seen Running Windows Service=== +===First time seen running windows service=== This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15310,16 +13490,7 @@ While this search does not require you to adhere to Splunk CIM, you must be inge ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1569.002 -| Service Execution -| Execution -|} + ====Kill Chain Phase==== @@ -15348,7 +13519,7 @@ This search looks for command-line arguments that use a `/c` parameter to execut * '''Product''': UEBA for Security Cloud * '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059/ T1059], [https://attack.mitre.org/techniques/T1117/ T1117], [https://attack.mitre.org/techniques/T1202/ T1202] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059/ T1059], [https://attack.mitre.org/techniques// ], [https://attack.mitre.org/techniques/T1202/ T1202] * '''Last Updated''': 2021-2-1
@@ -15387,6 +13558,7 @@ You must be populating the endpoint data model for SSA and specifically the proc * process + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -15406,6 +13578,7 @@ You must be populating the endpoint data model for SSA and specifically the proc | Defense Evasion |} + ====Kill Chain Phase==== * Command and Control @@ -15428,7 +13601,7 @@ Legitimate programs can also use command-line arguments to execute. Please verif ---- -===Hiding Files And Directories With Attrib exe=== +===Hiding files and directories with attrib exe=== Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -15460,16 +13633,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1222.001 -| Windows File and Directory Permissions Modification -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -15493,7 +13657,7 @@ Some applications and users may legitimately use attrib.exe to interact with the ---- -===Illegal Access To User Content via PowerSploit modules=== +===Illegal access to user content via powersploit modules=== This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc. * '''Product''': UEBA for Security Cloud @@ -15531,6 +13695,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -15554,6 +13719,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Lateral Movement |} + ====Kill Chain Phase==== * Actions on Objectives @@ -15576,7 +13742,7 @@ None identified. ---- -===Illegal Account Creation via PowerSploit modules=== +===Illegal account creation via powersploit modules=== This detection identifies access to PowerSploit modules that create accounts illegaly. * '''Product''': UEBA for Security Cloud @@ -15614,16 +13780,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1585 -| Establish Accounts -| Resource Development -|} + ====Kill Chain Phase==== @@ -15647,7 +13804,7 @@ None identified. ---- -===Illegal Deletion of Logs via Mimikatz modules=== +===Illegal deletion of logs via mimikatz modules=== This detection identifies access to PowerSploit modules that delete event logs. * '''Product''': UEBA for Security Cloud @@ -15685,16 +13842,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1070 -| Indicator Removal on Host -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -15718,7 +13866,7 @@ None identified. ---- -===Illegal Enabling or Disabling of Accounts via DSInternals modules=== +===Illegal enabling or disabling of accounts via dsinternals modules=== This detection identifies use of DSInternals modules that enable or disable accounts illegaly. * '''Product''': UEBA for Security Cloud @@ -15756,6 +13904,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -15771,6 +13920,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Persistence |} + ====Kill Chain Phase==== * Actions on Objectives @@ -15793,7 +13943,7 @@ None identified. ---- -===Illegal Management of Active Directory Elements and Policies via DSInternals modules=== +===Illegal management of active directory elements and policies via dsinternals modules=== This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies. * '''Product''': UEBA for Security Cloud @@ -15831,6 +13981,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -15850,6 +14001,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Defense Evasion, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -15872,7 +14024,7 @@ None identified. ---- -===Illegal Management of Computers and Active Directory Elements via PowerSploit modules=== +===Illegal management of computers and active directory elements via powersploit modules=== This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements. * '''Product''': UEBA for Security Cloud @@ -15911,6 +14063,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -15930,6 +14083,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Defense Evasion, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -15952,7 +14106,7 @@ None identified. ---- -===Illegal Privilege Elevation and Persistence via PowerSploit modules=== +===Illegal privilege elevation and persistence via powersploit modules=== This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities. * '''Product''': UEBA for Security Cloud @@ -15990,6 +14144,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -16009,6 +14164,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Defense Evasion, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -16031,7 +14187,7 @@ None identified. ---- -===Illegal Privilege Elevation via Mimikatz modules=== +===Illegal privilege elevation via mimikatz modules=== This detection identifies use of Mimikatz modules for illegal privilege elevation. * '''Product''': UEBA for Security Cloud @@ -16069,6 +14225,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -16084,6 +14241,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Defense Evasion, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -16106,7 +14264,7 @@ None identified. ---- -===Illegal Service and Process Control via Mimikatz modules=== +===Illegal service and process control via mimikatz modules=== This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service. * '''Product''': UEBA for Security Cloud @@ -16144,6 +14302,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -16163,6 +14322,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Execution |} + ====Kill Chain Phase==== * Actions on Objectives @@ -16185,7 +14345,7 @@ None identified. ---- -===Illegal Service and Process Control via PowerSploit modules=== +===Illegal service and process control via powersploit modules=== This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc. * '''Product''': UEBA for Security Cloud @@ -16224,6 +14384,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -16243,6 +14404,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Execution |} + ====Kill Chain Phase==== * Actions on Objectives @@ -16265,7 +14427,7 @@ None identified. ---- -===Kerberoasting spn request with RC4 encryption=== +===Kerberoasting spn request with rc4 encryption=== This search detects a potential kerberoasting attack via service principal name requests * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16294,16 +14456,7 @@ You must be ingesting endpoint data that tracks process activity, and include th ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1558.003 -| Kerberoasting -| Credential Access -|} + ====Kill Chain Phase==== @@ -16331,7 +14484,7 @@ Older systems that support kerberos RC4 by default NetApp may generate false pos ---- -===MacOS - Re-opened Applications=== +===Macos - re-opened applications=== This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16359,12 +14512,7 @@ In order to properly run this search, Splunk needs to ingest process data from y ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -16388,7 +14536,7 @@ At this stage, there are no known false positives. During testing, no process ev ---- -===Malicious PowerShell Process - Connect To Internet With Hidden Window=== +===Malicious powershell process - connect to internet with hidden window=== This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16420,16 +14568,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1059.001 -| PowerShell -| Execution -|} + ====Kill Chain Phase==== @@ -16455,7 +14594,7 @@ Legitimate process can have this combination of command-line options, but it's n ---- -===Malicious PowerShell Process - Encoded Command=== +===Malicious powershell process - encoded command=== This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16487,16 +14626,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1027 -| Obfuscated Files or Information -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -16522,7 +14652,7 @@ System administrators may use this option, but it's not common. ---- -===Malicious PowerShell Process - Execution Policy Bypass=== +===Malicious powershell process - execution policy bypass=== This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16552,16 +14682,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1059.001 -| PowerShell -| Execution -|} + ====Kill Chain Phase==== @@ -16587,7 +14708,7 @@ There may be legitimate reasons to bypass the PowerShell execution policy. The P ---- -===Malicious PowerShell Process With Obfuscation Techniques=== +===Malicious powershell process with obfuscation techniques=== This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16619,16 +14740,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1059.001 -| PowerShell -| Execution -|} + ====Kill Chain Phase==== @@ -16654,7 +14766,7 @@ These characters might be legitimately on the command-line, but it is not common ---- -===Monitor Registry Keys for Print Monitors=== +===Monitor registry keys for print monitors=== This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16684,16 +14796,7 @@ To successfully implement this search, you must be ingesting data that records r ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1547.010 -| Port Monitors -| Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -16717,7 +14820,7 @@ You will encounter noise from legitimate print-monitor registry entries. ---- -===More than usual number of LOLBAS applications in short time period=== +===More than usual number of lolbas applications in short time period=== Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine. * '''Product''': UEBA for Security Cloud @@ -16756,6 +14859,7 @@ Collect endpoint data such as sysmon or 4688 events. * process_name + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -16771,6 +14875,7 @@ Collect endpoint data such as sysmon or 4688 events. | Execution, Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Exploitation @@ -16794,7 +14899,7 @@ Some administrative tasks may involve multiple use of LOLBAS applications in a s ---- -===NLTest Domain Trust Discovery=== +===Nltest domain trust discovery=== This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16824,16 +14929,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1482 -| Domain Trust Discovery -| Discovery -|} + ====Kill Chain Phase==== @@ -16903,16 +14999,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.003 -| NTDS -| Credential Access -|} + ====Kill Chain Phase==== @@ -16944,7 +15031,7 @@ Highly possible Server Administrators will troubleshoot with ntdsutil.exe, gener ---- -===Overwriting Accessibility Binaries=== +===Overwriting accessibility binaries=== Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -16974,16 +15061,7 @@ You must be ingesting data that records the filesystem activity from your hosts ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1546.008 -| Accessibility Features -| Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -17007,7 +15085,7 @@ Microsoft may provide updates to these binaries. Verify that these changes do no ---- -===Probing Access with Stolen Credentials via PowerSploit modules=== +===Probing access with stolen credentials via powersploit modules=== This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services. * '''Product''': UEBA for Security Cloud @@ -17045,6 +15123,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_device_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -17060,6 +15139,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Persistence |} + ====Kill Chain Phase==== * Actions on Objectives @@ -17082,7 +15162,7 @@ None identified. ---- -===Process Creating LNK file in Suspicious Location=== +===Process creating lnk file in suspicious location=== This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -17119,16 +15199,7 @@ You must be ingesting data that records filesystem and process activity from you ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1566.002 -| Spearphishing Link -| Initial Access -|} + ====Kill Chain Phase==== @@ -17158,7 +15229,7 @@ This detection should yield little or no false positive results. It is uncommon ---- -===Process Execution via WMI=== +===Process execution via wmi=== This search looks for processes launched via WMI. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -17188,16 +15259,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1047 -| Windows Management Instrumentation -| Execution -|} + ====Kill Chain Phase==== @@ -17221,7 +15283,7 @@ Although unlikely, administrators may use wmi to execute commands for legitimate ---- -===Processes Tapping Keyboard Events=== +===Processes tapping keyboard events=== This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -17252,12 +15314,7 @@ In order to properly run this search, Splunk needs to ingest data from your osqu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -17313,16 +15370,7 @@ To successfully implement this search, you must be ingesting data that records p ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.004 -| Disable or Modify System Firewall -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -17346,7 +15394,7 @@ Some VPN applications are known to launch netsh.exe. Outside of these instances, ---- -===Rare Parent-Child Process Relationship=== +===Rare parent-child process relationship=== An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries * '''Product''': UEBA for Security Cloud @@ -17392,6 +15440,7 @@ Collect endpoint data such as sysmon or 4688 events. * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -17415,6 +15464,7 @@ Collect endpoint data such as sysmon or 4688 events. | Execution, Lateral Movement |} + ====Kill Chain Phase==== * Exploitation @@ -17436,7 +15486,7 @@ Some custom tools used by admins could be used rarely to launch remotely applica ---- -===Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules=== +===Reconnaissance and access to accounts groups and policies via powersploit modules=== This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over. * '''Product''': UEBA for Security Cloud @@ -17474,6 +15524,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -17493,6 +15544,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Defense Evasion, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -17515,7 +15567,7 @@ None identified. ---- -===Reconnaissance and Access to Accounts and Groups via Mimikatz modules=== +===Reconnaissance and access to accounts and groups via mimikatz modules=== This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them. * '''Product''': UEBA for Security Cloud @@ -17553,6 +15605,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -17572,6 +15625,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Defense Evasion, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -17594,7 +15648,7 @@ None identified. ---- -===Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules=== +===Reconnaissance and access to active directoty infrastructure via powersploit modules=== This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations. * '''Product''': UEBA for Security Cloud @@ -17632,6 +15686,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -17659,6 +15714,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Reconnaissance |} + ====Kill Chain Phase==== * Actions on Objectives @@ -17681,7 +15737,7 @@ None identified. ---- -===Reconnaissance and Access to Computers and Domains via PowerSploit modules=== +===Reconnaissance and access to computers and domains via powersploit modules=== This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over. * '''Product''': UEBA for Security Cloud @@ -17719,6 +15775,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -17738,6 +15795,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Discovery |} + ====Kill Chain Phase==== * Actions on Objectives @@ -17760,7 +15818,7 @@ None identified. ---- -===Reconnaissance and Access to Computers via Mimikatz modules=== +===Reconnaissance and access to computers via mimikatz modules=== This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them. * '''Product''': UEBA for Security Cloud @@ -17798,16 +15856,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1592 -| Gather Victim Host Information -| Reconnaissance -|} + ====Kill Chain Phase==== @@ -17831,7 +15880,7 @@ None identified. ---- -===Reconnaissance and Access to Operating System Elements via PowerSploit modules=== +===Reconnaissance and access to operating system elements via powersploit modules=== This detection identifies access to PowerSploit modules that discover and access operating system elements, such as processes, services, registry locations, security packages and files. * '''Product''': UEBA for Security Cloud @@ -17869,6 +15918,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -17908,6 +15958,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Reconnaissance |} + ====Kill Chain Phase==== * Actions on Objectives @@ -17930,7 +15981,7 @@ None identified. ---- -===Reconnaissance and Access to Processes and Services via Mimikatz modules=== +===Reconnaissance and access to processes and services via mimikatz modules=== This detection identifies use of Mimikatz modules for discovery and access to services and processes. * '''Product''': UEBA for Security Cloud @@ -17968,6 +16019,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -17987,6 +16039,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Discovery |} + ====Kill Chain Phase==== * Actions on Objectives @@ -18009,7 +16062,7 @@ None identified. ---- -===Reconnaissance and Access to Shared Resources via Mimikatz modules=== +===Reconnaissance and access to shared resources via mimikatz modules=== This detection identifies use of Mimikatz modules for discovery and access to network shares. * '''Product''': UEBA for Security Cloud @@ -18047,6 +16100,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -18066,6 +16120,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Collection |} + ====Kill Chain Phase==== * Actions on Objectives @@ -18088,7 +16143,7 @@ None identified. ---- -===Reconnaissance and Access to Shared Resources via PowerSploit modules=== +===Reconnaissance and access to shared resources via powersploit modules=== This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares. * '''Product''': UEBA for Security Cloud @@ -18126,6 +16181,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -18145,6 +16201,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Collection |} + ====Kill Chain Phase==== * Actions on Objectives @@ -18167,7 +16224,7 @@ None identified. ---- -===Reconnaissance of Access and Persistence Opportunities via PowerSploit modules=== +===Reconnaissance of access and persistence opportunities via powersploit modules=== This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised. * '''Product''': UEBA for Security Cloud @@ -18205,6 +16262,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -18236,6 +16294,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Defense Evasion, Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -18258,7 +16317,7 @@ None identified. ---- -===Reconnaissance of Connectivity via PowerSploit modules=== +===Reconnaissance of connectivity via powersploit modules=== This detection identifies access to PowerSploit modules for reconnaissance of connectivity. * '''Product''': UEBA for Security Cloud @@ -18296,6 +16355,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -18315,6 +16375,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Collection |} + ====Kill Chain Phase==== * Actions on Objectives @@ -18337,7 +16398,7 @@ None identified. ---- -===Reconnaissance of Credential Stores and Services via Mimikatz modules=== +===Reconnaissance of credential stores and services via mimikatz modules=== This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules. * '''Product''': UEBA for Security Cloud @@ -18375,6 +16436,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -18406,6 +16468,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Persistence |} + ====Kill Chain Phase==== * Actions on Objectives @@ -18428,7 +16491,7 @@ None identified. ---- -===Reconnaissance of Defensive Tools via PowerSploit modules=== +===Reconnaissance of defensive tools via powersploit modules=== This detection identifies use of PowerSploit modules for assessment of presence of defensive tools. * '''Product''': UEBA for Security Cloud @@ -18466,6 +16529,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -18481,6 +16545,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Reconnaissance |} + ====Kill Chain Phase==== * Actions on Objectives @@ -18503,7 +16568,7 @@ None identified. ---- -===Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules=== +===Reconnaissance of privilege escalation opportunities via powersploit modules=== This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities. * '''Product''': UEBA for Security Cloud @@ -18541,6 +16606,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -18560,6 +16626,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Persistence |} + ====Kill Chain Phase==== * Actions on Objectives @@ -18582,7 +16649,7 @@ None identified. ---- -===Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules=== +===Reconnaissance of process or service hijacking opportunities via mimikatz modules=== This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary. * '''Product''': UEBA for Security Cloud @@ -18620,6 +16687,7 @@ You must be ingesting Windows Security logs from devices of interest, including * dest_user_id + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -18639,6 +16707,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Defense Evasion, Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -18663,7 +16732,7 @@ None identified. ---- -===Reg exe Manipulating Windows Services Registry Keys=== +===Reg exe manipulating windows services registry keys=== The search looks for reg.exe modifying registry keys that define Windows services and their configurations. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -18695,16 +16764,7 @@ To successfully implement this search, you must be ingesting data that records r ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1574.011 -| Services Registry Permissions Weakness -| Defense Evasion, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -18728,7 +16788,7 @@ It is unusual for a service to be created or modified by directly manipulating t ---- -===Registry Keys Used For Persistence=== +===Registry keys used for persistence=== The search looks for modifications to registry keys that can be used to launch an application or service at system startup. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -18770,16 +16830,7 @@ To successfully implement this search, you must be ingesting data that records r ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1547.001 -| Registry Run Keys / Startup Folder -| Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -18803,7 +16854,7 @@ There are many legitimate applications that must execute on system startup and w ---- -===Registry Keys Used For Privilege Escalation=== +===Registry keys used for privilege escalation=== This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -18837,16 +16888,7 @@ To successfully implement this search, you must be ingesting data that records r ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1546.012 -| Image File Execution Options Injection -| Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -18872,7 +16914,7 @@ There are many legitimate applications that must execute upon system startup and ---- -===Registry Keys for Creating SHIM Databases=== +===Registry keys for creating shim databases=== This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -18904,16 +16946,7 @@ To successfully implement this search, you must populate the Change_Analysis dat ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1546.011 -| Application Shimming -| Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -18937,7 +16970,7 @@ There are many legitimate applications that leverage shim databases for compatib ---- -===Remote Desktop Process Running On System=== +===Remote desktop process running on system=== This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -18969,16 +17002,7 @@ To successfully implement this search, you must be ingesting data that records p ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1021.001 -| Remote Desktop Protocol -| Lateral Movement -|} + ====Kill Chain Phase==== @@ -19000,7 +17024,7 @@ Remote Desktop may be used legitimately by users on the network. ---- -===Remote Process Instantiation via WMI=== +===Remote process instantiation via wmi=== This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -19032,16 +17056,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1047 -| Windows Management Instrumentation -| Execution -|} + ====Kill Chain Phase==== @@ -19065,7 +17080,7 @@ The wmic.exe utility is a benign Windows application. It may be used legitimatel ---- -===RunDLL Loading DLL By Ordinal=== +===Rundll loading dll by ordinal=== This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -19095,16 +17110,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.011 -| Rundll32 -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -19128,7 +17134,7 @@ While not common, loading a DLL under %AppData% and calling a function by ordina ---- -===Ryuk Test Files Detected=== +===Ryuk test files detected=== The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -19158,16 +17164,7 @@ You must be ingesting data that records the filesystem activity from your hosts ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1486 -| Data Encrypted for Impact -| Impact -|} + ====Kill Chain Phase==== @@ -19191,7 +17188,7 @@ If there are files with this keywoord as file names it might trigger false possi ---- -===Samsam Test File Write=== +===Samsam test file write=== The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -19221,16 +17218,7 @@ You must be ingesting data that records the file-system activity from your hosts ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1486 -| Data Encrypted for Impact -| Impact -|} + ====Kill Chain Phase==== @@ -19254,7 +17242,7 @@ No false positives have been identified. ---- -===Sc exe Manipulating Windows Services=== +===Sc exe manipulating windows services=== This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -19294,16 +17282,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1543.003 -| Windows Service -| Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -19327,7 +17306,7 @@ Using sc.exe to manipulate Windows services is uncommon. However, there may be l ---- -===Scheduled Task Deleted Or Created via CMD=== +===Scheduled task deleted or created via cmd=== This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -19359,16 +17338,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1053.005 -| Scheduled Task -| Execution, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -19424,16 +17394,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1053.005 -| Scheduled Task -| Execution, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -19489,16 +17450,7 @@ To successfully implement this search you need to be ingesting logs with both th ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1053.005 -| Scheduled Task -| Execution, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -19522,7 +17474,7 @@ Administrators may create jobs on systems forcing reboots to perform updates, ma ---- -===Script Execution via WMI=== +===Script execution via wmi=== This search looks for scripts launched via WMI. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -19552,16 +17504,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1047 -| Windows Management Instrumentation -| Execution -|} + ====Kill Chain Phase==== @@ -19585,7 +17528,7 @@ Although unlikely, administrators may use wmi to launch scripts for legitimate p ---- -===Setting Credentials via DSInternals modules=== +===Setting credentials via dsinternals modules=== This detection identifies illegal setting of credentials via DSInternals modules. * '''Product''': UEBA for Security Cloud @@ -19629,6 +17572,7 @@ You must be ingesting Windows Security logs from devices of interest, including * process + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -19648,6 +17592,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Persistence |} + ====Kill Chain Phase==== * Actions on Objectives @@ -19670,7 +17615,7 @@ None identified. ---- -===Setting Credentials via Mimikatz modules=== +===Setting credentials via mimikatz modules=== This detection identifies illegal setting of credentials via Mimikatz modules. * '''Product''': UEBA for Security Cloud @@ -19708,6 +17653,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -19727,6 +17673,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Persistence |} + ====Kill Chain Phase==== * Actions on Objectives @@ -19749,7 +17696,7 @@ None identified. ---- -===Setting Credentials via PowerSploit modules=== +===Setting credentials via powersploit modules=== This detection identifies illegal setting of credentials via PowerSploit modules. * '''Product''': UEBA for Security Cloud @@ -19787,6 +17734,7 @@ You must be ingesting Windows Security logs from devices of interest, including * _time + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -19806,6 +17754,7 @@ You must be ingesting Windows Security logs from devices of interest, including | Persistence |} + ====Kill Chain Phase==== * Actions on Objectives @@ -19828,7 +17777,7 @@ None identified. ---- -===Shim Database File Creation=== +===Shim database file creation=== This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -19858,16 +17807,7 @@ You must be ingesting data that records the filesystem activity from your hosts ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1546.011 -| Application Shimming -| Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -19891,7 +17831,7 @@ Because legitimate shim files are created and used all the time, this event, in ---- -===Shim Database Installation With Suspicious Parameters=== +===Shim database installation with suspicious parameters=== This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -19921,16 +17861,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1546.011 -| Application Shimming -| Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -19954,7 +17885,7 @@ None identified ---- -===Short Lived Windows Accounts=== +===Short lived windows accounts=== This search detects accounts that were created and deleted in a short time period. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -19987,16 +17918,7 @@ This search requires you to have enabled your Group Management Audit Logs in you ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1136.001 -| Local Account -| Persistence -|} + ====Kill Chain Phase==== @@ -20022,7 +17944,7 @@ It is possible that an administrator created and deleted an account in a short t ---- -===Single Letter Process On Endpoint=== +===Single letter process on endpoint=== This search looks for process names that consist only of a single letter. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -20055,16 +17977,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1204.002 -| Malicious File -| Execution -|} + ====Kill Chain Phase==== @@ -20088,7 +18001,7 @@ Single-letter executables are not always malicious. Investigate this activity wi ---- -===Spike in File Writes=== +===Spike in file writes=== The search looks for a sharp increase in the number of files written to a particular host * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -20124,12 +18037,7 @@ In order to implement this search, you must populate the Endpoint file-system da ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -20151,7 +18059,7 @@ It is important to understand that if you happen to install any new applications ---- -===Sunburst Correlation DLL and Network Event=== +===Sunburst correlation dll and network event=== The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -20183,16 +18091,7 @@ This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1203 -| Exploitation for Client Execution -| Execution -|} + ====Kill Chain Phase==== @@ -20216,7 +18115,7 @@ unknown ---- -===Suspicious MSBuild Rename=== +===Suspicious msbuild rename=== The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -20246,6 +18145,7 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -20261,6 +18161,7 @@ To successfully implement this search, you need to be ingesting logs with the pr | Defense Evasion |} + ====Kill Chain Phase==== * Exploitation @@ -20289,7 +18190,7 @@ Although unlikely, some legitimate applications may use a moved copy of msbuild, ---- -===Suspicious MSBuild Spawn=== +===Suspicious msbuild spawn=== The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -20319,16 +18220,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1127.001 -| MSBuild -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -20356,7 +18248,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg ---- -===Suspicious Reg exe Process=== +===Suspicious reg exe process=== This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -20398,16 +18290,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1112 -| Modify Registry -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -20433,7 +18316,7 @@ It's possible for system administrators to write scripts that exhibit this behav ---- -===Suspicious Regsvr32 Register Suspicious Path=== +===Suspicious regsvr32 register suspicious path=== Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -20463,16 +18346,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.010 -| Regsvr32 -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -20506,7 +18380,7 @@ Limited false positives with the query restricted to specified paths. Add more w ---- -===Suspicious Rundll32 Rename=== +===Suspicious rundll32 rename=== The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -20536,6 +18410,7 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -20551,6 +18426,7 @@ To successfully implement this search, you need to be ingesting logs with the pr | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -20579,7 +18455,7 @@ Although unlikely, some legitimate applications may use a moved copy of rundll32 ---- -===Suspicious Rundll32 StartW=== +===Suspicious rundll32 startw=== The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -20611,16 +18487,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.011 -| Rundll32 -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -20654,7 +18521,7 @@ Although unlikely, some legitimate applications may use Start as a function and ---- -===Suspicious Rundll32 dllregisterserver=== +===Suspicious rundll32 dllregisterserver=== The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -20684,16 +18551,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.011 -| Rundll32 -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -20731,7 +18589,7 @@ This is likely to produce false positives and will require some filtering. Tune ---- -===Suspicious Rundll32 no CommandLine Arguments=== +===Suspicious rundll32 no commandline arguments=== The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -20764,16 +18622,7 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.011 -| Rundll32 -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -20810,7 +18659,7 @@ The following analytic identifies a renamed instance of microsoft.workflow.compi * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1127, T1036.003/ T1127, T1036.003] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1127/ T1127], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2021-01-12
@@ -20835,17 +18684,23 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== + ====ATT&CK==== {| ! style="text-align:left;"| ID ! Technique ! Tactic |- -| -| -| +| T1127 +| Trusted Developer Utilities Proxy Execution +| Defense Evasion +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion |} + ====Kill Chain Phase==== * Exploitation @@ -20902,16 +18757,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1127 -| Trusted Developer Utilities Proxy Execution -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -20969,6 +18815,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -20984,6 +18831,7 @@ To successfully implement this search you need to be ingesting information on pr | Defense Evasion |} + ====Kill Chain Phase==== * Exploitation @@ -21040,16 +18888,7 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.005 -| Mshta -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -21107,16 +18946,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.005 -| Mshta -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -21146,7 +18976,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg ---- -===Suspicious wevtutil Usage=== +===Suspicious wevtutil usage=== The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -21178,16 +19008,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1070.001 -| Clear Windows Event Logs -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -21211,7 +19032,7 @@ The wevtutil.exe application is a legitimate Windows event log utility. Administ ---- -===Suspicious writes to windows Recycle Bin=== +===Suspicious writes to windows recycle bin=== This search detects writes to the recycle bin by a process other than explorer.exe. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -21243,16 +19064,7 @@ To successfully implement this search you need to be ingesting information on fi ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1036 -| Masquerading -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -21274,7 +19086,7 @@ Because the Recycle Bin is a hidden folder in modern versions of Windows, it wou ---- -===System Information Discovery Detection=== +===System information discovery detection=== Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -21307,16 +19119,7 @@ To successfully implement this search you need to be ingesting information on pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1082 -| System Information Discovery -| Discovery -|} + ====Kill Chain Phase==== @@ -21342,7 +19145,7 @@ Administrators debugging servers ---- -===System Process Running from Unexpected Location=== +===System process running from unexpected location=== An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml * '''Product''': UEBA for Security Cloud @@ -21405,16 +19208,7 @@ Collect endpoint data such as sysmon or 4688 events. * process_path -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1036 -| Masquerading -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -21436,7 +19230,7 @@ None ---- -===System Processes Run From Unexpected Locations=== +===System processes run from unexpected locations=== This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -21471,16 +19265,7 @@ To successfully implement this search you need to ingest details about process e ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1036.003 -| Rename System Utilities -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -21504,7 +19289,7 @@ None identified ---- -===USN Journal Deletion=== +===Usn journal deletion=== The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -21537,16 +19322,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1070 -| Indicator Removal on Host -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -21570,7 +19346,7 @@ None identified ---- -===Unload Sysmon Filter Driver=== +===Unload sysmon filter driver=== Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -21601,16 +19377,7 @@ You must be ingesting data that records process activity from your hosts to popu ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.001 -| Disable or Modify Tools -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -21634,7 +19401,7 @@ You must be ingesting data that records process activity from your hosts to popu ---- -===Unusually Long Command Line=== +===Unusually long command line=== Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez. * '''Product''': UEBA for Security Cloud @@ -21680,12 +19447,7 @@ You must be ingesting sysmon endpoint data that monitors command lines. * process -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -21707,7 +19469,7 @@ This detection may flag suspiciously long command lines when there is not suffic ---- -===Unusually Long Command Line=== +===Unusually long command line=== Command lines that are extremely long may be indicative of malicious activity on your hosts. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -21748,12 +19510,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -21777,7 +19534,7 @@ Some legitimate applications start with long command lines. ---- -===Unusually Long Command Line - MLTK=== +===Unusually long command line - mltk=== Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -21819,12 +19576,7 @@ You must be ingesting endpoint data that monitors command lines and populates th ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -21846,7 +19598,7 @@ Some legitimate applications use long command lines for installs or updates. You ---- -===WBAdmin Delete System Backups=== +===Wbadmin delete system backups=== This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -21878,16 +19630,7 @@ You must be ingesting endpoint data that tracks process activity, including pare ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1490 -| Inhibit System Recovery -| Impact -|} + ====Kill Chain Phase==== @@ -21919,7 +19662,7 @@ Administrators may modify the boot configuration. ---- -===WMI Permanent Event Subscription=== +===Wmi permanent event subscription=== This search looks for the creation of WMI permanent event subscriptions. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -21952,16 +19695,7 @@ To successfully implement this search, you must be ingesting the Windows WMI act ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1047 -| Windows Management Instrumentation -| Execution -|} + ====Kill Chain Phase==== @@ -21983,7 +19717,7 @@ Although unlikely, administrators may use event subscriptions for legitimate pur ---- -===WMI Permanent Event Subscription - Sysmon=== +===Wmi permanent event subscription - sysmon=== This search looks for the creation of WMI permanent event subscriptions. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22011,16 +19745,7 @@ To successfully implement this search, you must be collecting Sysmon data using ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1546.003 -| Windows Management Instrumentation Event Subscription -| Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -22044,7 +19769,7 @@ Although unlikely, administrators may use event subscriptions for legitimate pur ---- -===WMI Temporary Event Subscription=== +===Wmi temporary event subscription=== This search looks for the creation of WMI temporary event subscriptions. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22076,16 +19801,7 @@ To successfully implement this search, you must be ingesting the Windows WMI act ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1047 -| Windows Management Instrumentation -| Execution -|} + ====Kill Chain Phase==== @@ -22107,7 +19823,7 @@ Some software may create WMI temporary event subscriptions for various purposes. ---- -===Windows AdFind Exe=== +===Windows adfind exe=== This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22137,16 +19853,7 @@ To successfully implement this search, you need to be ingesting logs with the pr ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1018 -| Remote System Discovery -| Discovery -|} + ====Kill Chain Phase==== @@ -22174,7 +19881,7 @@ administrators rarely use adfind, usually not used for legitimate reasons ---- -===Windows Event Log Cleared=== +===Windows event log cleared=== This search looks for Windows events that indicate one of the Windows event logs has been purged. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22205,16 +19912,7 @@ To successfully implement this search, you need to be ingesting Windows event lo ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1070.001 -| Clear Windows Event Logs -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -22240,7 +19938,7 @@ It is possible that these logs may be legitimately cleared by Administrators. ---- -===Windows Security Account Manager Stopped=== +===Windows security account manager stopped=== The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22270,16 +19968,7 @@ You must be ingesting data that records the process-system activity from your ho ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1489 -| Service Stop -| Impact -|} + ====Kill Chain Phase==== @@ -22308,7 +19997,7 @@ SAM is a critical windows service, stopping it would cause major issues on an en ==Network== -===DNS Query Length Outliers - MLTK=== +===Dns query length outliers - mltk=== This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22355,16 +20044,7 @@ Detailed documentation on how to create a new field within Incident Review may b ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1071.004 -| DNS -| Command and Control -|} + ====Kill Chain Phase==== @@ -22386,7 +20066,7 @@ If you are seeing more results than desired, you may consider reducing the value ---- -===DNS Query Length With High Standard Deviation=== +===Dns query length with high standard deviation=== This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22423,16 +20103,7 @@ To successfully implement this search, you will need to ensure that DNS data is ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1048.003 -| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol -| Exfiltration -|} + ====Kill Chain Phase==== @@ -22456,7 +20127,7 @@ It's possible there can be long domain names that are legitimate. ---- -===DNS record changed=== +===Dns record changed=== The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22503,16 +20174,7 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "DNS ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1071.004 -| DNS -| Command and Control -|} + ====Kill Chain Phase==== @@ -22534,7 +20196,7 @@ Legitimate DNS changes can be detected in this search. Investigate, verify and u ---- -===Detect ARP Poisoning=== +===Detect arp poisoning=== By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22564,6 +20226,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne ====Required field==== + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -22583,6 +20246,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | Collection, Credential Access |} + ====Kill Chain Phase==== * Reconnaissance @@ -22607,7 +20271,7 @@ This search might be prone to high false positives if DHCP Snooping or ARP inspe ---- -===Detect IPv6 Network Infrastructure Threats=== +===Detect ipv6 network infrastructure threats=== By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22639,6 +20303,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne ====Required field==== + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -22658,6 +20323,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | Collection, Credential Access |} + ====Kill Chain Phase==== * Reconnaissance @@ -22698,7 +20364,7 @@ None currently known ---- -===Detect Large Outbound ICMP Packets=== +===Detect large outbound icmp packets=== This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22729,16 +20395,7 @@ In order to run this search effectively, we highly recommend that you leverage t ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1095 -| Non-Application Layer Protocol -| Command and Control -|} + ====Kill Chain Phase==== @@ -22760,7 +20417,7 @@ ICMP packets are used in a variety of ways to help troubleshoot networking issue ---- -===Detect Outbound SMB Traffic=== +===Detect outbound smb traffic=== This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22794,16 +20451,7 @@ In order to run this search effectively, we highly recommend that you leverage t ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1071.002 -| File Transfer Protocols -| Command and Control -|} + ====Kill Chain Phase==== @@ -22827,7 +20475,7 @@ It is likely that the outbound Server Message Block (SMB) traffic is legitimate, ---- -===Detect Port Security Violation=== +===Detect port security violation=== By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22857,6 +20505,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne ====Required field==== + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -22876,6 +20525,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | Collection, Credential Access |} + ====Kill Chain Phase==== * Reconnaissance @@ -22902,7 +20552,7 @@ This search might be prone to high false positives if you have malfunctioning de ---- -===Detect Rogue DHCP Server=== +===Detect rogue dhcp server=== By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -22931,6 +20581,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne ====Required field==== + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -22950,6 +20601,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | Collection, Credential Access |} + ====Kill Chain Phase==== * Reconnaissance @@ -22974,7 +20626,7 @@ This search might be prone to high false positives if DHCP Snooping has been inc ---- -===Detect SNICat SNI Exfiltration=== +===Detect snicat sni exfiltration=== This search looks for commands that the SNICat tool uses in the TLS SNI field. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23014,16 +20666,7 @@ You must be ingesting Zeek SSL data into Splunk. Zeek data should also be gettin ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1041 -| Exfiltration Over C2 Channel -| Exfiltration -|} + ====Kill Chain Phase==== @@ -23051,7 +20694,7 @@ Unknown ---- -===Detect Software Download To Network Device=== +===Detect software download to network device=== Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23081,16 +20724,7 @@ This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1542.005 -| TFTP Boot -| Defense Evasion, Persistence -|} + ====Kill Chain Phase==== @@ -23112,7 +20746,7 @@ This search will also report any legitimate attempts of software downloads to ne ---- -===Detect Traffic Mirroring=== +===Detect traffic mirroring=== Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23141,6 +20775,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne ====Required field==== + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -23160,6 +20795,7 @@ This search uses a standard SPL query on logs from Cisco Network devices. The ne | Exfiltration |} + ====Kill Chain Phase==== * Delivery @@ -23182,7 +20818,7 @@ This search will return false positives for any legitimate traffic captures by n ---- -===Detect Unauthorized Assets by MAC address=== +===Detect unauthorized assets by mac address=== By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23216,12 +20852,7 @@ This search uses the Network_Sessions data model shipped with Enterprise Securit ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -23247,7 +20878,7 @@ This search might be prone to high false positives. Please consider this when co ---- -===Detect Windows DNS SIGRed via Splunk Stream=== +===Detect windows dns sigred via splunk stream=== This search detects SIGRed via Splunk Stream. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23281,16 +20912,7 @@ You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1203 -| Exploitation for Client Execution -| Execution -|} + ====Kill Chain Phase==== @@ -23314,7 +20936,7 @@ unknown ---- -===Detect Windows DNS SIGRed via Zeek=== +===Detect windows dns sigred via zeek=== This search detects SIGRed via Zeek DNS and Zeek Conn data. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23348,16 +20970,7 @@ You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1203 -| Exploitation for Client Execution -| Execution -|} + ====Kill Chain Phase==== @@ -23381,7 +20994,7 @@ unknown ---- -===Detect Zerologon via Zeek=== +===Detect zerologon via zeek=== This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23410,16 +21023,7 @@ You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be ge ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1190 -| Exploit Public-Facing Application -| Initial Access -|} + ====Kill Chain Phase==== @@ -23493,16 +21097,7 @@ Detailed documentation on how to create a new field within Incident Review may b ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1189 -| Drive-by Compromise -| Initial Access -|} + ====Kill Chain Phase==== @@ -23528,7 +21123,7 @@ Some users and applications may leverage Dynamic DNS to reach out to some domain ---- -===Excessive DNS Failures=== +===Excessive dns failures=== This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23565,16 +21160,7 @@ To successfully implement this search you must ensure that DNS data is populatin ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1071.004 -| DNS -| Command and Control -|} + ====Kill Chain Phase==== @@ -23630,16 +21216,7 @@ This search requires you to be ingesting your network traffic and populating the ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1114.002 -| Remote Email Collection -| Collection -|} + ====Kill Chain Phase==== @@ -23661,7 +21238,7 @@ The false-positive rate will vary based on how you set the deviation_threshold a ---- -===Large Volume of DNS ANY Queries=== +===Large volume of dns any queries=== The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23690,16 +21267,7 @@ To successfully implement this search you must ensure that DNS data is populatin ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1498.002 -| Reflection Amplification -| Impact -|} + ====Kill Chain Phase==== @@ -23721,7 +21289,7 @@ Legitimate ANY requests may trigger this search, however it is unusual to see a ---- -===Prohibited Network Traffic Allowed=== +===Prohibited network traffic allowed=== This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23757,16 +21325,7 @@ In order to properly run this search, Splunk needs to ingest data from firewalls ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1048 -| Exfiltration Over Alternative Protocol -| Exfiltration -|} + ====Kill Chain Phase==== @@ -23790,7 +21349,7 @@ None identified ---- -===Protocol or Port Mismatch=== +===Protocol or port mismatch=== This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23822,16 +21381,7 @@ Running this search properly requires a technology that can inspect network traf ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1048.003 -| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol -| Exfiltration -|} + ====Kill Chain Phase==== @@ -23883,12 +21433,7 @@ This search requires you to be ingesting your network traffic, and populating th ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -23912,7 +21457,7 @@ Some networks may use kerberized FTP or telnet servers, however, this is rare. ---- -===Remote Desktop Network Bruteforce=== +===Remote desktop network bruteforce=== This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -23945,16 +21490,7 @@ You must ensure that your network traffic data is populating the Network_Traffic ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1021.001 -| Remote Desktop Protocol -| Lateral Movement -|} + ====Kill Chain Phase==== @@ -23978,7 +21514,7 @@ RDP gateways may have unusually high amounts of traffic from all other hosts' RD ---- -===Remote Desktop Network Traffic=== +===Remote desktop network traffic=== This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24014,16 +21550,7 @@ To successfully implement this search you need to identify systems that commonly ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1021.001 -| Remote Desktop Protocol -| Lateral Movement -|} + ====Kill Chain Phase==== @@ -24045,7 +21572,7 @@ Remote Desktop may be used legitimately by users on the network. ---- -===SMB Traffic Spike=== +===Smb traffic spike=== This search looks for spikes in the number of Server Message Block (SMB) traffic connections. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24084,16 +21611,7 @@ This search requires you to be ingesting your network traffic logs and populatin ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1021.002 -| SMB/Windows Admin Shares -| Lateral Movement -|} + ====Kill Chain Phase==== @@ -24115,7 +21633,7 @@ A file server may experience high-demand loads that could cause this analytic to ---- -===SMB Traffic Spike - MLTK=== +===Smb traffic spike - mltk=== This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24159,16 +21677,7 @@ Detailed documentation on how to create a new field within Incident Review is fo ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1021.002 -| SMB/Windows Admin Shares -| Lateral Movement -|} + ====Kill Chain Phase==== @@ -24190,7 +21699,7 @@ If you are seeing more results than desired, you may consider reducing the value ---- -===TOR Traffic=== +===Tor traffic=== This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24226,16 +21735,7 @@ In order to properly run this search, Splunk needs to ingest data from firewalls ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1071.001 -| Web Protocols -| Command and Control -|} + ====Kill Chain Phase==== @@ -24257,7 +21757,7 @@ None at this time ---- -===Unusually Long Content-Type Length=== +===Unusually long content-type length=== This search looks for unusually long strings in the Content-Type http header that the client sends the server. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24286,12 +21786,7 @@ This particular search leverages data extracted from Stream:HTTP. You must confi ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -24318,7 +21813,7 @@ Very few legitimate Content-Type fields will have a length greater than 100 char ==Web== -===Detect F5 TMUI RCE CVE-2020-5902=== +===Detect f5 tmui rce cve-2020-5902=== This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24346,16 +21841,7 @@ To consistently detect exploit attempts on F5 devices using the vulnerabilities ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1190 -| Exploit Public-Facing Application -| Initial Access -|} + ====Kill Chain Phase==== @@ -24383,7 +21869,7 @@ unknown ---- -===Detect attackers scanning for vulnerable JBoss servers=== +===Detect attackers scanning for vulnerable jboss servers=== This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24415,16 +21901,7 @@ You must be ingesting data from the web server or network traffic that contains ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1082 -| System Information Discovery -| Discovery -|} + ====Kill Chain Phase==== @@ -24446,7 +21923,7 @@ It's possible for legitimate HTTP requests to be made to URLs containing the sus ---- -===Detect malicious requests to exploit JBoss servers=== +===Detect malicious requests to exploit jboss servers=== This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24480,12 +21957,7 @@ You must ingest data from the web server or capture network data that contains w ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -24507,7 +21979,7 @@ No known false positives for this detection. ---- -===Monitor Web Traffic For Brand Abuse=== +===Monitor web traffic for brand abuse=== This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24537,12 +22009,7 @@ You need to ingest data from your web traffic. This can be accomplished by index ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -24564,7 +22031,7 @@ None at this time ---- -===SQL Injection with Long URLs=== +===Sql injection with long urls=== This search looks for long URLs that have several SQL commands visible within them. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24594,16 +22061,7 @@ To successfully implement this search, you need to be monitoring network communi ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1190 -| Exploit Public-Facing Application -| Initial Access -|} + ====Kill Chain Phase==== @@ -24625,7 +22083,7 @@ It's possible that legitimate traffic will have long URLs or long user agent str ---- -===Supernova Webshell=== +===Supernova webshell=== This search aims to detect the Supernova webshell used in the SUNBURST attack. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24652,16 +22110,7 @@ To successfully implement this search, you need to be monitoring web traffic to ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1505.003 -| Web Shell -| Persistence -|} + ====Kill Chain Phase==== @@ -24687,7 +22136,7 @@ There might be false positives associted with this detection since items like ar ---- -===Web Fraud - Account Harvesting=== +===Web fraud - account harvesting=== This search is used to identify the creation of multiple user accounts using the same email domain name. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24720,16 +22169,7 @@ We start with a dataset that provides visibility into the email address used for ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1136 -| Create Account -| Persistence -|} + ====Kill Chain Phase==== @@ -24755,7 +22195,7 @@ As is common with many fraud-related searches, we are usually looking to attribu ---- -===Web Fraud - Anomalous User Clickspeed=== +===Web fraud - anomalous user clickspeed=== This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24786,16 +22226,7 @@ Start with a dataset that allows you to see clickstream data for each user click ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} + ====Kill Chain Phase==== @@ -24825,7 +22256,7 @@ As is common with many fraud-related searches, we are usually looking to attribu ---- -===Web Fraud - Password Sharing Across Accounts=== +===Web fraud - password sharing across accounts=== This search is used to identify user accounts that share a common password. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -24857,12 +22288,7 @@ We need to start with a dataset that allows us to see the values of usernames an ====Required field==== -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -24893,4 +22319,11 @@ As is common with many fraud-related searches, we are usually looking to attribu +''#############'' +''# Automatically generated by doc_gen.py in https://github.com/splunk/security_content'' +''# On Date: UTC'' +''# Author: Splunk Security Research'' +''# Contact: research@splunk.com'' +''#############'' + [[Category:V:ESSOC:drafts]] \ No newline at end of file diff --git a/docs/stories.md b/docs/stories.md index 81fc5be9c0..20e5d8db0e 100644 --- a/docs/stories.md +++ b/docs/stories.md @@ -1797,7 +1797,7 @@ Monitor and detect behaviors used by attackers who leverage trusted developer ut - **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud - **Datamodel**: Endpoint -- **ATT&CK**: [T1127](https://attack.mitre.org/techniques/T1127/), [T1127, T1036.003](https://attack.mitre.org/techniques/T1127, T1036.003/) +- **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036.003/), [T1127](https://attack.mitre.org/techniques/T1127/) - **Last Updated**: 2021-01-12
@@ -1814,8 +1814,8 @@ Monitor and detect behaviors used by attackers who leverage trusted developer ut | ID | Technique | Tactic | | ----------- | ----------- |--------------| -| | | | | T1127 | Trusted Developer Utilities Proxy Execution | Defense Evasion | +| T1036.003 | Rename System Utilities | Defense Evasion | #### Kill Chain Phase diff --git a/docs/stories.wiki b/docs/stories.wiki index a561af72b9..9130fdf780 100644 --- a/docs/stories.wiki +++ b/docs/stories.wiki @@ -6,7 +6,7 @@ All the Analytic Stories shipped to different Splunk products. Below is a breakd ==Abuse== -===Brand Monitoring=== +===Brand monitoring=== Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -19,19 +19,14 @@ Detect and investigate activity that may indicate that an adversary is using fau ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Monitor_DNS_For_Brand_Abuse|Monitor DNS For Brand Abuse]] +* [[Documentation:ESSOC:detections:Detections#Monitor_dns_for_brand_abuse|Monitor DNS For Brand Abuse]] -* [[Documentation:ESSOC:detections:Detections#Monitor_Email_For_Brand_Abuse|Monitor Email For Brand Abuse]] +* [[Documentation:ESSOC:detections:Detections#Monitor_email_for_brand_abuse|Monitor Email For Brand Abuse]] + +* [[Documentation:ESSOC:detections:Detections#Monitor_web_traffic_for_brand_abuse|Monitor Web Traffic For Brand Abuse]] -* [[Documentation:ESSOC:detections:Detections#Monitor_Web_Traffic_For_Brand_Abuse|Monitor Web Traffic For Brand Abuse]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -55,7 +50,7 @@ Detect and investigate activity that may indicate that an adversary is using fau ---- -===DNS Amplification Attacks=== +===Dns amplification attacks=== DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -68,19 +63,10 @@ DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it respond ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Large_Volume_of_DNS_ANY_Queries|Large Volume of DNS ANY Queries]] +* [[Documentation:ESSOC:detections:Detections#Large_volume_of_dns_any_queries|Large Volume of DNS ANY Queries]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1498.002 -| Reflection Amplification -| Impact -|} ====Kill Chain Phase==== @@ -100,12 +86,12 @@ DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it respond ---- -===Data Protection=== +===Data protection=== Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Change_Analysis, Network_Resolution -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1189/ T1189], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1048/ T1048] * '''Last Updated''': 2017-09-14
@@ -113,11 +99,12 @@ Fortify your data-protection arsenal--while continuing to ensure data confidenti ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_USB_device_insertion|Detect USB device insertion]] +* [[Documentation:ESSOC:detections:Detections#Detect_usb_device_insertion|Detect USB device insertion]] * [[Documentation:ESSOC:detections:Detections#Detect_hosts_connecting_to_dynamic_domain_providers|Detect hosts connecting to dynamic domain providers]] -* [[Documentation:ESSOC:detections:Detections#Detection_of_DNS_Tunnels|Detection of DNS Tunnels]] +* [[Documentation:ESSOC:detections:Detections#Detection_of_dns_tunnels|Detection of DNS Tunnels]] + ====ATT&CK==== @@ -143,6 +130,7 @@ Fortify your data-protection arsenal--while continuing to ensure data confidenti | Exfiltration |} + ====Kill Chain Phase==== * Actions on Objectives @@ -167,12 +155,12 @@ Fortify your data-protection arsenal--while continuing to ensure data confidenti ---- -===Host Redirection=== +===Host redirection=== Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Network_Resolution -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.004/ T1071.004] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.004/ T1071.004], [https://attack.mitre.org/techniques/T1095/ T1095], [https://attack.mitre.org/techniques/T1189/ T1189], [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1071.001/ T1071.001] * '''Last Updated''': 2017-09-14
@@ -180,13 +168,14 @@ Detect evidence of tactics used to redirect traffic from a host to a destination ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Clients_Connecting_to_Multiple_DNS_Servers|Clients Connecting to Multiple DNS Servers]] +* [[Documentation:ESSOC:detections:Detections#Clients_connecting_to_multiple_dns_servers|Clients Connecting to Multiple DNS Servers]] -* [[Documentation:ESSOC:detections:Detections#DNS_Query_Requests_Resolved_by_Unauthorized_DNS_Servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] +* [[Documentation:ESSOC:detections:Detections#Dns_query_requests_resolved_by_unauthorized_dns_servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] * [[Documentation:ESSOC:detections:Detections#Windows_hosts_file_modification|Windows hosts file modification]] + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -218,6 +207,7 @@ Detect evidence of tactics used to redirect traffic from a host to a destination | Command and Control |} + ====Kill Chain Phase==== * Command and Control @@ -234,7 +224,7 @@ Detect evidence of tactics used to redirect traffic from a host to a destination ---- -===Netsh Abuse=== +===Netsh abuse=== Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -252,16 +242,7 @@ Detect activities and various techniques associated with the abuse of `netsh.exe * [[Documentation:ESSOC:detections:Detections#Processes_launching_netsh|Processes launching netsh]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.004 -| Disable or Modify System Firewall -| Defense Evasion -|} + ====Kill Chain Phase==== @@ -283,12 +264,12 @@ Detect activities and various techniques associated with the abuse of `netsh.exe ---- -===Web Fraud Detection=== +===Web fraud detection=== Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1136/ T1136] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136/ T1136], [https://attack.mitre.org/techniques/T1078/ T1078] * '''Last Updated''': 2018-10-08
@@ -296,11 +277,12 @@ Monitor your environment for activity consistent with common attack techniques b ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Web_Fraud_-_Account_Harvesting|Web Fraud - Account Harvesting]] +* [[Documentation:ESSOC:detections:Detections#Web_fraud_-_account_harvesting|Web Fraud - Account Harvesting]] -* [[Documentation:ESSOC:detections:Detections#Web_Fraud_-_Anomalous_User_Clickspeed|Web Fraud - Anomalous User Clickspeed]] +* [[Documentation:ESSOC:detections:Detections#Web_fraud_-_anomalous_user_clickspeed|Web Fraud - Anomalous User Clickspeed]] + +* [[Documentation:ESSOC:detections:Detections#Web_fraud_-_password_sharing_across_accounts|Web Fraud - Password Sharing Across Accounts]] -* [[Documentation:ESSOC:detections:Detections#Web_Fraud_-_Password_Sharing_Across_Accounts|Web Fraud - Password Sharing Across Accounts]] ====ATT&CK==== @@ -318,6 +300,7 @@ Monitor your environment for activity consistent with common attack techniques b | Defense Evasion, Initial Access, Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -341,7 +324,7 @@ Monitor your environment for activity consistent with common attack techniques b ==Adversary Tactics== -===Baron Samedit CVE-2021-3156=== +===Baron samedit cve-2021-3156=== Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Research Team, this vulnerability has been found to affect sudo across multiple Linux distributions (Ubuntu 20.04 and prior, Debian 10 and prior, Fedora 33 and prior). As this vulnerability was committed to code in July 2011, there will be many distributions affected. Successful exploitation of this vulnerability allows any unprivileged user to gain root privileges on the vulnerable host. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -354,23 +337,14 @@ Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Researc ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Baron_Samedit_CVE-2021-3156|Detect Baron Samedit CVE-2021-3156]] +* [[Documentation:ESSOC:detections:Detections#Detect_baron_samedit_cve-2021-3156|Detect Baron Samedit CVE-2021-3156]] -* [[Documentation:ESSOC:detections:Detections#Detect_Baron_Samedit_CVE-2021-3156_Segfault|Detect Baron Samedit CVE-2021-3156 Segfault]] +* [[Documentation:ESSOC:detections:Detections#Detect_baron_samedit_cve-2021-3156_segfault|Detect Baron Samedit CVE-2021-3156 Segfault]] + +* [[Documentation:ESSOC:detections:Detections#Detect_baron_samedit_cve-2021-3156_via_osquery|Detect Baron Samedit CVE-2021-3156 via OSQuery]] -* [[Documentation:ESSOC:detections:Detections#Detect_Baron_Samedit_CVE-2021-3156_via_OSQuery|Detect Baron Samedit CVE-2021-3156 via OSQuery]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1068 -| Exploitation for Privilege Escalation -| Privilege Escalation -|} ====Kill Chain Phase==== @@ -388,7 +362,7 @@ Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Researc ---- -===Cobalt Strike=== +===Cobalt strike=== Cobalt Strike is threat emulation software. Red teams and penetration testers use Cobalt Strike to demonstrate the risk of a breach and evaluate mature security programs. Most recently, Cobalt Strike has become the choice tool by threat groups due to its ease of use and extensibility. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -401,21 +375,12 @@ Cobalt Strike is threat emulation software. Red teams and penetration testers us ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_StartW|Suspicious Rundll32 StartW]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_rundll32_startw|Suspicious Rundll32 StartW]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_rundll32_no_commandline_arguments|Suspicious Rundll32 no CommandLine Arguments]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_no_CommandLine_Arguments|Suspicious Rundll32 no CommandLine Arguments]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.011 -| Rundll32 -| Defense Evasion -|} ====Kill Chain Phase==== @@ -441,12 +406,12 @@ Cobalt Strike is threat emulation software. Red teams and penetration testers us ---- -===Collection and Staging=== +===Collection and staging=== Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint, Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036/ T1036], [https://attack.mitre.org/techniques/T1114.001/ T1114.001], [https://attack.mitre.org/techniques/T1114.002/ T1114.002] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1114.001/ T1114.001], [https://attack.mitre.org/techniques/T1114.002/ T1114.002], [https://attack.mitre.org/techniques/T1036/ T1036] * '''Last Updated''': 2020-02-03
@@ -454,15 +419,16 @@ Monitor for and investigate activities--such as suspicious writes to the Windows ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Email_files_written_outside_of_the_Outlook_directory|Email files written outside of the Outlook directory]] +* [[Documentation:ESSOC:detections:Detections#Email_files_written_outside_of_the_outlook_directory|Email files written outside of the Outlook directory]] * [[Documentation:ESSOC:detections:Detections#Email_servers_sending_high_volume_traffic_to_hosts|Email servers sending high volume traffic to hosts]] * [[Documentation:ESSOC:detections:Detections#Hosts_receiving_high_volume_of_network_traffic_from_email_server|Hosts receiving high volume of network traffic from email server]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_writes_to_System_Volume_Information|Suspicious writes to System Volume Information]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_writes_to_system_volume_information|Suspicious writes to System Volume Information]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_writes_to_windows_recycle_bin|Suspicious writes to windows Recycle Bin]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_writes_to_windows_Recycle_Bin|Suspicious writes to windows Recycle Bin]] ====ATT&CK==== @@ -484,6 +450,7 @@ Monitor for and investigate activities--such as suspicious writes to the Windows | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -502,12 +469,12 @@ Monitor for and investigate activities--such as suspicious writes to the Windows ---- -===Command and Control=== +===Command and control=== Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Network_Resolution, Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1071.004/ T1071.004], [https://attack.mitre.org/techniques/T1095/ T1095], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.004/ T1071.004], [https://attack.mitre.org/techniques/T1095/ T1095], [https://attack.mitre.org/techniques/T1189/ T1189], [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1071.001/ T1071.001] * '''Last Updated''': 2018-06-01
@@ -515,31 +482,32 @@ Detect and investigate tactics, techniques, and procedures leveraged by attacker ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Clients_Connecting_to_Multiple_DNS_Servers|Clients Connecting to Multiple DNS Servers]] +* [[Documentation:ESSOC:detections:Detections#Clients_connecting_to_multiple_dns_servers|Clients Connecting to Multiple DNS Servers]] -* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_Outliers_-_MLTK|DNS Query Length Outliers - MLTK]] +* [[Documentation:ESSOC:detections:Detections#Dns_query_length_outliers_-_mltk|DNS Query Length Outliers - MLTK]] -* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_With_High_Standard_Deviation|DNS Query Length With High Standard Deviation]] +* [[Documentation:ESSOC:detections:Detections#Dns_query_length_with_high_standard_deviation|DNS Query Length With High Standard Deviation]] -* [[Documentation:ESSOC:detections:Detections#DNS_Query_Requests_Resolved_by_Unauthorized_DNS_Servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] +* [[Documentation:ESSOC:detections:Detections#Dns_query_requests_resolved_by_unauthorized_dns_servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] -* [[Documentation:ESSOC:detections:Detections#Detect_Large_Outbound_ICMP_Packets|Detect Large Outbound ICMP Packets]] +* [[Documentation:ESSOC:detections:Detections#Detect_large_outbound_icmp_packets|Detect Large Outbound ICMP Packets]] -* [[Documentation:ESSOC:detections:Detections#Detect_Long_DNS_TXT_Record_Response|Detect Long DNS TXT Record Response]] +* [[Documentation:ESSOC:detections:Detections#Detect_long_dns_txt_record_response|Detect Long DNS TXT Record Response]] -* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_blocked_Outbound_Traffic_from_your_AWS|Detect Spike in blocked Outbound Traffic from your AWS]] +* [[Documentation:ESSOC:detections:Detections#Detect_spike_in_blocked_outbound_traffic_from_your_aws|Detect Spike in blocked Outbound Traffic from your AWS]] * [[Documentation:ESSOC:detections:Detections#Detect_hosts_connecting_to_dynamic_domain_providers|Detect hosts connecting to dynamic domain providers]] -* [[Documentation:ESSOC:detections:Detections#Detection_of_DNS_Tunnels|Detection of DNS Tunnels]] +* [[Documentation:ESSOC:detections:Detections#Detection_of_dns_tunnels|Detection of DNS Tunnels]] -* [[Documentation:ESSOC:detections:Detections#Excessive_DNS_Failures|Excessive DNS Failures]] +* [[Documentation:ESSOC:detections:Detections#Excessive_dns_failures|Excessive DNS Failures]] -* [[Documentation:ESSOC:detections:Detections#Prohibited_Network_Traffic_Allowed|Prohibited Network Traffic Allowed]] +* [[Documentation:ESSOC:detections:Detections#Prohibited_network_traffic_allowed|Prohibited Network Traffic Allowed]] -* [[Documentation:ESSOC:detections:Detections#Protocol_or_Port_Mismatch|Protocol or Port Mismatch]] +* [[Documentation:ESSOC:detections:Detections#Protocol_or_port_mismatch|Protocol or Port Mismatch]] + +* [[Documentation:ESSOC:detections:Detections#Tor_traffic|TOR Traffic]] -* [[Documentation:ESSOC:detections:Detections#TOR_Traffic|TOR Traffic]] ====ATT&CK==== @@ -573,6 +541,7 @@ Detect and investigate tactics, techniques, and procedures leveraged by attacker | Command and Control |} + ====Kill Chain Phase==== * Actions on Objectives @@ -595,7 +564,7 @@ Detect and investigate tactics, techniques, and procedures leveraged by attacker ---- -===Common Phishing Frameworks=== +===Common phishing frameworks=== Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -608,19 +577,10 @@ Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_DNS_requests_to_Phishing_Sites_leveraging_EvilGinx2|Detect DNS requests to Phishing Sites leveraging EvilGinx2]] +* [[Documentation:ESSOC:detections:Detections#Detect_dns_requests_to_phishing_sites_leveraging_evilginx2|Detect DNS requests to Phishing Sites leveraging EvilGinx2]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1566.003 -| Spearphishing via Service -| Initial Access -|} ====Kill Chain Phase==== @@ -644,12 +604,12 @@ Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. ---- -===Credential Dumping=== +===Credential dumping=== Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003], [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1003.002/ T1003.002], [https://attack.mitre.org/techniques/T1003.003/ T1003.003], [https://attack.mitre.org/techniques/T1059.001/ T1059.001] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1003.002/ T1003.002], [https://attack.mitre.org/techniques/T1003/ T1003], [https://attack.mitre.org/techniques/T1003.003/ T1003.003] * '''Last Updated''': 2020-02-04
@@ -657,39 +617,40 @@ Uncover activity consistent with credential dumping, a technique wherein attacke ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Access_LSASS_Memory_for_Dump_Creation|Access LSASS Memory for Dump Creation]] +* [[Documentation:ESSOC:detections:Detections#Access_lsass_memory_for_dump_creation|Access LSASS Memory for Dump Creation]] -* [[Documentation:ESSOC:detections:Detections#Attempt_To_Set_Default_PowerShell_Execution_Policy_To_Unrestricted_or_Bypass|Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass]] +* [[Documentation:ESSOC:detections:Detections#Attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass|Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass]] -* [[Documentation:ESSOC:detections:Detections#Attempted_Credential_Dump_From_Registry_via_Reg_exe|Attempted Credential Dump From Registry via Reg exe]] +* [[Documentation:ESSOC:detections:Detections#Attempted_credential_dump_from_registry_via_reg_exe|Attempted Credential Dump From Registry via Reg exe]] -* [[Documentation:ESSOC:detections:Detections#Create_Remote_Thread_into_LSASS|Create Remote Thread into LSASS]] +* [[Documentation:ESSOC:detections:Detections#Create_remote_thread_into_lsass|Create Remote Thread into LSASS]] -* [[Documentation:ESSOC:detections:Detections#Creation_of_Shadow_Copy|Creation of Shadow Copy]] +* [[Documentation:ESSOC:detections:Detections#Creation_of_shadow_copy|Creation of Shadow Copy]] -* [[Documentation:ESSOC:detections:Detections#Creation_of_Shadow_Copy_with_wmic_and_powershell|Creation of Shadow Copy with wmic and powershell]] +* [[Documentation:ESSOC:detections:Detections#Creation_of_shadow_copy_with_wmic_and_powershell|Creation of Shadow Copy with wmic and powershell]] -* [[Documentation:ESSOC:detections:Detections#Creation_of_lsass_Dump_with_Taskmgr|Creation of lsass Dump with Taskmgr]] +* [[Documentation:ESSOC:detections:Detections#Creation_of_lsass_dump_with_taskmgr|Creation of lsass Dump with Taskmgr]] -* [[Documentation:ESSOC:detections:Detections#Credential_Dumping_via_Copy_Command_from_Shadow_Copy|Credential Dumping via Copy Command from Shadow Copy]] +* [[Documentation:ESSOC:detections:Detections#Credential_dumping_via_copy_command_from_shadow_copy|Credential Dumping via Copy Command from Shadow Copy]] -* [[Documentation:ESSOC:detections:Detections#Credential_Dumping_via_Symlink_to_Shadow_Copy|Credential Dumping via Symlink to Shadow Copy]] +* [[Documentation:ESSOC:detections:Detections#Credential_dumping_via_symlink_to_shadow_copy|Credential Dumping via Symlink to Shadow Copy]] -* [[Documentation:ESSOC:detections:Detections#Detect_Credential_Dumping_through_LSASS_access|Detect Credential Dumping through LSASS access]] +* [[Documentation:ESSOC:detections:Detections#Detect_credential_dumping_through_lsass_access|Detect Credential Dumping through LSASS access]] -* [[Documentation:ESSOC:detections:Detections#Detect_Dump_LSASS_Memory_using_comsvcs|Detect Dump LSASS Memory using comsvcs]] +* [[Documentation:ESSOC:detections:Detections#Detect_dump_lsass_memory_using_comsvcs|Detect Dump LSASS Memory using comsvcs]] -* [[Documentation:ESSOC:detections:Detections#Detect_Mimikatz_Using_Loaded_Images|Detect Mimikatz Using Loaded Images]] +* [[Documentation:ESSOC:detections:Detections#Detect_mimikatz_using_loaded_images|Detect Mimikatz Using Loaded Images]] -* [[Documentation:ESSOC:detections:Detections#Dump_LSASS_via_comsvcs_DLL|Dump LSASS via comsvcs DLL]] +* [[Documentation:ESSOC:detections:Detections#Dump_lsass_via_comsvcs_dll|Dump LSASS via comsvcs DLL]] -* [[Documentation:ESSOC:detections:Detections#Dump_LSASS_via_procdump|Dump LSASS via procdump]] +* [[Documentation:ESSOC:detections:Detections#Dump_lsass_via_procdump|Dump LSASS via procdump]] -* [[Documentation:ESSOC:detections:Detections#Dump_LSASS_via_procdump_Rename|Dump LSASS via procdump Rename]] +* [[Documentation:ESSOC:detections:Detections#Dump_lsass_via_procdump_rename|Dump LSASS via procdump Rename]] * [[Documentation:ESSOC:detections:Detections#Ntdsutil_export_ntds|Ntdsutil export ntds]] -* [[Documentation:ESSOC:detections:Detections#Unsigned_Image_Loaded_by_LSASS|Unsigned Image Loaded by LSASS]] +* [[Documentation:ESSOC:detections:Detections#Unsigned_image_loaded_by_lsass|Unsigned Image Loaded by LSASS]] + ====ATT&CK==== @@ -719,6 +680,7 @@ Uncover activity consistent with credential dumping, a technique wherein attacke | Credential Access |} + ====Kill Chain Phase==== * Actions on Objectives @@ -739,12 +701,12 @@ Uncover activity consistent with credential dumping, a technique wherein attacke ---- -===DNS Hijacking=== +===Dns hijacking=== Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Network_Resolution -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.004/ T1071.004], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.004/ T1071.004], [https://attack.mitre.org/techniques/T1095/ T1095], [https://attack.mitre.org/techniques/T1189/ T1189], [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1071.001/ T1071.001] * '''Last Updated''': 2020-02-04
@@ -752,15 +714,16 @@ Secure your environment against DNS hijacks with searches that help you detect a ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Clients_Connecting_to_Multiple_DNS_Servers|Clients Connecting to Multiple DNS Servers]] +* [[Documentation:ESSOC:detections:Detections#Clients_connecting_to_multiple_dns_servers|Clients Connecting to Multiple DNS Servers]] -* [[Documentation:ESSOC:detections:Detections#DNS_Query_Requests_Resolved_by_Unauthorized_DNS_Servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] +* [[Documentation:ESSOC:detections:Detections#Dns_query_requests_resolved_by_unauthorized_dns_servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] -* [[Documentation:ESSOC:detections:Detections#DNS_record_changed|DNS record changed]] +* [[Documentation:ESSOC:detections:Detections#Dns_record_changed|DNS record changed]] * [[Documentation:ESSOC:detections:Detections#Detect_hosts_connecting_to_dynamic_domain_providers|Detect hosts connecting to dynamic domain providers]] + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -792,6 +755,7 @@ Secure your environment against DNS hijacks with searches that help you detect a | Command and Control |} + ====Kill Chain Phase==== * Actions on Objectives @@ -816,7 +780,7 @@ Secure your environment against DNS hijacks with searches that help you detect a ---- -===Data Exfiltration=== +===Data exfiltration=== The stealing of data by an adversary. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -829,19 +793,10 @@ The stealing of data by an adversary. ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_SNICat_SNI_Exfiltration|Detect SNICat SNI Exfiltration]] +* [[Documentation:ESSOC:detections:Detections#Detect_snicat_sni_exfiltration|Detect SNICat SNI Exfiltration]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1041 -| Exfiltration Over C2 Channel -| Exfiltration -|} ====Kill Chain Phase==== @@ -859,12 +814,12 @@ The stealing of data by an adversary. ---- -===Detect Zerologon Attack=== +===Detect zerologon attack=== Uncover activity related to the execution of Zerologon CVE-2020-11472, a technique wherein attackers target a Microsoft Windows Domain Controller to reset its computer account password. The result from this attack is attackers can now provide themselves high privileges and take over Domain Controller. The included searches in this Analytic Story are designed to identify attempts to reset Domain Controller Computer Account via exploit code remotely or via the use of tool Mimikatz as payload carrier. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1190/ T1190], [https://attack.mitre.org/techniques/T1210/ T1210] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1210/ T1210], [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1190/ T1190] * '''Last Updated''': 2020-09-18
@@ -872,13 +827,14 @@ Uncover activity related to the execution of Zerologon CVE-2020-11472, a techniq ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Computer_Changed_with_Anonymous_Account|Detect Computer Changed with Anonymous Account]] +* [[Documentation:ESSOC:detections:Detections#Detect_computer_changed_with_anonymous_account|Detect Computer Changed with Anonymous Account]] -* [[Documentation:ESSOC:detections:Detections#Detect_Credential_Dumping_through_LSASS_access|Detect Credential Dumping through LSASS access]] +* [[Documentation:ESSOC:detections:Detections#Detect_credential_dumping_through_lsass_access|Detect Credential Dumping through LSASS access]] -* [[Documentation:ESSOC:detections:Detections#Detect_Mimikatz_Using_Loaded_Images|Detect Mimikatz Using Loaded Images]] +* [[Documentation:ESSOC:detections:Detections#Detect_mimikatz_using_loaded_images|Detect Mimikatz Using Loaded Images]] + +* [[Documentation:ESSOC:detections:Detections#Detect_zerologon_via_zeek|Detect Zerologon via Zeek]] -* [[Documentation:ESSOC:detections:Detections#Detect_Zerologon_via_Zeek|Detect Zerologon via Zeek]] ====ATT&CK==== @@ -900,6 +856,7 @@ Uncover activity related to the execution of Zerologon CVE-2020-11472, a techniq | Initial Access |} + ====Kill Chain Phase==== * Actions on Objectives @@ -924,12 +881,12 @@ Uncover activity related to the execution of Zerologon CVE-2020-11472, a techniq ---- -===Disabling Security Tools=== +===Disabling security tools=== Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1112/ T1112], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1553.004/ T1553.004], [https://attack.mitre.org/techniques/T1562.001/ T1562.001], [https://attack.mitre.org/techniques/T1562.004/ T1562.004] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1553.004/ T1553.004], [https://attack.mitre.org/techniques/T1562.001/ T1562.001], [https://attack.mitre.org/techniques/T1562.004/ T1562.004], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1112/ T1112] * '''Last Updated''': 2020-02-04
@@ -937,17 +894,18 @@ Looks for activities and techniques associated with the disabling of security to ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Attempt_To_Add_Certificate_To_Untrusted_Store|Attempt To Add Certificate To Untrusted Store]] +* [[Documentation:ESSOC:detections:Detections#Attempt_to_add_certificate_to_untrusted_store|Attempt To Add Certificate To Untrusted Store]] -* [[Documentation:ESSOC:detections:Detections#Attempt_To_Stop_Security_Service|Attempt To Stop Security Service]] +* [[Documentation:ESSOC:detections:Detections#Attempt_to_stop_security_service|Attempt To Stop Security Service]] * [[Documentation:ESSOC:detections:Detections#Processes_launching_netsh|Processes launching netsh]] -* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] +* [[Documentation:ESSOC:detections:Detections#Sc_exe_manipulating_windows_services|Sc exe Manipulating Windows Services]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Reg_exe_Process|Suspicious Reg exe Process]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_reg_exe_process|Suspicious Reg exe Process]] + +* [[Documentation:ESSOC:detections:Detections#Unload_sysmon_filter_driver|Unload Sysmon Filter Driver]] -* [[Documentation:ESSOC:detections:Detections#Unload_Sysmon_Filter_Driver|Unload Sysmon Filter Driver]] ====ATT&CK==== @@ -977,6 +935,7 @@ Looks for activities and techniques associated with the disabling of security to | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -999,7 +958,7 @@ Looks for activities and techniques associated with the disabling of security to ---- -===F5 TMUI RCE CVE-2020-5902=== +===F5 tmui rce cve-2020-5902=== Uncover activity consistent with CVE-2020-5902. Discovered by Positive Technologies researchers, this vulnerability affects F5 BIG-IP, BIG-IQ. and Traffix SDC devices (vulnerable versions in F5 support link below). This vulnerability allows unauthenticated users, along with authenticated users, who have access to the configuration utility to execute system commands, create/delete files, disable services, and/or execute Java code. This vulnerability can result in full system compromise. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1012,19 +971,10 @@ Uncover activity consistent with CVE-2020-5902. Discovered by Positive Technolog ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_F5_TMUI_RCE_CVE-2020-5902|Detect F5 TMUI RCE CVE-2020-5902]] +* [[Documentation:ESSOC:detections:Detections#Detect_f5_tmui_rce_cve-2020-5902|Detect F5 TMUI RCE CVE-2020-5902]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1190 -| Exploit Public-Facing Application -| Initial Access -|} ====Kill Chain Phase==== @@ -1046,12 +996,12 @@ Uncover activity consistent with CVE-2020-5902. Discovered by Positive Technolog ---- -===Lateral Movement=== +===Lateral movement=== Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint, Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1550.002/ T1550.002], [https://attack.mitre.org/techniques/T1558.003/ T1558.003] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1550.002/ T1550.002], [https://attack.mitre.org/techniques/T1558.003/ T1558.003], [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1053.005/ T1053.005] * '''Last Updated''': 2020-02-04
@@ -1059,17 +1009,18 @@ Detect and investigate tactics, techniques, and procedures around how attackers ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Activity_Related_to_Pass_the_Hash_Attacks|Detect Activity Related to Pass the Hash Attacks]] +* [[Documentation:ESSOC:detections:Detections#Detect_activity_related_to_pass_the_hash_attacks|Detect Activity Related to Pass the Hash Attacks]] -* [[Documentation:ESSOC:detections:Detections#Kerberoasting_spn_request_with_RC4_encryption|Kerberoasting spn request with RC4 encryption]] +* [[Documentation:ESSOC:detections:Detections#Kerberoasting_spn_request_with_rc4_encryption|Kerberoasting spn request with RC4 encryption]] -* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Traffic|Remote Desktop Network Traffic]] +* [[Documentation:ESSOC:detections:Detections#Remote_desktop_network_traffic|Remote Desktop Network Traffic]] -* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Process_Running_On_System|Remote Desktop Process Running On System]] +* [[Documentation:ESSOC:detections:Detections#Remote_desktop_process_running_on_system|Remote Desktop Process Running On System]] * [[Documentation:ESSOC:detections:Detections#Schtasks_scheduling_job_on_remote_system|Schtasks scheduling job on remote system]] + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -1093,6 +1044,7 @@ Detect and investigate tactics, techniques, and procedures around how attackers | Execution, Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -1109,12 +1061,12 @@ Detect and investigate tactics, techniques, and procedures around how attackers ---- -===Malicious PowerShell=== +===Malicious powershell=== Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1027/ T1027], [https://attack.mitre.org/techniques/T1059.001/ T1059.001] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1027/ T1027] * '''Last Updated''': 2017-08-23
@@ -1122,15 +1074,16 @@ Attackers are finding stealthy ways "live off the land," leveraging utilities an ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Attempt_To_Set_Default_PowerShell_Execution_Policy_To_Unrestricted_or_Bypass|Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass]] +* [[Documentation:ESSOC:detections:Detections#Attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass|Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass]] -* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Connect_To_Internet_With_Hidden_Window|Malicious PowerShell Process - Connect To Internet With Hidden Window]] +* [[Documentation:ESSOC:detections:Detections#Malicious_powershell_process_-_connect_to_internet_with_hidden_window|Malicious PowerShell Process - Connect To Internet With Hidden Window]] -* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Encoded_Command|Malicious PowerShell Process - Encoded Command]] +* [[Documentation:ESSOC:detections:Detections#Malicious_powershell_process_-_encoded_command|Malicious PowerShell Process - Encoded Command]] -* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Multiple_Suspicious_Command-Line_Arguments|Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments]] +* [[Documentation:ESSOC:detections:Detections#Malicious_powershell_process_-_multiple_suspicious_command-line_arguments|Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments]] + +* [[Documentation:ESSOC:detections:Detections#Malicious_powershell_process_with_obfuscation_techniques|Malicious PowerShell Process With Obfuscation Techniques]] -* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_With_Obfuscation_Techniques|Malicious PowerShell Process With Obfuscation Techniques]] ====ATT&CK==== @@ -1148,6 +1101,7 @@ Attackers are finding stealthy ways "live off the land," leveraging utilities an | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -1170,7 +1124,7 @@ Attackers are finding stealthy ways "live off the land," leveraging utilities an ---- -===Phishing Payloads=== +===Phishing payloads=== Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1183,9 +1137,10 @@ Detect signs of malicious payloads that may indicate that your environment has b ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Oulook_exe_writing_a__zip_file|Detect Oulook exe writing a zip file]] +* [[Documentation:ESSOC:detections:Detections#Detect_oulook_exe_writing_a__zip_file|Detect Oulook exe writing a zip file]] + +* [[Documentation:ESSOC:detections:Detections#Process_creating_lnk_file_in_suspicious_location|Process Creating LNK file in Suspicious Location]] -* [[Documentation:ESSOC:detections:Detections#Process_Creating_LNK_file_in_Suspicious_Location|Process Creating LNK file in Suspicious Location]] ====ATT&CK==== @@ -1203,6 +1158,7 @@ Detect signs of malicious payloads that may indicate that your environment has b | Initial Access |} + ====Kill Chain Phase==== * Actions on Objectives @@ -1221,7 +1177,7 @@ Detect signs of malicious payloads that may indicate that your environment has b ---- -===Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns=== +===Possible backdoor activity associated with mudcarp espionage campaigns=== Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1236,13 +1192,14 @@ Monitor your environment for suspicious behaviors that resemble the techniques e * [[Documentation:ESSOC:detections:Detections#First_time_seen_command_line_argument|First time seen command line argument]] -* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Connect_To_Internet_With_Hidden_Window|Malicious PowerShell Process - Connect To Internet With Hidden Window]] +* [[Documentation:ESSOC:detections:Detections#Malicious_powershell_process_-_connect_to_internet_with_hidden_window|Malicious PowerShell Process - Connect To Internet With Hidden Window]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_used_for_persistence|Registry Keys Used For Persistence]] -* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line|Unusually Long Command Line]] +* [[Documentation:ESSOC:detections:Detections#Unusually_long_command_line|Unusually Long Command Line]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_long_command_line_-_mltk|Unusually Long Command Line - MLTK]] -* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line_-_MLTK|Unusually Long Command Line - MLTK]] ====ATT&CK==== @@ -1264,6 +1221,7 @@ Monitor your environment for suspicious behaviors that resemble the techniques e | Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -1284,7 +1242,7 @@ Monitor your environment for suspicious behaviors that resemble the techniques e ---- -===SQL Injection=== +===Sql injection=== Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1297,19 +1255,10 @@ Use the searches in this Analytic Story to help you detect structured query lang ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#SQL_Injection_with_Long_URLs|SQL Injection with Long URLs]] +* [[Documentation:ESSOC:detections:Detections#Sql_injection_with_long_urls|SQL Injection with Long URLs]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1190 -| Exploit Public-Facing Application -| Initial Access -|} ====Kill Chain Phase==== @@ -1329,12 +1278,12 @@ Use the searches in this Analytic Story to help you detect structured query lang ---- -===Sunburst Malware=== +===Sunburst malware=== Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and management software. It was discovered by FireEye in December 2020. The actors behind this campaign gained access to numerous public and private organizations around the world. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint, Network_Traffic, Web -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1018/ T1018], [https://attack.mitre.org/techniques/T1027/ T1027], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1071.002/ T1071.002], [https://attack.mitre.org/techniques/T1203/ T1203], [https://attack.mitre.org/techniques/T1505.003/ T1505.003], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1569.002/ T1569.002] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.002/ T1071.002], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1569.002/ T1569.002], [https://attack.mitre.org/techniques/T1027/ T1027], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1203/ T1203], [https://attack.mitre.org/techniques/T1505.003/ T1505.003], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1018/ T1018] * '''Last Updated''': 2020-12-14
@@ -1342,27 +1291,28 @@ Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and managemen ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Outbound_SMB_Traffic|Detect Outbound SMB Traffic]] +* [[Documentation:ESSOC:detections:Detections#Detect_outbound_smb_traffic|Detect Outbound SMB Traffic]] -* [[Documentation:ESSOC:detections:Detections#Detect_Prohibited_Applications_Spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] +* [[Documentation:ESSOC:detections:Detections#Detect_prohibited_applications_spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] -* [[Documentation:ESSOC:detections:Detections#First_Time_Seen_Running_Windows_Service|First Time Seen Running Windows Service]] +* [[Documentation:ESSOC:detections:Detections#First_time_seen_running_windows_service|First Time Seen Running Windows Service]] -* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Encoded_Command|Malicious PowerShell Process - Encoded Command]] +* [[Documentation:ESSOC:detections:Detections#Malicious_powershell_process_-_encoded_command|Malicious PowerShell Process - Encoded Command]] -* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] +* [[Documentation:ESSOC:detections:Detections#Sc_exe_manipulating_windows_services|Sc exe Manipulating Windows Services]] -* [[Documentation:ESSOC:detections:Detections#Scheduled_Task_Deleted_Or_Created_via_CMD|Scheduled Task Deleted Or Created via CMD]] +* [[Documentation:ESSOC:detections:Detections#Scheduled_task_deleted_or_created_via_cmd|Scheduled Task Deleted Or Created via CMD]] * [[Documentation:ESSOC:detections:Detections#Schtasks_scheduling_job_on_remote_system|Schtasks scheduling job on remote system]] -* [[Documentation:ESSOC:detections:Detections#Sunburst_Correlation_DLL_and_Network_Event|Sunburst Correlation DLL and Network Event]] +* [[Documentation:ESSOC:detections:Detections#Sunburst_correlation_dll_and_network_event|Sunburst Correlation DLL and Network Event]] -* [[Documentation:ESSOC:detections:Detections#Supernova_Webshell|Supernova Webshell]] +* [[Documentation:ESSOC:detections:Detections#Supernova_webshell|Supernova Webshell]] -* [[Documentation:ESSOC:detections:Detections#TOR_Traffic|TOR Traffic]] +* [[Documentation:ESSOC:detections:Detections#Tor_traffic|TOR Traffic]] + +* [[Documentation:ESSOC:detections:Detections#Windows_adfind_exe|Windows AdFind Exe]] -* [[Documentation:ESSOC:detections:Detections#Windows_AdFind_Exe|Windows AdFind Exe]] ====ATT&CK==== @@ -1412,6 +1362,7 @@ Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and managemen | Discovery |} + ====Kill Chain Phase==== * Actions on Objectives @@ -1438,12 +1389,12 @@ Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and managemen ---- -===Suspicious Command-Line Executions=== +===Suspicious command-line executions=== Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2020-02-03
@@ -1451,17 +1402,18 @@ Leveraging the Windows command-line interface (CLI) is one of the most common at ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Prohibited_Applications_Spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] +* [[Documentation:ESSOC:detections:Detections#Detect_prohibited_applications_spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] -* [[Documentation:ESSOC:detections:Detections#Detect_Use_of_cmd_exe_to_Launch_Script_Interpreters|Detect Use of cmd exe to Launch Script Interpreters]] +* [[Documentation:ESSOC:detections:Detections#Detect_use_of_cmd_exe_to_launch_script_interpreters|Detect Use of cmd exe to Launch Script Interpreters]] * [[Documentation:ESSOC:detections:Detections#First_time_seen_command_line_argument|First time seen command line argument]] -* [[Documentation:ESSOC:detections:Detections#System_Processes_Run_From_Unexpected_Locations|System Processes Run From Unexpected Locations]] +* [[Documentation:ESSOC:detections:Detections#System_processes_run_from_unexpected_locations|System Processes Run From Unexpected Locations]] -* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line|Unusually Long Command Line]] +* [[Documentation:ESSOC:detections:Detections#Unusually_long_command_line|Unusually Long Command Line]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_long_command_line_-_mltk|Unusually Long Command Line - MLTK]] -* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line_-_MLTK|Unusually Long Command Line - MLTK]] ====ATT&CK==== @@ -1487,6 +1439,7 @@ Leveraging the Windows command-line interface (CLI) is one of the most common at | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -1511,7 +1464,7 @@ Leveraging the Windows command-line interface (CLI) is one of the most common at ---- -===Suspicious Compiled HTML Activity=== +===Suspicious compiled html activity=== Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1524,25 +1477,16 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_HTML_Help_Renamed|Detect HTML Help Renamed]] +* [[Documentation:ESSOC:detections:Detections#Detect_html_help_renamed|Detect HTML Help Renamed]] -* [[Documentation:ESSOC:detections:Detections#Detect_HTML_Help_Spawn_Child_Process|Detect HTML Help Spawn Child Process]] +* [[Documentation:ESSOC:detections:Detections#Detect_html_help_spawn_child_process|Detect HTML Help Spawn Child Process]] -* [[Documentation:ESSOC:detections:Detections#Detect_HTML_Help_URL_in_Command_Line|Detect HTML Help URL in Command Line]] +* [[Documentation:ESSOC:detections:Detections#Detect_html_help_url_in_command_line|Detect HTML Help URL in Command Line]] + +* [[Documentation:ESSOC:detections:Detections#Detect_html_help_using_infotech_storage_handlers|Detect HTML Help Using InfoTech Storage Handlers]] -* [[Documentation:ESSOC:detections:Detections#Detect_HTML_Help_Using_InfoTech_Storage_Handlers|Detect HTML Help Using InfoTech Storage Handlers]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.001 -| Compiled HTML File -| Defense Evasion -|} ====Kill Chain Phase==== @@ -1564,12 +1508,12 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce ---- -===Suspicious DNS Traffic=== +===Suspicious dns traffic=== Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Network_Resolution -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.004/ T1071.004], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.004/ T1071.004], [https://attack.mitre.org/techniques/T1095/ T1095], [https://attack.mitre.org/techniques/T1189/ T1189], [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1071.001/ T1071.001] * '''Last Updated''': 2017-09-18
@@ -1577,21 +1521,22 @@ Attackers often attempt to hide within or otherwise abuse the domain name system ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Clients_Connecting_to_Multiple_DNS_Servers|Clients Connecting to Multiple DNS Servers]] +* [[Documentation:ESSOC:detections:Detections#Clients_connecting_to_multiple_dns_servers|Clients Connecting to Multiple DNS Servers]] -* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_Outliers_-_MLTK|DNS Query Length Outliers - MLTK]] +* [[Documentation:ESSOC:detections:Detections#Dns_query_length_outliers_-_mltk|DNS Query Length Outliers - MLTK]] -* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_With_High_Standard_Deviation|DNS Query Length With High Standard Deviation]] +* [[Documentation:ESSOC:detections:Detections#Dns_query_length_with_high_standard_deviation|DNS Query Length With High Standard Deviation]] -* [[Documentation:ESSOC:detections:Detections#DNS_Query_Requests_Resolved_by_Unauthorized_DNS_Servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] +* [[Documentation:ESSOC:detections:Detections#Dns_query_requests_resolved_by_unauthorized_dns_servers|DNS Query Requests Resolved by Unauthorized DNS Servers]] -* [[Documentation:ESSOC:detections:Detections#Detect_Long_DNS_TXT_Record_Response|Detect Long DNS TXT Record Response]] +* [[Documentation:ESSOC:detections:Detections#Detect_long_dns_txt_record_response|Detect Long DNS TXT Record Response]] * [[Documentation:ESSOC:detections:Detections#Detect_hosts_connecting_to_dynamic_domain_providers|Detect hosts connecting to dynamic domain providers]] -* [[Documentation:ESSOC:detections:Detections#Detection_of_DNS_Tunnels|Detection of DNS Tunnels]] +* [[Documentation:ESSOC:detections:Detections#Detection_of_dns_tunnels|Detection of DNS Tunnels]] + +* [[Documentation:ESSOC:detections:Detections#Excessive_dns_failures|Excessive DNS Failures]] -* [[Documentation:ESSOC:detections:Detections#Excessive_DNS_Failures|Excessive DNS Failures]] ====ATT&CK==== @@ -1625,6 +1570,7 @@ Attackers often attempt to hide within or otherwise abuse the domain name system | Command and Control |} + ====Kill Chain Phase==== * Actions on Objectives @@ -1647,7 +1593,7 @@ Attackers often attempt to hide within or otherwise abuse the domain name system ---- -===Suspicious Emails=== +===Suspicious emails=== Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1660,13 +1606,14 @@ Email remains one of the primary means for attackers to gain an initial foothold ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Email_Attachments_With_Lots_Of_Spaces|Email Attachments With Lots Of Spaces]] +* [[Documentation:ESSOC:detections:Detections#Email_attachments_with_lots_of_spaces|Email Attachments With Lots Of Spaces]] -* [[Documentation:ESSOC:detections:Detections#Monitor_Email_For_Brand_Abuse|Monitor Email For Brand Abuse]] +* [[Documentation:ESSOC:detections:Detections#Monitor_email_for_brand_abuse|Monitor Email For Brand Abuse]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Email_-_UBA_Anomaly|Suspicious Email - UBA Anomaly]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_email_-_uba_anomaly|Suspicious Email - UBA Anomaly]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_email_attachment_extensions|Suspicious Email Attachment Extensions]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Email_Attachment_Extensions|Suspicious Email Attachment Extensions]] ====ATT&CK==== @@ -1684,6 +1631,7 @@ Email remains one of the primary means for attackers to gain an initial foothold | Initial Access |} + ====Kill Chain Phase==== * Delivery @@ -1700,12 +1648,12 @@ Email remains one of the primary means for attackers to gain an initial foothold ---- -===Suspicious MSHTA Activity=== +===Suspicious mshta activity=== Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1218.005/ T1218.005], [https://attack.mitre.org/techniques/T1547.001/ T1547.001] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.005/ T1218.005], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1547.001/ T1547.001] * '''Last Updated''': 2021-01-20
@@ -1713,23 +1661,24 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_MSHTA_Url_in_Command_Line|Detect MSHTA Url in Command Line]] +* [[Documentation:ESSOC:detections:Detections#Detect_mshta_url_in_command_line|Detect MSHTA Url in Command Line]] -* [[Documentation:ESSOC:detections:Detections#Detect_Prohibited_Applications_Spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] +* [[Documentation:ESSOC:detections:Detections#Detect_prohibited_applications_spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] -* [[Documentation:ESSOC:detections:Detections#Detect_Rundll32_Inline_HTA_Execution|Detect Rundll32 Inline HTA Execution]] +* [[Documentation:ESSOC:detections:Detections#Detect_rundll32_inline_hta_execution|Detect Rundll32 Inline HTA Execution]] * [[Documentation:ESSOC:detections:Detections#Detect_mshta_inline_hta_execution|Detect mshta inline hta execution]] * [[Documentation:ESSOC:detections:Detections#Detect_mshta_renamed|Detect mshta renamed]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_used_for_persistence|Registry Keys Used For Persistence]] * [[Documentation:ESSOC:detections:Detections#Suspicious_mshta_child_process|Suspicious mshta child process]] * [[Documentation:ESSOC:detections:Detections#Suspicious_mshta_spawn|Suspicious mshta spawn]] + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -1749,6 +1698,7 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce | Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -1773,7 +1723,7 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce ---- -===Suspicious Okta Activity=== +===Suspicious okta activity=== Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1786,25 +1736,16 @@ Monitor your Okta environment for suspicious activities. Due to the Covid outbre ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Multiple_Okta_Users_With_Invalid_Credentials_From_The_Same_IP|Multiple Okta Users With Invalid Credentials From The Same IP]] +* [[Documentation:ESSOC:detections:Detections#Multiple_okta_users_with_invalid_credentials_from_the_same_ip|Multiple Okta Users With Invalid Credentials From The Same IP]] -* [[Documentation:ESSOC:detections:Detections#Okta_Account_Lockout_Events|Okta Account Lockout Events]] +* [[Documentation:ESSOC:detections:Detections#Okta_account_lockout_events|Okta Account Lockout Events]] -* [[Documentation:ESSOC:detections:Detections#Okta_Failed_SSO_Attempts|Okta Failed SSO Attempts]] +* [[Documentation:ESSOC:detections:Detections#Okta_failed_sso_attempts|Okta Failed SSO Attempts]] + +* [[Documentation:ESSOC:detections:Detections#Okta_user_logins_from_multiple_cities|Okta User Logins From Multiple Cities]] -* [[Documentation:ESSOC:detections:Detections#Okta_User_Logins_From_Multiple_Cities|Okta User Logins From Multiple Cities]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.001 -| Default Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} ====Kill Chain Phase==== @@ -1824,7 +1765,7 @@ Monitor your Okta environment for suspicious activities. Due to the Covid outbre ---- -===Suspicious Regsvcs Regasm Activity=== +===Suspicious regsvcs regasm activity=== Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1837,29 +1778,20 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Regasm_Spawning_a_Process|Detect Regasm Spawning a Process]] +* [[Documentation:ESSOC:detections:Detections#Detect_regasm_spawning_a_process|Detect Regasm Spawning a Process]] -* [[Documentation:ESSOC:detections:Detections#Detect_Regasm_with_Network_Connection|Detect Regasm with Network Connection]] +* [[Documentation:ESSOC:detections:Detections#Detect_regasm_with_network_connection|Detect Regasm with Network Connection]] -* [[Documentation:ESSOC:detections:Detections#Detect_Regasm_with_no_Command_Line_Arguments|Detect Regasm with no Command Line Arguments]] +* [[Documentation:ESSOC:detections:Detections#Detect_regasm_with_no_command_line_arguments|Detect Regasm with no Command Line Arguments]] -* [[Documentation:ESSOC:detections:Detections#Detect_Regsvcs_Spawning_a_Process|Detect Regsvcs Spawning a Process]] +* [[Documentation:ESSOC:detections:Detections#Detect_regsvcs_spawning_a_process|Detect Regsvcs Spawning a Process]] -* [[Documentation:ESSOC:detections:Detections#Detect_Regsvcs_with_Network_Connection|Detect Regsvcs with Network Connection]] +* [[Documentation:ESSOC:detections:Detections#Detect_regsvcs_with_network_connection|Detect Regsvcs with Network Connection]] + +* [[Documentation:ESSOC:detections:Detections#Detect_regsvcs_with_no_command_line_arguments|Detect Regsvcs with No Command Line Arguments]] -* [[Documentation:ESSOC:detections:Detections#Detect_Regsvcs_with_No_Command_Line_Arguments|Detect Regsvcs with No Command Line Arguments]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.009 -| Regsvcs/Regasm -| Defense Evasion -|} ====Kill Chain Phase==== @@ -1881,7 +1813,7 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce ---- -===Suspicious Regsvr32 Activity=== +===Suspicious regsvr32 activity=== Monitor and detect techniques used by attackers who leverage the regsvr32.exe process to execute malicious code. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -1894,21 +1826,12 @@ Monitor and detect techniques used by attackers who leverage the regsvr32.exe pr ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Regsvr32_Application_Control_Bypass|Detect Regsvr32 Application Control Bypass]] +* [[Documentation:ESSOC:detections:Detections#Detect_regsvr32_application_control_bypass|Detect Regsvr32 Application Control Bypass]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_regsvr32_register_suspicious_path|Suspicious Regsvr32 Register Suspicious Path]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Regsvr32_Register_Suspicious_Path|Suspicious Regsvr32 Register Suspicious Path]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1218.010 -| Regsvr32 -| Defense Evasion -|} ====Kill Chain Phase==== @@ -1930,12 +1853,12 @@ Monitor and detect techniques used by attackers who leverage the regsvr32.exe pr ---- -===Suspicious Rundll32 Activity=== +===Suspicious rundll32 activity=== Monitor and detect techniques used by attackers who leverage rundll32.exe to execute arbitrary malicious code. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1218.011/ T1218.011], [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2021-02-03
@@ -1943,21 +1866,22 @@ Monitor and detect techniques used by attackers who leverage rundll32.exe to exe ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Rundll32_Application_Control_Bypass_-_advpack|Detect Rundll32 Application Control Bypass - advpack]] +* [[Documentation:ESSOC:detections:Detections#Detect_rundll32_application_control_bypass_-_advpack|Detect Rundll32 Application Control Bypass - advpack]] -* [[Documentation:ESSOC:detections:Detections#Detect_Rundll32_Application_Control_Bypass_-_setupapi|Detect Rundll32 Application Control Bypass - setupapi]] +* [[Documentation:ESSOC:detections:Detections#Detect_rundll32_application_control_bypass_-_setupapi|Detect Rundll32 Application Control Bypass - setupapi]] -* [[Documentation:ESSOC:detections:Detections#Detect_Rundll32_Application_Control_Bypass_-_syssetup|Detect Rundll32 Application Control Bypass - syssetup]] +* [[Documentation:ESSOC:detections:Detections#Detect_rundll32_application_control_bypass_-_syssetup|Detect Rundll32 Application Control Bypass - syssetup]] -* [[Documentation:ESSOC:detections:Detections#Dump_LSASS_via_comsvcs_DLL|Dump LSASS via comsvcs DLL]] +* [[Documentation:ESSOC:detections:Detections#Dump_lsass_via_comsvcs_dll|Dump LSASS via comsvcs DLL]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_Rename|Suspicious Rundll32 Rename]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_rundll32_rename|Suspicious Rundll32 Rename]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_StartW|Suspicious Rundll32 StartW]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_rundll32_startw|Suspicious Rundll32 StartW]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_dllregisterserver|Suspicious Rundll32 dllregisterserver]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_rundll32_dllregisterserver|Suspicious Rundll32 dllregisterserver]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_rundll32_no_commandline_arguments|Suspicious Rundll32 no CommandLine Arguments]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Rundll32_no_CommandLine_Arguments|Suspicious Rundll32 no CommandLine Arguments]] ====ATT&CK==== @@ -1979,6 +1903,7 @@ Monitor and detect techniques used by attackers who leverage rundll32.exe to exe | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -1999,7 +1924,7 @@ Monitor and detect techniques used by attackers who leverage rundll32.exe to exe ---- -===Suspicious WMI Use=== +===Suspicious wmi use=== Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2012,19 +1937,20 @@ Attackers are increasingly abusing Windows Management Instrumentation (WMI), a f ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Process_Execution_via_WMI|Process Execution via WMI]] +* [[Documentation:ESSOC:detections:Detections#Process_execution_via_wmi|Process Execution via WMI]] -* [[Documentation:ESSOC:detections:Detections#Remote_Process_Instantiation_via_WMI|Remote Process Instantiation via WMI]] +* [[Documentation:ESSOC:detections:Detections#Remote_process_instantiation_via_wmi|Remote Process Instantiation via WMI]] -* [[Documentation:ESSOC:detections:Detections#Remote_WMI_Command_Attempt|Remote WMI Command Attempt]] +* [[Documentation:ESSOC:detections:Detections#Remote_wmi_command_attempt|Remote WMI Command Attempt]] -* [[Documentation:ESSOC:detections:Detections#Script_Execution_via_WMI|Script Execution via WMI]] +* [[Documentation:ESSOC:detections:Detections#Script_execution_via_wmi|Script Execution via WMI]] -* [[Documentation:ESSOC:detections:Detections#WMI_Permanent_Event_Subscription|WMI Permanent Event Subscription]] +* [[Documentation:ESSOC:detections:Detections#Wmi_permanent_event_subscription|WMI Permanent Event Subscription]] -* [[Documentation:ESSOC:detections:Detections#WMI_Permanent_Event_Subscription_-_Sysmon|WMI Permanent Event Subscription - Sysmon]] +* [[Documentation:ESSOC:detections:Detections#Wmi_permanent_event_subscription_-_sysmon|WMI Permanent Event Subscription - Sysmon]] + +* [[Documentation:ESSOC:detections:Detections#Wmi_temporary_event_subscription|WMI Temporary Event Subscription]] -* [[Documentation:ESSOC:detections:Detections#WMI_Temporary_Event_Subscription|WMI Temporary Event Subscription]] ====ATT&CK==== @@ -2042,6 +1968,7 @@ Attackers are increasingly abusing Windows Management Instrumentation (WMI), a f | Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -2060,12 +1987,12 @@ Attackers are increasingly abusing Windows Management Instrumentation (WMI), a f ---- -===Suspicious Windows Registry Activities=== +===Suspicious windows registry activities=== Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1546.001/ T1546.001], [https://attack.mitre.org/techniques/T1546.011/ T1546.011], [https://attack.mitre.org/techniques/T1546.012/ T1546.012], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1547.010/ T1547.010], [https://attack.mitre.org/techniques/T1548.002/ T1548.002], [https://attack.mitre.org/techniques/T1564.001/ T1564.001] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1548.002/ T1548.002], [https://attack.mitre.org/techniques/T1222.001/ T1222.001], [https://attack.mitre.org/techniques/T1547.010/ T1547.010], [https://attack.mitre.org/techniques/T1564.001/ T1564.001], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1546.012/ T1546.012], [https://attack.mitre.org/techniques/T1546.011/ T1546.011], [https://attack.mitre.org/techniques/T1546.001/ T1546.001], [https://attack.mitre.org/techniques/T1112/ T1112] * '''Last Updated''': 2018-05-31
@@ -2073,21 +2000,22 @@ Monitor and detect registry changes initiated from remote locations, which can b ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Disabling_Remote_User_Account_Control|Disabling Remote User Account Control]] +* [[Documentation:ESSOC:detections:Detections#Disabling_remote_user_account_control|Disabling Remote User Account Control]] -* [[Documentation:ESSOC:detections:Detections#Monitor_Registry_Keys_for_Print_Monitors|Monitor Registry Keys for Print Monitors]] +* [[Documentation:ESSOC:detections:Detections#Monitor_registry_keys_for_print_monitors|Monitor Registry Keys for Print Monitors]] * [[Documentation:ESSOC:detections:Detections#Reg_exe_used_to_hide_files_directories_via_registry_keys|Reg exe used to hide files directories via registry keys]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_used_for_persistence|Registry Keys Used For Persistence]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Privilege_Escalation|Registry Keys Used For Privilege Escalation]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_used_for_privilege_escalation|Registry Keys Used For Privilege Escalation]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_for_Creating_SHIM_Databases|Registry Keys for Creating SHIM Databases]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_for_creating_shim_databases|Registry Keys for Creating SHIM Databases]] -* [[Documentation:ESSOC:detections:Detections#Remote_Registry_Key_modifications|Remote Registry Key modifications]] +* [[Documentation:ESSOC:detections:Detections#Remote_registry_key_modifications|Remote Registry Key modifications]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_changes_to_file_associations|Suspicious Changes to File Associations]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Changes_to_File_Associations|Suspicious Changes to File Associations]] ====ATT&CK==== @@ -2133,6 +2061,7 @@ Monitor and detect registry changes initiated from remote locations, which can b | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -2151,12 +2080,12 @@ Monitor and detect registry changes initiated from remote locations, which can b ---- -===Suspicious Zoom Child Processes=== +===Suspicious zoom child processes=== Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1068/ T1068] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2020-04-13
@@ -2164,9 +2093,10 @@ Attackers are using Zoom as an vector to increase privileges on a sytems. This s ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Prohibited_Applications_Spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] +* [[Documentation:ESSOC:detections:Detections#Detect_prohibited_applications_spawning_cmd_exe|Detect Prohibited Applications Spawning cmd exe]] + +* [[Documentation:ESSOC:detections:Detections#First_time_seen_child_process_of_zoom|First Time Seen Child Process of Zoom]] -* [[Documentation:ESSOC:detections:Detections#First_Time_Seen_Child_Process_of_Zoom|First Time Seen Child Process of Zoom]] ====ATT&CK==== @@ -2192,6 +2122,7 @@ Attackers are using Zoom as an vector to increase privileges on a sytems. This s | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -2212,12 +2143,12 @@ Attackers are using Zoom as an vector to increase privileges on a sytems. This s ---- -===Trusted Developer Utilities Proxy Execution=== +===Trusted developer utilities proxy execution=== Monitor and detect behaviors used by attackers who leverage trusted developer utilities to execute malicious code. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1127/ T1127], [https://attack.mitre.org/techniques/T1127, T1036.003/ T1127, T1036.003] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1127/ T1127], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2021-01-12
@@ -2230,21 +2161,23 @@ Monitor and detect behaviors used by attackers who leverage trusted developer ut * [[Documentation:ESSOC:detections:Detections#Suspicious_microsoft_workflow_compiler_usage|Suspicious microsoft workflow compiler usage]] + ====ATT&CK==== {| ! style="text-align:left;"| ID ! Technique ! Tactic |- -| -| -| -|- | T1127 | Trusted Developer Utilities Proxy Execution | Defense Evasion +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion |} + ====Kill Chain Phase==== * Exploitation @@ -2265,12 +2198,12 @@ Monitor and detect behaviors used by attackers who leverage trusted developer ut ---- -===Trusted Developer Utilities Proxy Execution MSBuild=== +===Trusted developer utilities proxy execution msbuild=== Monitor and detect techniques used by attackers who leverage the msbuild.exe process to execute malicious code. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1127.001/ T1127.001] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1127.001/ T1127.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003] * '''Last Updated''': 2021-01-21
@@ -2278,13 +2211,14 @@ Monitor and detect techniques used by attackers who leverage the msbuild.exe pro ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Suspicious_MSBuild_Rename|Suspicious MSBuild Rename]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_msbuild_rename|Suspicious MSBuild Rename]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_MSBuild_Spawn|Suspicious MSBuild Spawn]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_msbuild_spawn|Suspicious MSBuild Spawn]] * [[Documentation:ESSOC:detections:Detections#Suspicious_msbuild_path|Suspicious msbuild path]] + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -2300,6 +2234,7 @@ Monitor and detect techniques used by attackers who leverage the msbuild.exe pro | Defense Evasion |} + ====Kill Chain Phase==== * Exploitation @@ -2326,7 +2261,7 @@ Monitor and detect techniques used by attackers who leverage the msbuild.exe pro ---- -===Windows DNS SIGRed CVE-2020-1350=== +===Windows dns sigred cve-2020-1350=== Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpoint researchers, this vulnerability affects Windows 2003 to 2019, and is triggered by a malicious DNS response (only affects DNS over TCP). An attacker can use the malicious payload to cause a buffer overflow on the vulnerable system, leading to compromise. The included searches in this Analytic Story are designed to identify the large response payload for SIG and KEY DNS records which can be used for the exploit. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2339,21 +2274,12 @@ Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpo ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Windows_DNS_SIGRed_via_Splunk_Stream|Detect Windows DNS SIGRed via Splunk Stream]] +* [[Documentation:ESSOC:detections:Detections#Detect_windows_dns_sigred_via_splunk_stream|Detect Windows DNS SIGRed via Splunk Stream]] + +* [[Documentation:ESSOC:detections:Detections#Detect_windows_dns_sigred_via_zeek|Detect Windows DNS SIGRed via Zeek]] -* [[Documentation:ESSOC:detections:Detections#Detect_Windows_DNS_SIGRed_via_Zeek|Detect Windows DNS SIGRed via Zeek]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1203 -| Exploitation for Client Execution -| Execution -|} ====Kill Chain Phase==== @@ -2373,12 +2299,12 @@ Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpo ---- -===Windows Defense Evasion Tactics=== +===Windows defense evasion tactics=== Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1112/ T1112], [https://attack.mitre.org/techniques/T1222.001/ T1222.001], [https://attack.mitre.org/techniques/T1548.002/ T1548.002], [https://attack.mitre.org/techniques/T1564.001/ T1564.001] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1548.002/ T1548.002], [https://attack.mitre.org/techniques/T1222.001/ T1222.001], [https://attack.mitre.org/techniques/T1547.010/ T1547.010], [https://attack.mitre.org/techniques/T1564.001/ T1564.001], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1546.012/ T1546.012], [https://attack.mitre.org/techniques/T1546.011/ T1546.011], [https://attack.mitre.org/techniques/T1546.001/ T1546.001], [https://attack.mitre.org/techniques/T1112/ T1112] * '''Last Updated''': 2018-05-31
@@ -2386,15 +2312,16 @@ Detect tactics used by malware to evade defenses on Windows endpoints. A few of ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Disabling_Remote_User_Account_Control|Disabling Remote User Account Control]] +* [[Documentation:ESSOC:detections:Detections#Disabling_remote_user_account_control|Disabling Remote User Account Control]] -* [[Documentation:ESSOC:detections:Detections#Hiding_Files_And_Directories_With_Attrib_exe|Hiding Files And Directories With Attrib exe]] +* [[Documentation:ESSOC:detections:Detections#Hiding_files_and_directories_with_attrib_exe|Hiding Files And Directories With Attrib exe]] * [[Documentation:ESSOC:detections:Detections#Reg_exe_used_to_hide_files_directories_via_registry_keys|Reg exe used to hide files directories via registry keys]] -* [[Documentation:ESSOC:detections:Detections#Remote_Registry_Key_modifications|Remote Registry Key modifications]] +* [[Documentation:ESSOC:detections:Detections#Remote_registry_key_modifications|Remote Registry Key modifications]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_reg_exe_process|Suspicious Reg exe Process]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Reg_exe_Process|Suspicious Reg exe Process]] ====ATT&CK==== @@ -2440,6 +2367,7 @@ Detect tactics used by malware to evade defenses on Windows endpoints. A few of | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -2456,12 +2384,12 @@ Detect tactics used by malware to evade defenses on Windows endpoints. A few of ---- -===Windows Log Manipulation=== +===Windows log manipulation=== Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1070/ T1070], [https://attack.mitre.org/techniques/T1070.001/ T1070.001], [https://attack.mitre.org/techniques/T1490/ T1490] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1490/ T1490], [https://attack.mitre.org/techniques/T1070.001/ T1070.001], [https://attack.mitre.org/techniques/T1070/ T1070] * '''Last Updated''': 2017-09-12
@@ -2469,13 +2397,14 @@ Adversaries often try to cover their tracks by manipulating Windows logs. Use th ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Deleting_Shadow_Copies|Deleting Shadow Copies]] +* [[Documentation:ESSOC:detections:Detections#Deleting_shadow_copies|Deleting Shadow Copies]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_wevtutil_Usage|Suspicious wevtutil Usage]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_wevtutil_usage|Suspicious wevtutil Usage]] -* [[Documentation:ESSOC:detections:Detections#USN_Journal_Deletion|USN Journal Deletion]] +* [[Documentation:ESSOC:detections:Detections#Usn_journal_deletion|USN Journal Deletion]] + +* [[Documentation:ESSOC:detections:Detections#Windows_event_log_cleared|Windows Event Log Cleared]] -* [[Documentation:ESSOC:detections:Detections#Windows_Event_Log_Cleared|Windows Event Log Cleared]] ====ATT&CK==== @@ -2497,6 +2426,7 @@ Adversaries often try to cover their tracks by manipulating Windows logs. Use th | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -2517,12 +2447,12 @@ Adversaries often try to cover their tracks by manipulating Windows logs. Use th ---- -===Windows Persistence Techniques=== +===Windows persistence techniques=== Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1222.001/ T1222.001], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1546.011/ T1546.011], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1547.010/ T1547.010], [https://attack.mitre.org/techniques/T1564.001/ T1564.001], [https://attack.mitre.org/techniques/T1574.009/ T1574.009], [https://attack.mitre.org/techniques/T1574.011/ T1574.011] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1574.009/ T1574.009], [https://attack.mitre.org/techniques/T1222.001/ T1222.001], [https://attack.mitre.org/techniques/T1547.010/ T1547.010], [https://attack.mitre.org/techniques/T1574.011/ T1574.011], [https://attack.mitre.org/techniques/T1564.001/ T1564.001], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1546.011/ T1546.011], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1053.005/ T1053.005] * '''Last Updated''': 2018-05-31
@@ -2532,29 +2462,30 @@ Monitor for activities and techniques associated with maintaining persistence on * [[Documentation:ESSOC:detections:Detections#Certutil_exe_certificate_extraction|Certutil exe certificate extraction]] -* [[Documentation:ESSOC:detections:Detections#Detect_Path_Interception_By_Creation_Of_program_exe|Detect Path Interception By Creation Of program exe]] +* [[Documentation:ESSOC:detections:Detections#Detect_path_interception_by_creation_of_program_exe|Detect Path Interception By Creation Of program exe]] -* [[Documentation:ESSOC:detections:Detections#Hiding_Files_And_Directories_With_Attrib_exe|Hiding Files And Directories With Attrib exe]] +* [[Documentation:ESSOC:detections:Detections#Hiding_files_and_directories_with_attrib_exe|Hiding Files And Directories With Attrib exe]] -* [[Documentation:ESSOC:detections:Detections#Monitor_Registry_Keys_for_Print_Monitors|Monitor Registry Keys for Print Monitors]] +* [[Documentation:ESSOC:detections:Detections#Monitor_registry_keys_for_print_monitors|Monitor Registry Keys for Print Monitors]] -* [[Documentation:ESSOC:detections:Detections#Reg_exe_Manipulating_Windows_Services_Registry_Keys|Reg exe Manipulating Windows Services Registry Keys]] +* [[Documentation:ESSOC:detections:Detections#Reg_exe_manipulating_windows_services_registry_keys|Reg exe Manipulating Windows Services Registry Keys]] * [[Documentation:ESSOC:detections:Detections#Reg_exe_used_to_hide_files_directories_via_registry_keys|Reg exe used to hide files directories via registry keys]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_used_for_persistence|Registry Keys Used For Persistence]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_for_Creating_SHIM_Databases|Registry Keys for Creating SHIM Databases]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_for_creating_shim_databases|Registry Keys for Creating SHIM Databases]] -* [[Documentation:ESSOC:detections:Detections#Remote_Registry_Key_modifications|Remote Registry Key modifications]] +* [[Documentation:ESSOC:detections:Detections#Remote_registry_key_modifications|Remote Registry Key modifications]] -* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] +* [[Documentation:ESSOC:detections:Detections#Sc_exe_manipulating_windows_services|Sc exe Manipulating Windows Services]] * [[Documentation:ESSOC:detections:Detections#Schtasks_used_for_forcing_a_reboot|Schtasks used for forcing a reboot]] -* [[Documentation:ESSOC:detections:Detections#Shim_Database_File_Creation|Shim Database File Creation]] +* [[Documentation:ESSOC:detections:Detections#Shim_database_file_creation|Shim Database File Creation]] + +* [[Documentation:ESSOC:detections:Detections#Shim_database_installation_with_suspicious_parameters|Shim Database Installation With Suspicious Parameters]] -* [[Documentation:ESSOC:detections:Detections#Shim_Database_Installation_With_Suspicious_Parameters|Shim Database Installation With Suspicious Parameters]] ====ATT&CK==== @@ -2600,6 +2531,7 @@ Monitor for activities and techniques associated with maintaining persistence on | Execution, Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -2626,12 +2558,12 @@ Monitor for activities and techniques associated with maintaining persistence on ---- -===Windows Privilege Escalation=== +===Windows privilege escalation=== Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1546.008/ T1546.008], [https://attack.mitre.org/techniques/T1546.012/ T1546.012] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1068/ T1068], [https://attack.mitre.org/techniques/T1546.008/ T1546.008], [https://attack.mitre.org/techniques/T1546.012/ T1546.012], [https://attack.mitre.org/techniques/T1204.002/ T1204.002] * '''Last Updated''': 2020-02-04
@@ -2639,13 +2571,14 @@ Monitor for and investigate activities that may be associated with a Windows pri ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Child_Processes_of_Spoolsv_exe|Child Processes of Spoolsv exe]] +* [[Documentation:ESSOC:detections:Detections#Child_processes_of_spoolsv_exe|Child Processes of Spoolsv exe]] -* [[Documentation:ESSOC:detections:Detections#Overwriting_Accessibility_Binaries|Overwriting Accessibility Binaries]] +* [[Documentation:ESSOC:detections:Detections#Overwriting_accessibility_binaries|Overwriting Accessibility Binaries]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Privilege_Escalation|Registry Keys Used For Privilege Escalation]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_used_for_privilege_escalation|Registry Keys Used For Privilege Escalation]] + +* [[Documentation:ESSOC:detections:Detections#Uncommon_processes_on_endpoint|Uncommon Processes On Endpoint]] -* [[Documentation:ESSOC:detections:Detections#Uncommon_Processes_On_Endpoint|Uncommon Processes On Endpoint]] ====ATT&CK==== @@ -2671,6 +2604,7 @@ Monitor for and investigate activities that may be associated with a Windows pri | Execution |} + ====Kill Chain Phase==== * Actions on Objectives @@ -2694,7 +2628,7 @@ Monitor for and investigate activities that may be associated with a Windows pri ==Best Practices== -===Asset Tracking=== +===Asset tracking=== Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2707,15 +2641,10 @@ Keep a careful inventory of every asset on your network to make it easier to det ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Unauthorized_Assets_by_MAC_address|Detect Unauthorized Assets by MAC address]] +* [[Documentation:ESSOC:detections:Detections#Detect_unauthorized_assets_by_mac_address|Detect Unauthorized Assets by MAC address]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -2737,7 +2666,7 @@ Keep a careful inventory of every asset on your network to make it easier to det ---- -===Monitor Backup Solution=== +===Monitor backup solution=== Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2750,17 +2679,12 @@ Address common concerns when monitoring your backup processes. These searches ca ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Extended_Period_Without_Successful_Netbackup_Backups|Extended Period Without Successful Netbackup Backups]] +* [[Documentation:ESSOC:detections:Detections#Extended_period_without_successful_netbackup_backups|Extended Period Without Successful Netbackup Backups]] + +* [[Documentation:ESSOC:detections:Detections#Unsuccessful_netbackup_backups|Unsuccessful Netbackup backups]] -* [[Documentation:ESSOC:detections:Detections#Unsuccessful_Netbackup_backups|Unsuccessful Netbackup backups]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -2776,7 +2700,7 @@ Address common concerns when monitoring your backup processes. These searches ca ---- -===Monitor for Unauthorized Software=== +===Monitor for unauthorized software=== Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2789,15 +2713,10 @@ Identify and investigate prohibited/unauthorized software or processes that may ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Prohibited_Software_On_Endpoint|Prohibited Software On Endpoint]] +* [[Documentation:ESSOC:detections:Detections#Prohibited_software_on_endpoint|Prohibited Software On Endpoint]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -2819,7 +2738,7 @@ Identify and investigate prohibited/unauthorized software or processes that may ---- -===Monitor for Updates=== +===Monitor for updates=== Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -2832,15 +2751,10 @@ Monitor your enterprise to ensure that your endpoints are being patched and upda ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#No_Windows_Updates_in_a_time_frame|No Windows Updates in a time frame]] +* [[Documentation:ESSOC:detections:Detections#No_windows_updates_in_a_time_frame|No Windows Updates in a time frame]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -2856,12 +2770,12 @@ Monitor your enterprise to ensure that your endpoints are being patched and upda ---- -===Prohibited Traffic Allowed or Protocol Mismatch=== +===Prohibited traffic allowed or protocol mismatch=== Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Network_Resolution, Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1189/ T1189], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1048/ T1048] * '''Last Updated''': 2017-09-11
@@ -2871,11 +2785,12 @@ Detect instances of prohibited network traffic allowed in the environment, as we * [[Documentation:ESSOC:detections:Detections#Detect_hosts_connecting_to_dynamic_domain_providers|Detect hosts connecting to dynamic domain providers]] -* [[Documentation:ESSOC:detections:Detections#Prohibited_Network_Traffic_Allowed|Prohibited Network Traffic Allowed]] +* [[Documentation:ESSOC:detections:Detections#Prohibited_network_traffic_allowed|Prohibited Network Traffic Allowed]] -* [[Documentation:ESSOC:detections:Detections#Protocol_or_Port_Mismatch|Protocol or Port Mismatch]] +* [[Documentation:ESSOC:detections:Detections#Protocol_or_port_mismatch|Protocol or Port Mismatch]] + +* [[Documentation:ESSOC:detections:Detections#Tor_traffic|TOR Traffic]] -* [[Documentation:ESSOC:detections:Detections#TOR_Traffic|TOR Traffic]] ====ATT&CK==== @@ -2901,6 +2816,7 @@ Detect instances of prohibited network traffic allowed in the environment, as we | Exfiltration |} + ====Kill Chain Phase==== * Actions on Objectives @@ -2921,12 +2837,12 @@ Detect instances of prohibited network traffic allowed in the environment, as we ---- -===Router and Infrastructure Security=== +===Router and infrastructure security=== Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Authentication, Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1020.001/ T1020.001], [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1542.005/ T1542.005], [https://attack.mitre.org/techniques/T1557/ T1557], [https://attack.mitre.org/techniques/T1557.002/ T1557.002] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1200/ T1200], [https://attack.mitre.org/techniques/T1498/ T1498], [https://attack.mitre.org/techniques/T1557.002/ T1557.002], [https://attack.mitre.org/techniques/T1557/ T1557], [https://attack.mitre.org/techniques/T1542.005/ T1542.005], [https://attack.mitre.org/techniques/T1020.001/ T1020.001] * '''Last Updated''': 2017-09-12
@@ -2934,19 +2850,20 @@ Validate the security configuration of network infrastructure and verify that on ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_ARP_Poisoning|Detect ARP Poisoning]] +* [[Documentation:ESSOC:detections:Detections#Detect_arp_poisoning|Detect ARP Poisoning]] -* [[Documentation:ESSOC:detections:Detections#Detect_IPv6_Network_Infrastructure_Threats|Detect IPv6 Network Infrastructure Threats]] +* [[Documentation:ESSOC:detections:Detections#Detect_ipv6_network_infrastructure_threats|Detect IPv6 Network Infrastructure Threats]] -* [[Documentation:ESSOC:detections:Detections#Detect_New_Login_Attempts_to_Routers|Detect New Login Attempts to Routers]] +* [[Documentation:ESSOC:detections:Detections#Detect_new_login_attempts_to_routers|Detect New Login Attempts to Routers]] -* [[Documentation:ESSOC:detections:Detections#Detect_Port_Security_Violation|Detect Port Security Violation]] +* [[Documentation:ESSOC:detections:Detections#Detect_port_security_violation|Detect Port Security Violation]] -* [[Documentation:ESSOC:detections:Detections#Detect_Rogue_DHCP_Server|Detect Rogue DHCP Server]] +* [[Documentation:ESSOC:detections:Detections#Detect_rogue_dhcp_server|Detect Rogue DHCP Server]] -* [[Documentation:ESSOC:detections:Detections#Detect_Software_Download_To_Network_Device|Detect Software Download To Network Device]] +* [[Documentation:ESSOC:detections:Detections#Detect_software_download_to_network_device|Detect Software Download To Network Device]] + +* [[Documentation:ESSOC:detections:Detections#Detect_traffic_mirroring|Detect Traffic Mirroring]] -* [[Documentation:ESSOC:detections:Detections#Detect_Traffic_Mirroring|Detect Traffic Mirroring]] ====ATT&CK==== @@ -2980,6 +2897,7 @@ Validate the security configuration of network infrastructure and verify that on | Exfiltration |} + ====Kill Chain Phase==== * Actions on Objectives @@ -3004,7 +2922,7 @@ Validate the security configuration of network infrastructure and verify that on ---- -===Use of Cleartext Protocols=== +===Use of cleartext protocols=== Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3020,12 +2938,7 @@ Leverage searches that detect cleartext network protocols that may leak credenti * [[Documentation:ESSOC:detections:Detections#Protocols_passing_authentication_in_cleartext|Protocols passing authentication in cleartext]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} + ====Kill Chain Phase==== @@ -3050,7 +2963,7 @@ Leverage searches that detect cleartext network protocols that may leak credenti ==Cloud Security== -===AWS Cross Account Activity=== +===Aws cross account activity=== Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3063,15 +2976,16 @@ Track when a user assumes an IAM role in another AWS account to obtain cross-acc ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#aws_detect_attach_to_role_policy|aws detect attach to role policy]] +* [[Documentation:ESSOC:detections:Detections#Aws_detect_attach_to_role_policy|aws detect attach to role policy]] -* [[Documentation:ESSOC:detections:Detections#aws_detect_permanent_key_creation|aws detect permanent key creation]] +* [[Documentation:ESSOC:detections:Detections#Aws_detect_permanent_key_creation|aws detect permanent key creation]] -* [[Documentation:ESSOC:detections:Detections#aws_detect_role_creation|aws detect role creation]] +* [[Documentation:ESSOC:detections:Detections#Aws_detect_role_creation|aws detect role creation]] -* [[Documentation:ESSOC:detections:Detections#aws_detect_sts_assume_role_abuse|aws detect sts assume role abuse]] +* [[Documentation:ESSOC:detections:Detections#Aws_detect_sts_assume_role_abuse|aws detect sts assume role abuse]] + +* [[Documentation:ESSOC:detections:Detections#Aws_detect_sts_get_session_token_abuse|aws detect sts get session token abuse]] -* [[Documentation:ESSOC:detections:Detections#aws_detect_sts_get_session_token_abuse|aws detect sts get session token abuse]] ====ATT&CK==== @@ -3089,6 +3003,7 @@ Track when a user assumes an IAM role in another AWS account to obtain cross-acc | Defense Evasion, Lateral Movement |} + ====Kill Chain Phase==== * Lateral Movement @@ -3105,7 +3020,7 @@ Track when a user assumes an IAM role in another AWS account to obtain cross-acc ---- -===AWS Cryptomining=== +===Aws cryptomining=== Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3118,17 +3033,18 @@ Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomin ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Launched_by_User|Abnormally High AWS Instances Launched by User]] +* [[Documentation:ESSOC:detections:Detections#Abnormally_high_aws_instances_launched_by_user|Abnormally High AWS Instances Launched by User]] -* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Launched_by_User_-_MLTK|Abnormally High AWS Instances Launched by User - MLTK]] +* [[Documentation:ESSOC:detections:Detections#Abnormally_high_aws_instances_launched_by_user_-_mltk|Abnormally High AWS Instances Launched by User - MLTK]] -* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_In_Previously_Unseen_Region|EC2 Instance Started In Previously Unseen Region]] +* [[Documentation:ESSOC:detections:Detections#Ec2_instance_started_in_previously_unseen_region|EC2 Instance Started In Previously Unseen Region]] -* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_With_Previously_Unseen_AMI|EC2 Instance Started With Previously Unseen AMI]] +* [[Documentation:ESSOC:detections:Detections#Ec2_instance_started_with_previously_unseen_ami|EC2 Instance Started With Previously Unseen AMI]] -* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_With_Previously_Unseen_Instance_Type|EC2 Instance Started With Previously Unseen Instance Type]] +* [[Documentation:ESSOC:detections:Detections#Ec2_instance_started_with_previously_unseen_instance_type|EC2 Instance Started With Previously Unseen Instance Type]] + +* [[Documentation:ESSOC:detections:Detections#Ec2_instance_started_with_previously_unseen_user|EC2 Instance Started With Previously Unseen User]] -* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_With_Previously_Unseen_User|EC2 Instance Started With Previously Unseen User]] ====ATT&CK==== @@ -3146,6 +3062,7 @@ Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomin | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -3162,7 +3079,7 @@ Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomin ---- -===AWS Network ACL Activity=== +===Aws network acl activity=== Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3175,25 +3092,16 @@ Monitor your AWS network infrastructure for bad configurations and malicious act ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#AWS_Network_Access_Control_List_Created_with_All_Open_Ports|AWS Network Access Control List Created with All Open Ports]] +* [[Documentation:ESSOC:detections:Detections#Aws_network_access_control_list_created_with_all_open_ports|AWS Network Access Control List Created with All Open Ports]] -* [[Documentation:ESSOC:detections:Detections#AWS_Network_Access_Control_List_Deleted|AWS Network Access Control List Deleted]] +* [[Documentation:ESSOC:detections:Detections#Aws_network_access_control_list_deleted|AWS Network Access Control List Deleted]] -* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_Network_ACL_Activity|Detect Spike in Network ACL Activity]] +* [[Documentation:ESSOC:detections:Detections#Detect_spike_in_network_acl_activity|Detect Spike in Network ACL Activity]] + +* [[Documentation:ESSOC:detections:Detections#Detect_spike_in_blocked_outbound_traffic_from_your_aws|Detect Spike in blocked Outbound Traffic from your AWS]] -* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_blocked_Outbound_Traffic_from_your_AWS|Detect Spike in blocked Outbound Traffic from your AWS]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1562.007 -| Disable or Modify Cloud Firewall -| Defense Evasion -|} ====Kill Chain Phase==== @@ -3215,7 +3123,7 @@ Monitor your AWS network infrastructure for bad configurations and malicious act ---- -===AWS Security Hub Alerts=== +===Aws security hub alerts=== This story is focused around detecting Security Hub alerts generated from AWS * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3228,17 +3136,12 @@ This story is focused around detecting Security Hub alerts generated from AWS ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_AWS_Security_Hub_Alerts_for_EC2_Instance|Detect Spike in AWS Security Hub Alerts for EC2 Instance]] +* [[Documentation:ESSOC:detections:Detections#Detect_spike_in_aws_security_hub_alerts_for_ec2_instance|Detect Spike in AWS Security Hub Alerts for EC2 Instance]] + +* [[Documentation:ESSOC:detections:Detections#Detect_spike_in_aws_security_hub_alerts_for_user|Detect Spike in AWS Security Hub Alerts for User]] -* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_AWS_Security_Hub_Alerts_for_User|Detect Spike in AWS Security Hub Alerts for User]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -3254,7 +3157,7 @@ This story is focused around detecting Security Hub alerts generated from AWS ---- -===AWS Suspicious Provisioning Activities=== +===Aws suspicious provisioning activities=== Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3267,25 +3170,16 @@ Monitor your AWS provisioning activities for behaviors originating from unfamili ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#AWS_Cloud_Provisioning_From_Previously_Unseen_City|AWS Cloud Provisioning From Previously Unseen City]] +* [[Documentation:ESSOC:detections:Detections#Aws_cloud_provisioning_from_previously_unseen_city|AWS Cloud Provisioning From Previously Unseen City]] -* [[Documentation:ESSOC:detections:Detections#AWS_Cloud_Provisioning_From_Previously_Unseen_Country|AWS Cloud Provisioning From Previously Unseen Country]] +* [[Documentation:ESSOC:detections:Detections#Aws_cloud_provisioning_from_previously_unseen_country|AWS Cloud Provisioning From Previously Unseen Country]] -* [[Documentation:ESSOC:detections:Detections#AWS_Cloud_Provisioning_From_Previously_Unseen_IP_Address|AWS Cloud Provisioning From Previously Unseen IP Address]] +* [[Documentation:ESSOC:detections:Detections#Aws_cloud_provisioning_from_previously_unseen_ip_address|AWS Cloud Provisioning From Previously Unseen IP Address]] + +* [[Documentation:ESSOC:detections:Detections#Aws_cloud_provisioning_from_previously_unseen_region|AWS Cloud Provisioning From Previously Unseen Region]] -* [[Documentation:ESSOC:detections:Detections#AWS_Cloud_Provisioning_From_Previously_Unseen_Region|AWS Cloud Provisioning From Previously Unseen Region]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1535 -| Unused/Unsupported Cloud Regions -| Defense Evasion -|} ====Kill Chain Phase==== @@ -3301,7 +3195,7 @@ Monitor your AWS provisioning activities for behaviors originating from unfamili ---- -===AWS User Monitoring=== +===Aws user monitoring=== Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3314,27 +3208,18 @@ Detect and investigate dormant user accounts for your AWS environment that have ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_API_activity_from_users_without_MFA|Detect API activity from users without MFA]] +* [[Documentation:ESSOC:detections:Detections#Detect_api_activity_from_users_without_mfa|Detect API activity from users without MFA]] -* [[Documentation:ESSOC:detections:Detections#Detect_AWS_API_Activities_From_Unapproved_Accounts|Detect AWS API Activities From Unapproved Accounts]] +* [[Documentation:ESSOC:detections:Detections#Detect_aws_api_activities_from_unapproved_accounts|Detect AWS API Activities From Unapproved Accounts]] -* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_AWS_API_Activity|Detect Spike in AWS API Activity]] +* [[Documentation:ESSOC:detections:Detections#Detect_spike_in_aws_api_activity|Detect Spike in AWS API Activity]] -* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_Security_Group_Activity|Detect Spike in Security Group Activity]] +* [[Documentation:ESSOC:detections:Detections#Detect_spike_in_security_group_activity|Detect Spike in Security Group Activity]] + +* [[Documentation:ESSOC:detections:Detections#Detect_new_api_calls_from_user_roles|Detect new API calls from user roles]] -* [[Documentation:ESSOC:detections:Detections#Detect_new_API_calls_from_user_roles|Detect new API calls from user roles]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} ====Kill Chain Phase==== @@ -3354,7 +3239,7 @@ Detect and investigate dormant user accounts for your AWS environment that have ---- -===Cloud Cryptomining=== +===Cloud cryptomining=== Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3367,15 +3252,16 @@ Monitor your cloud compute instances for activities related to cryptojacking/cry ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Abnormally_High_Number_Of_Cloud_Instances_Launched|Abnormally High Number Of Cloud Instances Launched]] +* [[Documentation:ESSOC:detections:Detections#Abnormally_high_number_of_cloud_instances_launched|Abnormally High Number Of Cloud Instances Launched]] -* [[Documentation:ESSOC:detections:Detections#Cloud_Compute_Instance_Created_By_Previously_Unseen_User|Cloud Compute Instance Created By Previously Unseen User]] +* [[Documentation:ESSOC:detections:Detections#Cloud_compute_instance_created_by_previously_unseen_user|Cloud Compute Instance Created By Previously Unseen User]] -* [[Documentation:ESSOC:detections:Detections#Cloud_Compute_Instance_Created_In_Previously_Unused_Region|Cloud Compute Instance Created In Previously Unused Region]] +* [[Documentation:ESSOC:detections:Detections#Cloud_compute_instance_created_in_previously_unused_region|Cloud Compute Instance Created In Previously Unused Region]] -* [[Documentation:ESSOC:detections:Detections#Cloud_Compute_Instance_Created_With_Previously_Unseen_Image|Cloud Compute Instance Created With Previously Unseen Image]] +* [[Documentation:ESSOC:detections:Detections#Cloud_compute_instance_created_with_previously_unseen_image|Cloud Compute Instance Created With Previously Unseen Image]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_compute_instance_created_with_previously_unseen_instance_type|Cloud Compute Instance Created With Previously Unseen Instance Type]] -* [[Documentation:ESSOC:detections:Detections#Cloud_Compute_Instance_Created_With_Previously_Unseen_Instance_Type|Cloud Compute Instance Created With Previously Unseen Instance Type]] ====ATT&CK==== @@ -3393,6 +3279,7 @@ Monitor your cloud compute instances for activities related to cryptojacking/cry | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -3409,12 +3296,12 @@ Monitor your cloud compute instances for activities related to cryptojacking/cry ---- -===Cloud Federated Credential Abuse=== +===Cloud federated credential abuse=== This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1136.003/ T1136.003], [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1546.012/ T1546.012], [https://attack.mitre.org/techniques/T1556/ T1556] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1136.003/ T1136.003], [https://attack.mitre.org/techniques/T1556/ T1556], [https://attack.mitre.org/techniques/T1546.012/ T1546.012], [https://attack.mitre.org/techniques/T1204.002/ T1204.002] * '''Last Updated''': 2021-01-26
@@ -3422,29 +3309,30 @@ This analytical story addresses events that indicate abuse of cloud federated cr ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#AWS_SAML_Access_by_Provider_User_and_Principal|AWS SAML Access by Provider User and Principal]] +* [[Documentation:ESSOC:detections:Detections#Aws_saml_access_by_provider_user_and_principal|AWS SAML Access by Provider User and Principal]] -* [[Documentation:ESSOC:detections:Detections#AWS_SAML_Update_identity_provider|AWS SAML Update identity provider]] +* [[Documentation:ESSOC:detections:Detections#Aws_saml_update_identity_provider|AWS SAML Update identity provider]] * [[Documentation:ESSOC:detections:Detections#Certutil_exe_certificate_extraction|Certutil exe certificate extraction]] -* [[Documentation:ESSOC:detections:Detections#Detect_Mimikatz_Using_Loaded_Images|Detect Mimikatz Using Loaded Images]] +* [[Documentation:ESSOC:detections:Detections#Detect_mimikatz_using_loaded_images|Detect Mimikatz Using Loaded Images]] -* [[Documentation:ESSOC:detections:Detections#Detect_Mimikatz_Via_PowerShell_And_EventCode_4703|Detect Mimikatz Via PowerShell And EventCode 4703]] +* [[Documentation:ESSOC:detections:Detections#Detect_mimikatz_via_powershell_and_eventcode_4703|Detect Mimikatz Via PowerShell And EventCode 4703]] -* [[Documentation:ESSOC:detections:Detections#Detect_Rare_Executables|Detect Rare Executables]] +* [[Documentation:ESSOC:detections:Detections#Detect_rare_executables|Detect Rare Executables]] -* [[Documentation:ESSOC:detections:Detections#O365_Add_App_Role_Assignment_Grant_User|O365 Add App Role Assignment Grant User]] +* [[Documentation:ESSOC:detections:Detections#O365_add_app_role_assignment_grant_user|O365 Add App Role Assignment Grant User]] -* [[Documentation:ESSOC:detections:Detections#O365_Added_Service_Principal|O365 Added Service Principal]] +* [[Documentation:ESSOC:detections:Detections#O365_added_service_principal|O365 Added Service Principal]] -* [[Documentation:ESSOC:detections:Detections#O365_Excessive_SSO_logon_errors|O365 Excessive SSO logon errors]] +* [[Documentation:ESSOC:detections:Detections#O365_excessive_sso_logon_errors|O365 Excessive SSO logon errors]] -* [[Documentation:ESSOC:detections:Detections#O365_New_Federated_Domain_Added|O365 New Federated Domain Added]] +* [[Documentation:ESSOC:detections:Detections#O365_new_federated_domain_added|O365 New Federated Domain Added]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Privilege_Escalation|Registry Keys Used For Privilege Escalation]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_used_for_privilege_escalation|Registry Keys Used For Privilege Escalation]] + +* [[Documentation:ESSOC:detections:Detections#Uncommon_processes_on_endpoint|Uncommon Processes On Endpoint]] -* [[Documentation:ESSOC:detections:Detections#Uncommon_Processes_On_Endpoint|Uncommon Processes On Endpoint]] ====ATT&CK==== @@ -3478,6 +3366,7 @@ This analytical story addresses events that indicate abuse of cloud federated cr | Execution |} + ====Kill Chain Phase==== * Actions on Objective @@ -3504,7 +3393,7 @@ This analytical story addresses events that indicate abuse of cloud federated cr ---- -===Container Implantation Monitoring and Investigation=== +===Container implantation monitoring and investigation=== Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3517,21 +3406,12 @@ Use the searches in this story to monitor your Kubernetes registry repositories ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#GCP_GCR_container_uploaded|GCP GCR container uploaded]] +* [[Documentation:ESSOC:detections:Detections#Gcp_gcr_container_uploaded|GCP GCR container uploaded]] + +* [[Documentation:ESSOC:detections:Detections#New_container_uploaded_to_aws_ecr|New container uploaded to AWS ECR]] -* [[Documentation:ESSOC:detections:Detections#New_container_uploaded_to_AWS_ECR|New container uploaded to AWS ECR]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1525 -| Implant Container Image -| Persistence -|} ====Kill Chain Phase==== @@ -3547,7 +3427,7 @@ Use the searches in this story to monitor your Kubernetes registry repositories ---- -===GCP Cross Account Activity=== +===Gcp cross account activity=== Track when a user assumes an IAM role in another GCP account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3560,25 +3440,16 @@ Track when a user assumes an IAM role in another GCP account to obtain cross-acc ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#GCP_Detect_accounts_with_high_risk_roles_by_project|GCP Detect accounts with high risk roles by project]] +* [[Documentation:ESSOC:detections:Detections#Gcp_detect_accounts_with_high_risk_roles_by_project|GCP Detect accounts with high risk roles by project]] -* [[Documentation:ESSOC:detections:Detections#GCP_Detect_gcploit_framework|GCP Detect gcploit framework]] +* [[Documentation:ESSOC:detections:Detections#Gcp_detect_gcploit_framework|GCP Detect gcploit framework]] -* [[Documentation:ESSOC:detections:Detections#GCP_Detect_high_risk_permissions_by_resource_and_account|GCP Detect high risk permissions by resource and account]] +* [[Documentation:ESSOC:detections:Detections#Gcp_detect_high_risk_permissions_by_resource_and_account|GCP Detect high risk permissions by resource and account]] + +* [[Documentation:ESSOC:detections:Detections#Gcp_detect_oauth_token_abuse|gcp detect oauth token abuse]] -* [[Documentation:ESSOC:detections:Detections#gcp_detect_oauth_token_abuse|gcp detect oauth token abuse]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} ====Kill Chain Phase==== @@ -3596,7 +3467,7 @@ Track when a user assumes an IAM role in another GCP account to obtain cross-acc ---- -===Kubernetes Scanning Activity=== +===Kubernetes scanning activity=== This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3609,29 +3480,20 @@ This story addresses detection against Kubernetes cluster fingerprint scan and a ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Amazon_EKS_Kubernetes_Pod_scan_detection|Amazon EKS Kubernetes Pod scan detection]] +* [[Documentation:ESSOC:detections:Detections#Amazon_eks_kubernetes_pod_scan_detection|Amazon EKS Kubernetes Pod scan detection]] -* [[Documentation:ESSOC:detections:Detections#Amazon_EKS_Kubernetes_cluster_scan_detection|Amazon EKS Kubernetes cluster scan detection]] +* [[Documentation:ESSOC:detections:Detections#Amazon_eks_kubernetes_cluster_scan_detection|Amazon EKS Kubernetes cluster scan detection]] -* [[Documentation:ESSOC:detections:Detections#GCP_Kubernetes_cluster_pod_scan_detection|GCP Kubernetes cluster pod scan detection]] +* [[Documentation:ESSOC:detections:Detections#Gcp_kubernetes_cluster_pod_scan_detection|GCP Kubernetes cluster pod scan detection]] -* [[Documentation:ESSOC:detections:Detections#GCP_Kubernetes_cluster_scan_detection|GCP Kubernetes cluster scan detection]] +* [[Documentation:ESSOC:detections:Detections#Gcp_kubernetes_cluster_scan_detection|GCP Kubernetes cluster scan detection]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_pod_scan_fingerprint|Kubernetes Azure pod scan fingerprint]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_azure_pod_scan_fingerprint|Kubernetes Azure pod scan fingerprint]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_azure_scan_fingerprint|Kubernetes Azure scan fingerprint]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_scan_fingerprint|Kubernetes Azure scan fingerprint]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1526 -| Cloud Service Discovery -| Discovery -|} ====Kill Chain Phase==== @@ -3649,7 +3511,7 @@ This story addresses detection against Kubernetes cluster fingerprint scan and a ---- -===Kubernetes Sensitive Object Access Activity=== +===Kubernetes sensitive object access activity=== This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3662,31 +3524,26 @@ This story addresses detection and response of accounts acccesing Kubernetes clu ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#AWS_EKS_Kubernetes_cluster_sensitive_object_access|AWS EKS Kubernetes cluster sensitive object access]] +* [[Documentation:ESSOC:detections:Detections#Aws_eks_kubernetes_cluster_sensitive_object_access|AWS EKS Kubernetes cluster sensitive object access]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_AWS_detect_service_accounts_forbidden_failure_access|Kubernetes AWS detect service accounts forbidden failure access]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_aws_detect_service_accounts_forbidden_failure_access|Kubernetes AWS detect service accounts forbidden failure access]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_AWS_detect_suspicious_kubectl_calls|Kubernetes AWS detect suspicious kubectl calls]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_aws_detect_suspicious_kubectl_calls|Kubernetes AWS detect suspicious kubectl calls]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_sensitive_object_access|Kubernetes Azure detect sensitive object access]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_azure_detect_sensitive_object_access|Kubernetes Azure detect sensitive object access]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_service_accounts_forbidden_failure_access|Kubernetes Azure detect service accounts forbidden failure access]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_azure_detect_service_accounts_forbidden_failure_access|Kubernetes Azure detect service accounts forbidden failure access]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_suspicious_kubectl_calls|Kubernetes Azure detect suspicious kubectl calls]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_azure_detect_suspicious_kubectl_calls|Kubernetes Azure detect suspicious kubectl calls]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_sensitive_object_access|Kubernetes GCP detect sensitive object access]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_gcp_detect_sensitive_object_access|Kubernetes GCP detect sensitive object access]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_service_accounts_forbidden_failure_access|Kubernetes GCP detect service accounts forbidden failure access]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_gcp_detect_service_accounts_forbidden_failure_access|Kubernetes GCP detect service accounts forbidden failure access]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_gcp_detect_suspicious_kubectl_calls|Kubernetes GCP detect suspicious kubectl calls]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_suspicious_kubectl_calls|Kubernetes GCP detect suspicious kubectl calls]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -3704,7 +3561,7 @@ This story addresses detection and response of accounts acccesing Kubernetes clu ---- -===Kubernetes Sensitive Role Activity=== +===Kubernetes sensitive role activity=== This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3717,31 +3574,26 @@ This story addresses detection and response around Sensitive Role usage within a ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Kubernetes_AWS_detect_RBAC_authorization_by_account|Kubernetes AWS detect RBAC authorization by account]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_aws_detect_rbac_authorization_by_account|Kubernetes AWS detect RBAC authorization by account]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_AWS_detect_most_active_service_accounts_by_pod|Kubernetes AWS detect most active service accounts by pod]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_aws_detect_most_active_service_accounts_by_pod|Kubernetes AWS detect most active service accounts by pod]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_AWS_detect_sensitive_role_access|Kubernetes AWS detect sensitive role access]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_aws_detect_sensitive_role_access|Kubernetes AWS detect sensitive role access]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_RBAC_authorization_by_account|Kubernetes Azure detect RBAC authorization by account]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_azure_detect_rbac_authorization_by_account|Kubernetes Azure detect RBAC authorization by account]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_most_active_service_accounts_by_pod_namespace|Kubernetes Azure detect most active service accounts by pod namespace]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace|Kubernetes Azure detect most active service accounts by pod namespace]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_Azure_detect_sensitive_role_access|Kubernetes Azure detect sensitive role access]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_azure_detect_sensitive_role_access|Kubernetes Azure detect sensitive role access]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_RBAC_authorizations_by_account|Kubernetes GCP detect RBAC authorizations by account]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_gcp_detect_rbac_authorizations_by_account|Kubernetes GCP detect RBAC authorizations by account]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_most_active_service_accounts_by_pod|Kubernetes GCP detect most active service accounts by pod]] +* [[Documentation:ESSOC:detections:Detections#Kubernetes_gcp_detect_most_active_service_accounts_by_pod|Kubernetes GCP detect most active service accounts by pod]] + +* [[Documentation:ESSOC:detections:Detections#Kubernetes_gcp_detect_sensitive_role_access|Kubernetes GCP detect sensitive role access]] -* [[Documentation:ESSOC:detections:Detections#Kubernetes_GCP_detect_sensitive_role_access|Kubernetes GCP detect sensitive role access]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -3759,12 +3611,12 @@ This story addresses detection and response around Sensitive Role usage within a ---- -===Office 365 Detections=== +===Office 365 detections=== This story is focused around detecting Office 365 Attacks. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1110/ T1110], [https://attack.mitre.org/techniques/T1110.001/ T1110.001], [https://attack.mitre.org/techniques/T1114/ T1114], [https://attack.mitre.org/techniques/T1114.002/ T1114.002], [https://attack.mitre.org/techniques/T1114.003/ T1114.003], [https://attack.mitre.org/techniques/T1136.003/ T1136.003], [https://attack.mitre.org/techniques/T1556/ T1556], [https://attack.mitre.org/techniques/T1562.007/ T1562.007] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1110.001/ T1110.001], [https://attack.mitre.org/techniques/T1136.003/ T1136.003], [https://attack.mitre.org/techniques/T1562.007/ T1562.007], [https://attack.mitre.org/techniques/T1556/ T1556], [https://attack.mitre.org/techniques/T1110/ T1110], [https://attack.mitre.org/techniques/T1114/ T1114], [https://attack.mitre.org/techniques/T1114.003/ T1114.003], [https://attack.mitre.org/techniques/T1114.002/ T1114.002] * '''Last Updated''': 2020-12-16
@@ -3772,29 +3624,30 @@ This story is focused around detecting Office 365 Attacks. ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#High_Number_of_Login_Failures_from_a_single_source|High Number of Login Failures from a single source]] +* [[Documentation:ESSOC:detections:Detections#High_number_of_login_failures_from_a_single_source|High Number of Login Failures from a single source]] -* [[Documentation:ESSOC:detections:Detections#O365_Add_App_Role_Assignment_Grant_User|O365 Add App Role Assignment Grant User]] +* [[Documentation:ESSOC:detections:Detections#O365_add_app_role_assignment_grant_user|O365 Add App Role Assignment Grant User]] -* [[Documentation:ESSOC:detections:Detections#O365_Added_Service_Principal|O365 Added Service Principal]] +* [[Documentation:ESSOC:detections:Detections#O365_added_service_principal|O365 Added Service Principal]] -* [[Documentation:ESSOC:detections:Detections#O365_Bypass_MFA_via_Trusted_IP|O365 Bypass MFA via Trusted IP]] +* [[Documentation:ESSOC:detections:Detections#O365_bypass_mfa_via_trusted_ip|O365 Bypass MFA via Trusted IP]] -* [[Documentation:ESSOC:detections:Detections#O365_Disable_MFA|O365 Disable MFA]] +* [[Documentation:ESSOC:detections:Detections#O365_disable_mfa|O365 Disable MFA]] -* [[Documentation:ESSOC:detections:Detections#O365_Excessive_Authentication_Failures_Alert|O365 Excessive Authentication Failures Alert]] +* [[Documentation:ESSOC:detections:Detections#O365_excessive_authentication_failures_alert|O365 Excessive Authentication Failures Alert]] -* [[Documentation:ESSOC:detections:Detections#O365_Excessive_SSO_logon_errors|O365 Excessive SSO logon errors]] +* [[Documentation:ESSOC:detections:Detections#O365_excessive_sso_logon_errors|O365 Excessive SSO logon errors]] -* [[Documentation:ESSOC:detections:Detections#O365_New_Federated_Domain_Added|O365 New Federated Domain Added]] +* [[Documentation:ESSOC:detections:Detections#O365_new_federated_domain_added|O365 New Federated Domain Added]] -* [[Documentation:ESSOC:detections:Detections#O365_PST_export_alert|O365 PST export alert]] +* [[Documentation:ESSOC:detections:Detections#O365_pst_export_alert|O365 PST export alert]] -* [[Documentation:ESSOC:detections:Detections#O365_Suspicious_Admin_Email_Forwarding|O365 Suspicious Admin Email Forwarding]] +* [[Documentation:ESSOC:detections:Detections#O365_suspicious_admin_email_forwarding|O365 Suspicious Admin Email Forwarding]] -* [[Documentation:ESSOC:detections:Detections#O365_Suspicious_Rights_Delegation|O365 Suspicious Rights Delegation]] +* [[Documentation:ESSOC:detections:Detections#O365_suspicious_rights_delegation|O365 Suspicious Rights Delegation]] + +* [[Documentation:ESSOC:detections:Detections#O365_suspicious_user_email_forwarding|O365 Suspicious User Email Forwarding]] -* [[Documentation:ESSOC:detections:Detections#O365_Suspicious_User_Email_Forwarding|O365 Suspicious User Email Forwarding]] ====ATT&CK==== @@ -3836,6 +3689,7 @@ This story is focused around detecting Office 365 Attacks. | Collection |} + ====Kill Chain Phase==== * Actions on Objective @@ -3856,7 +3710,7 @@ This story is focused around detecting Office 365 Attacks. ---- -===Suspicious AWS EC2 Activities=== +===Suspicious aws ec2 activities=== Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3869,17 +3723,18 @@ Use the searches in this Analytic Story to monitor your AWS EC2 instances for ev ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Launched_by_User|Abnormally High AWS Instances Launched by User]] +* [[Documentation:ESSOC:detections:Detections#Abnormally_high_aws_instances_launched_by_user|Abnormally High AWS Instances Launched by User]] -* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Launched_by_User_-_MLTK|Abnormally High AWS Instances Launched by User - MLTK]] +* [[Documentation:ESSOC:detections:Detections#Abnormally_high_aws_instances_launched_by_user_-_mltk|Abnormally High AWS Instances Launched by User - MLTK]] -* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Terminated_by_User|Abnormally High AWS Instances Terminated by User]] +* [[Documentation:ESSOC:detections:Detections#Abnormally_high_aws_instances_terminated_by_user|Abnormally High AWS Instances Terminated by User]] -* [[Documentation:ESSOC:detections:Detections#Abnormally_High_AWS_Instances_Terminated_by_User_-_MLTK|Abnormally High AWS Instances Terminated by User - MLTK]] +* [[Documentation:ESSOC:detections:Detections#Abnormally_high_aws_instances_terminated_by_user_-_mltk|Abnormally High AWS Instances Terminated by User - MLTK]] -* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_In_Previously_Unseen_Region|EC2 Instance Started In Previously Unseen Region]] +* [[Documentation:ESSOC:detections:Detections#Ec2_instance_started_in_previously_unseen_region|EC2 Instance Started In Previously Unseen Region]] + +* [[Documentation:ESSOC:detections:Detections#Ec2_instance_started_with_previously_unseen_user|EC2 Instance Started With Previously Unseen User]] -* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Started_With_Previously_Unseen_User|EC2 Instance Started With Previously Unseen User]] ====ATT&CK==== @@ -3897,6 +3752,7 @@ Use the searches in this Analytic Story to monitor your AWS EC2 instances for ev | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -3913,12 +3769,12 @@ Use the searches in this Analytic Story to monitor your AWS EC2 instances for ev ---- -===Suspicious AWS Login Activities=== +===Suspicious aws login activities=== Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Authentication -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004], [https://attack.mitre.org/techniques/T1535/ T1535] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535], [https://attack.mitre.org/techniques/T1078.004/ T1078.004] * '''Last Updated''': 2019-05-01
@@ -3926,13 +3782,14 @@ Monitor your AWS authentication events using your CloudTrail logs. Searches with ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_City|Detect AWS Console Login by User from New City]] +* [[Documentation:ESSOC:detections:Detections#Detect_aws_console_login_by_user_from_new_city|Detect AWS Console Login by User from New City]] -* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_Country|Detect AWS Console Login by User from New Country]] +* [[Documentation:ESSOC:detections:Detections#Detect_aws_console_login_by_user_from_new_country|Detect AWS Console Login by User from New Country]] -* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_Region|Detect AWS Console Login by User from New Region]] +* [[Documentation:ESSOC:detections:Detections#Detect_aws_console_login_by_user_from_new_region|Detect AWS Console Login by User from New Region]] + +* [[Documentation:ESSOC:detections:Detections#Detect_new_user_aws_console_login|Detect new user AWS Console Login]] -* [[Documentation:ESSOC:detections:Detections#Detect_new_user_AWS_Console_Login|Detect new user AWS Console Login]] ====ATT&CK==== @@ -3950,6 +3807,7 @@ Monitor your AWS authentication events using your CloudTrail logs. Searches with | Defense Evasion, Initial Access, Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -3966,7 +3824,7 @@ Monitor your AWS authentication events using your CloudTrail logs. Searches with ---- -===Suspicious AWS S3 Activities=== +===Suspicious aws s3 activities=== Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -3979,25 +3837,16 @@ Use the searches in this Analytic Story to monitor your AWS S3 buckets for evide ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_New_Open_S3_Buckets_over_AWS_CLI|Detect New Open S3 Buckets over AWS CLI]] +* [[Documentation:ESSOC:detections:Detections#Detect_new_open_s3_buckets_over_aws_cli|Detect New Open S3 Buckets over AWS CLI]] -* [[Documentation:ESSOC:detections:Detections#Detect_New_Open_S3_buckets|Detect New Open S3 buckets]] +* [[Documentation:ESSOC:detections:Detections#Detect_new_open_s3_buckets|Detect New Open S3 buckets]] -* [[Documentation:ESSOC:detections:Detections#Detect_S3_access_from_a_new_IP|Detect S3 access from a new IP]] +* [[Documentation:ESSOC:detections:Detections#Detect_s3_access_from_a_new_ip|Detect S3 access from a new IP]] + +* [[Documentation:ESSOC:detections:Detections#Detect_spike_in_s3_bucket_deletion|Detect Spike in S3 Bucket deletion]] -* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_S3_Bucket_deletion|Detect Spike in S3 Bucket deletion]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} ====Kill Chain Phase==== @@ -4017,7 +3866,7 @@ Use the searches in this Analytic Story to monitor your AWS S3 buckets for evide ---- -===Suspicious AWS Traffic=== +===Suspicious aws traffic=== Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4030,15 +3879,10 @@ Leverage these searches to monitor your AWS network traffic for evidence of anom ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Spike_in_blocked_Outbound_Traffic_from_your_AWS|Detect Spike in blocked Outbound Traffic from your AWS]] +* [[Documentation:ESSOC:detections:Detections#Detect_spike_in_blocked_outbound_traffic_from_your_aws|Detect Spike in blocked Outbound Traffic from your AWS]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -4058,12 +3902,12 @@ Leverage these searches to monitor your AWS network traffic for evidence of anom ---- -===Suspicious Cloud Authentication Activities=== +===Suspicious cloud authentication activities=== Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Authentication -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1535/ T1535], [https://attack.mitre.org/techniques/T1078.004/ T1078.004] * '''Last Updated''': 2020-06-04
@@ -4071,15 +3915,16 @@ Monitor your cloud authentication events. Searches within this Analytic Story le ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#AWS_Cross_Account_Activity_From_Previously_Unseen_Account|AWS Cross Account Activity From Previously Unseen Account]] +* [[Documentation:ESSOC:detections:Detections#Aws_cross_account_activity_from_previously_unseen_account|AWS Cross Account Activity From Previously Unseen Account]] -* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_New_User|Detect AWS Console Login by New User]] +* [[Documentation:ESSOC:detections:Detections#Detect_aws_console_login_by_new_user|Detect AWS Console Login by New User]] -* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_City|Detect AWS Console Login by User from New City]] +* [[Documentation:ESSOC:detections:Detections#Detect_aws_console_login_by_user_from_new_city|Detect AWS Console Login by User from New City]] -* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_Country|Detect AWS Console Login by User from New Country]] +* [[Documentation:ESSOC:detections:Detections#Detect_aws_console_login_by_user_from_new_country|Detect AWS Console Login by User from New Country]] + +* [[Documentation:ESSOC:detections:Detections#Detect_aws_console_login_by_user_from_new_region|Detect AWS Console Login by User from New Region]] -* [[Documentation:ESSOC:detections:Detections#Detect_AWS_Console_Login_by_User_from_New_Region|Detect AWS Console Login by User from New Region]] ====ATT&CK==== @@ -4097,6 +3942,7 @@ Monitor your cloud authentication events. Searches within this Analytic Story le | Defense Evasion, Initial Access, Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -4115,7 +3961,7 @@ Monitor your cloud authentication events. Searches within this Analytic Story le ---- -===Suspicious Cloud Instance Activities=== +===Suspicious cloud instance activities=== Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4128,23 +3974,14 @@ Monitor your cloud infrastructure provisioning activities for behaviors originat ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Abnormally_High_Number_Of_Cloud_Instances_Destroyed|Abnormally High Number Of Cloud Instances Destroyed]] +* [[Documentation:ESSOC:detections:Detections#Abnormally_high_number_of_cloud_instances_destroyed|Abnormally High Number Of Cloud Instances Destroyed]] -* [[Documentation:ESSOC:detections:Detections#Abnormally_High_Number_Of_Cloud_Instances_Launched|Abnormally High Number Of Cloud Instances Launched]] +* [[Documentation:ESSOC:detections:Detections#Abnormally_high_number_of_cloud_instances_launched|Abnormally High Number Of Cloud Instances Launched]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_instance_modified_by_previously_unseen_user|Cloud Instance Modified By Previously Unseen User]] -* [[Documentation:ESSOC:detections:Detections#Cloud_Instance_Modified_By_Previously_Unseen_User|Cloud Instance Modified By Previously Unseen User]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} ====Kill Chain Phase==== @@ -4162,7 +3999,7 @@ Monitor your cloud infrastructure provisioning activities for behaviors originat ---- -===Suspicious Cloud Provisioning Activities=== +===Suspicious cloud provisioning activities=== Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4175,25 +4012,16 @@ Monitor your cloud infrastructure provisioning activities for behaviors originat ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Cloud_Provisioning_Activity_From_Previously_Unseen_City|Cloud Provisioning Activity From Previously Unseen City]] +* [[Documentation:ESSOC:detections:Detections#Cloud_provisioning_activity_from_previously_unseen_city|Cloud Provisioning Activity From Previously Unseen City]] -* [[Documentation:ESSOC:detections:Detections#Cloud_Provisioning_Activity_From_Previously_Unseen_Country|Cloud Provisioning Activity From Previously Unseen Country]] +* [[Documentation:ESSOC:detections:Detections#Cloud_provisioning_activity_from_previously_unseen_country|Cloud Provisioning Activity From Previously Unseen Country]] -* [[Documentation:ESSOC:detections:Detections#Cloud_Provisioning_Activity_From_Previously_Unseen_IP_Address|Cloud Provisioning Activity From Previously Unseen IP Address]] +* [[Documentation:ESSOC:detections:Detections#Cloud_provisioning_activity_from_previously_unseen_ip_address|Cloud Provisioning Activity From Previously Unseen IP Address]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_provisioning_activity_from_previously_unseen_region|Cloud Provisioning Activity From Previously Unseen Region]] -* [[Documentation:ESSOC:detections:Detections#Cloud_Provisioning_Activity_From_Previously_Unseen_Region|Cloud Provisioning Activity From Previously Unseen Region]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078 -| Valid Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} ====Kill Chain Phase==== @@ -4209,12 +4037,12 @@ Monitor your cloud infrastructure provisioning activities for behaviors originat ---- -===Suspicious Cloud User Activities=== +===Suspicious cloud user activities=== Detect and investigate suspicious activities by users and roles in your cloud environments. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Change -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078/ T1078], [https://attack.mitre.org/techniques/T1078.004/ T1078.004] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1078.004/ T1078.004], [https://attack.mitre.org/techniques/T1078/ T1078] * '''Last Updated''': 2020-09-04
@@ -4222,11 +4050,12 @@ Detect and investigate suspicious activities by users and roles in your cloud en ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Abnormally_High_Number_Of_Cloud_Infrastructure_API_Calls|Abnormally High Number Of Cloud Infrastructure API Calls]] +* [[Documentation:ESSOC:detections:Detections#Abnormally_high_number_of_cloud_infrastructure_api_calls|Abnormally High Number Of Cloud Infrastructure API Calls]] -* [[Documentation:ESSOC:detections:Detections#Abnormally_High_Number_Of_Cloud_Security_Group_API_Calls|Abnormally High Number Of Cloud Security Group API Calls]] +* [[Documentation:ESSOC:detections:Detections#Abnormally_high_number_of_cloud_security_group_api_calls|Abnormally High Number Of Cloud Security Group API Calls]] + +* [[Documentation:ESSOC:detections:Detections#Cloud_api_calls_from_previously_unseen_user_roles|Cloud API Calls From Previously Unseen User Roles]] -* [[Documentation:ESSOC:detections:Detections#Cloud_API_Calls_From_Previously_Unseen_User_Roles|Cloud API Calls From Previously Unseen User Roles]] ====ATT&CK==== @@ -4244,6 +4073,7 @@ Detect and investigate suspicious activities by users and roles in your cloud en | Defense Evasion, Initial Access, Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -4262,7 +4092,7 @@ Detect and investigate suspicious activities by users and roles in your cloud en ---- -===Suspicious GCP Storage Activities=== +===Suspicious gcp storage activities=== Use the searches in this Analytic Story to monitor your GCP Storage buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open storage buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4275,21 +4105,12 @@ Use the searches in this Analytic Story to monitor your GCP Storage buckets for ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_GCP_Storage_access_from_a_new_IP|Detect GCP Storage access from a new IP]] +* [[Documentation:ESSOC:detections:Detections#Detect_gcp_storage_access_from_a_new_ip|Detect GCP Storage access from a new IP]] + +* [[Documentation:ESSOC:detections:Detections#Detect_new_open_gcp_storage_buckets|Detect New Open GCP Storage Buckets]] -* [[Documentation:ESSOC:detections:Detections#Detect_New_Open_GCP_Storage_Buckets|Detect New Open GCP Storage Buckets]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1530 -| Data from Cloud Storage Object -| Collection -|} ====Kill Chain Phase==== @@ -4309,7 +4130,7 @@ Use the searches in this Analytic Story to monitor your GCP Storage buckets for ---- -===Unusual AWS EC2 Modifications=== +===Unusual aws ec2 modifications=== Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4322,19 +4143,10 @@ Identify unusual changes to your AWS EC2 instances that may indicate malicious a ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#EC2_Instance_Modified_With_Previously_Unseen_User|EC2 Instance Modified With Previously Unseen User]] +* [[Documentation:ESSOC:detections:Detections#Ec2_instance_modified_with_previously_unseen_user|EC2 Instance Modified With Previously Unseen User]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1078.004 -| Cloud Accounts -| Defense Evasion, Initial Access, Persistence, Privilege Escalation -|} ====Kill Chain Phase==== @@ -4355,7 +4167,7 @@ Identify unusual changes to your AWS EC2 instances that may indicate malicious a ==Malware== -===ColdRoot MacOS RAT=== +===Coldroot macos rat=== Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4368,17 +4180,12 @@ Leverage searches that allow you to detect and investigate unusual activities th ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Osquery_pack_-_ColdRoot_detection|Osquery pack - ColdRoot detection]] +* [[Documentation:ESSOC:detections:Detections#Osquery_pack_-_coldroot_detection|Osquery pack - ColdRoot detection]] + +* [[Documentation:ESSOC:detections:Detections#Processes_tapping_keyboard_events|Processes Tapping Keyboard Events]] -* [[Documentation:ESSOC:detections:Detections#Processes_Tapping_Keyboard_Events|Processes Tapping Keyboard Events]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -4402,12 +4209,12 @@ Leverage searches that allow you to detect and investigate unusual activities th ---- -===DHS Report TA18-074A=== +===Dhs report ta18-074a=== Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint, Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1071.002/ T1071.002], [https://attack.mitre.org/techniques/T1112/ T1112], [https://attack.mitre.org/techniques/T1136.001/ T1136.001], [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1562.004/ T1562.004] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1136.001/ T1136.001], [https://attack.mitre.org/techniques/T1071.002/ T1071.002], [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1562.004/ T1562.004], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1112/ T1112] * '''Last Updated''': 2020-01-22
@@ -4417,31 +4224,32 @@ Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA * [[Documentation:ESSOC:detections:Detections#Create_local_admin_accounts_using_net_exe|Create local admin accounts using net exe]] -* [[Documentation:ESSOC:detections:Detections#Detect_New_Local_Admin_account|Detect New Local Admin account]] +* [[Documentation:ESSOC:detections:Detections#Detect_new_local_admin_account|Detect New Local Admin account]] -* [[Documentation:ESSOC:detections:Detections#Detect_Outbound_SMB_Traffic|Detect Outbound SMB Traffic]] +* [[Documentation:ESSOC:detections:Detections#Detect_outbound_smb_traffic|Detect Outbound SMB Traffic]] -* [[Documentation:ESSOC:detections:Detections#Detect_PsExec_With_accepteula_Flag|Detect PsExec With accepteula Flag]] +* [[Documentation:ESSOC:detections:Detections#Detect_psexec_with_accepteula_flag|Detect PsExec With accepteula Flag]] * [[Documentation:ESSOC:detections:Detections#First_time_seen_command_line_argument|First time seen command line argument]] -* [[Documentation:ESSOC:detections:Detections#Malicious_PowerShell_Process_-_Execution_Policy_Bypass|Malicious PowerShell Process - Execution Policy Bypass]] +* [[Documentation:ESSOC:detections:Detections#Malicious_powershell_process_-_execution_policy_bypass|Malicious PowerShell Process - Execution Policy Bypass]] * [[Documentation:ESSOC:detections:Detections#Processes_launching_netsh|Processes launching netsh]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_used_for_persistence|Registry Keys Used For Persistence]] -* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike|SMB Traffic Spike]] +* [[Documentation:ESSOC:detections:Detections#Smb_traffic_spike|SMB Traffic Spike]] -* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike_-_MLTK|SMB Traffic Spike - MLTK]] +* [[Documentation:ESSOC:detections:Detections#Smb_traffic_spike_-_mltk|SMB Traffic Spike - MLTK]] -* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] +* [[Documentation:ESSOC:detections:Detections#Sc_exe_manipulating_windows_services|Sc exe Manipulating Windows Services]] -* [[Documentation:ESSOC:detections:Detections#Scheduled_Task_Deleted_Or_Created_via_CMD|Scheduled Task Deleted Or Created via CMD]] +* [[Documentation:ESSOC:detections:Detections#Scheduled_task_deleted_or_created_via_cmd|Scheduled Task Deleted Or Created via CMD]] -* [[Documentation:ESSOC:detections:Detections#Single_Letter_Process_On_Endpoint|Single Letter Process On Endpoint]] +* [[Documentation:ESSOC:detections:Detections#Single_letter_process_on_endpoint|Single Letter Process On Endpoint]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_reg_exe_process|Suspicious Reg exe Process]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Reg_exe_Process|Suspicious Reg exe Process]] ====ATT&CK==== @@ -4495,6 +4303,7 @@ Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA | Defense Evasion |} + ====Kill Chain Phase==== * Actions on Objectives @@ -4515,12 +4324,12 @@ Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA ---- -===Dynamic DNS=== +===Dynamic dns=== Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and deny lists. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Network_Resolution, Web -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1189/ T1189] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1189/ T1189], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1048/ T1048] * '''Last Updated''': 2018-09-06
@@ -4533,6 +4342,7 @@ Detect and investigate hosts in your environment that may be communicating with * [[Documentation:ESSOC:detections:Detections#Detect_web_traffic_to_dynamic_domain_providers|Detect web traffic to dynamic domain providers]] + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -4556,6 +4366,7 @@ Detect and investigate hosts in your environment that may be communicating with | Exfiltration |} + ====Kill Chain Phase==== * Actions on Objectives @@ -4580,12 +4391,12 @@ Detect and investigate hosts in your environment that may be communicating with ---- -===Emotet Malware DHS Report TA18-201A === +===Emotet malware dhs report ta18-201a === Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Email, Endpoint, Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1072/ T1072], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1566.001/ T1566.001] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1072/ T1072], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1566.001/ T1566.001] * '''Last Updated''': 2020-01-27
@@ -4593,23 +4404,24 @@ Detect rarely used executables, specific registry paths that may confer malware ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Rare_Executables|Detect Rare Executables]] +* [[Documentation:ESSOC:detections:Detections#Detect_rare_executables|Detect Rare Executables]] -* [[Documentation:ESSOC:detections:Detections#Detect_Use_of_cmd_exe_to_Launch_Script_Interpreters|Detect Use of cmd exe to Launch Script Interpreters]] +* [[Documentation:ESSOC:detections:Detections#Detect_use_of_cmd_exe_to_launch_script_interpreters|Detect Use of cmd exe to Launch Script Interpreters]] -* [[Documentation:ESSOC:detections:Detections#Detection_of_tools_built_by_NirSoft|Detection of tools built by NirSoft]] +* [[Documentation:ESSOC:detections:Detections#Detection_of_tools_built_by_nirsoft|Detection of tools built by NirSoft]] -* [[Documentation:ESSOC:detections:Detections#Email_Attachments_With_Lots_Of_Spaces|Email Attachments With Lots Of Spaces]] +* [[Documentation:ESSOC:detections:Detections#Email_attachments_with_lots_of_spaces|Email Attachments With Lots Of Spaces]] -* [[Documentation:ESSOC:detections:Detections#Prohibited_Software_On_Endpoint|Prohibited Software On Endpoint]] +* [[Documentation:ESSOC:detections:Detections#Prohibited_software_on_endpoint|Prohibited Software On Endpoint]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_used_for_persistence|Registry Keys Used For Persistence]] -* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike|SMB Traffic Spike]] +* [[Documentation:ESSOC:detections:Detections#Smb_traffic_spike|SMB Traffic Spike]] -* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike_-_MLTK|SMB Traffic Spike - MLTK]] +* [[Documentation:ESSOC:detections:Detections#Smb_traffic_spike_-_mltk|SMB Traffic Spike - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_email_attachment_extensions|Suspicious Email Attachment Extensions]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Email_Attachment_Extensions|Suspicious Email Attachment Extensions]] ====ATT&CK==== @@ -4639,6 +4451,7 @@ Detect rarely used executables, specific registry paths that may confer malware | Initial Access |} + ====Kill Chain Phase==== * Actions on Objectives @@ -4667,12 +4480,12 @@ Detect rarely used executables, specific registry paths that may confer malware ---- -===Hidden Cobra Malware=== +===Hidden cobra malware=== Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint, Network_Resolution, Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1070.005/ T1070.005], [https://attack.mitre.org/techniques/T1071.002/ T1071.002], [https://attack.mitre.org/techniques/T1071.004/ T1071.004] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1070.005/ T1070.005], [https://attack.mitre.org/techniques/T1071.004/ T1071.004], [https://attack.mitre.org/techniques/T1048.003/ T1048.003], [https://attack.mitre.org/techniques/T1071.002/ T1071.002], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1021.002/ T1021.002] * '''Last Updated''': 2020-01-22
@@ -4682,23 +4495,24 @@ Monitor for and investigate activities, including the creation or deletion of hi * [[Documentation:ESSOC:detections:Detections#Create_or_delete_windows_shares_using_net_exe|Create or delete windows shares using net exe]] -* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_Outliers_-_MLTK|DNS Query Length Outliers - MLTK]] +* [[Documentation:ESSOC:detections:Detections#Dns_query_length_outliers_-_mltk|DNS Query Length Outliers - MLTK]] -* [[Documentation:ESSOC:detections:Detections#DNS_Query_Length_With_High_Standard_Deviation|DNS Query Length With High Standard Deviation]] +* [[Documentation:ESSOC:detections:Detections#Dns_query_length_with_high_standard_deviation|DNS Query Length With High Standard Deviation]] -* [[Documentation:ESSOC:detections:Detections#Detect_Outbound_SMB_Traffic|Detect Outbound SMB Traffic]] +* [[Documentation:ESSOC:detections:Detections#Detect_outbound_smb_traffic|Detect Outbound SMB Traffic]] * [[Documentation:ESSOC:detections:Detections#First_time_seen_command_line_argument|First time seen command line argument]] -* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Traffic|Remote Desktop Network Traffic]] +* [[Documentation:ESSOC:detections:Detections#Remote_desktop_network_traffic|Remote Desktop Network Traffic]] -* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Process_Running_On_System|Remote Desktop Process Running On System]] +* [[Documentation:ESSOC:detections:Detections#Remote_desktop_process_running_on_system|Remote Desktop Process Running On System]] -* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike|SMB Traffic Spike]] +* [[Documentation:ESSOC:detections:Detections#Smb_traffic_spike|SMB Traffic Spike]] -* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike_-_MLTK|SMB Traffic Spike - MLTK]] +* [[Documentation:ESSOC:detections:Detections#Smb_traffic_spike_-_mltk|SMB Traffic Spike - MLTK]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_file_write|Suspicious File Write]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_File_Write|Suspicious File Write]] ====ATT&CK==== @@ -4740,6 +4554,7 @@ Monitor for and investigate activities, including the creation or deletion of hi | Lateral Movement |} + ====Kill Chain Phase==== * Actions on Objectives @@ -4760,12 +4575,12 @@ Monitor for and investigate activities, including the creation or deletion of hi ---- -===Orangeworm Attack Group=== +===Orangeworm attack group=== Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1569.002/ T1569.002] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1569.002/ T1569.002], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1574.011/ T1574.011], [https://attack.mitre.org/techniques/T1543.003/ T1543.003] * '''Last Updated''': 2020-01-22
@@ -4773,11 +4588,12 @@ Detect activities and various techniques associated with the Orangeworm Attack G ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#First_Time_Seen_Running_Windows_Service|First Time Seen Running Windows Service]] +* [[Documentation:ESSOC:detections:Detections#First_time_seen_running_windows_service|First Time Seen Running Windows Service]] * [[Documentation:ESSOC:detections:Detections#First_time_seen_command_line_argument|First time seen command line argument]] -* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] +* [[Documentation:ESSOC:detections:Detections#Sc_exe_manipulating_windows_services|Sc exe Manipulating Windows Services]] + ====ATT&CK==== @@ -4807,6 +4623,7 @@ Detect activities and various techniques associated with the Orangeworm Attack G | Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -4834,7 +4651,7 @@ Leverage searches that allow you to detect and investigate unusual activities th * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint, Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1047/ T1047], [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1070/ T1070], [https://attack.mitre.org/techniques/T1070.001/ T1070.001], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1485/ T1485], [https://attack.mitre.org/techniques/T1490/ T1490], [https://attack.mitre.org/techniques/T1547.001/ T1547.001] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1490/ T1490], [https://attack.mitre.org/techniques/T1485/ T1485], [https://attack.mitre.org/techniques/T1482/ T1482], [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1047/ T1047], [https://attack.mitre.org/techniques/T1486/ T1486], [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1070.001/ T1070.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1070/ T1070], [https://attack.mitre.org/techniques/T1562.001/ T1562.001], [https://attack.mitre.org/techniques/T1489/ T1489], [https://attack.mitre.org/techniques/T1059.003/ T1059.003] * '''Last Updated''': 2020-02-04
@@ -4842,45 +4659,46 @@ Leverage searches that allow you to detect and investigate unusual activities th ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#BCDEdit_Failure_Recovery_Modification|BCDEdit Failure Recovery Modification]] +* [[Documentation:ESSOC:detections:Detections#Bcdedit_failure_recovery_modification|BCDEdit Failure Recovery Modification]] -* [[Documentation:ESSOC:detections:Detections#Common_Ransomware_Extensions|Common Ransomware Extensions]] +* [[Documentation:ESSOC:detections:Detections#Common_ransomware_extensions|Common Ransomware Extensions]] -* [[Documentation:ESSOC:detections:Detections#Common_Ransomware_Notes|Common Ransomware Notes]] +* [[Documentation:ESSOC:detections:Detections#Common_ransomware_notes|Common Ransomware Notes]] -* [[Documentation:ESSOC:detections:Detections#Deleting_Shadow_Copies|Deleting Shadow Copies]] +* [[Documentation:ESSOC:detections:Detections#Deleting_shadow_copies|Deleting Shadow Copies]] -* [[Documentation:ESSOC:detections:Detections#Prohibited_Network_Traffic_Allowed|Prohibited Network Traffic Allowed]] +* [[Documentation:ESSOC:detections:Detections#Prohibited_network_traffic_allowed|Prohibited Network Traffic Allowed]] -* [[Documentation:ESSOC:detections:Detections#Registry_Keys_Used_For_Persistence|Registry Keys Used For Persistence]] +* [[Documentation:ESSOC:detections:Detections#Registry_keys_used_for_persistence|Registry Keys Used For Persistence]] -* [[Documentation:ESSOC:detections:Detections#Remote_Process_Instantiation_via_WMI|Remote Process Instantiation via WMI]] +* [[Documentation:ESSOC:detections:Detections#Remote_process_instantiation_via_wmi|Remote Process Instantiation via WMI]] -* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike|SMB Traffic Spike]] +* [[Documentation:ESSOC:detections:Detections#Smb_traffic_spike|SMB Traffic Spike]] -* [[Documentation:ESSOC:detections:Detections#SMB_Traffic_Spike_-_MLTK|SMB Traffic Spike - MLTK]] +* [[Documentation:ESSOC:detections:Detections#Smb_traffic_spike_-_mltk|SMB Traffic Spike - MLTK]] -* [[Documentation:ESSOC:detections:Detections#Scheduled_tasks_used_in_BadRabbit_ransomware|Scheduled tasks used in BadRabbit ransomware]] +* [[Documentation:ESSOC:detections:Detections#Scheduled_tasks_used_in_badrabbit_ransomware|Scheduled tasks used in BadRabbit ransomware]] * [[Documentation:ESSOC:detections:Detections#Schtasks_used_for_forcing_a_reboot|Schtasks used for forcing a reboot]] -* [[Documentation:ESSOC:detections:Detections#Spike_in_File_Writes|Spike in File Writes]] +* [[Documentation:ESSOC:detections:Detections#Spike_in_file_writes|Spike in File Writes]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_wevtutil_Usage|Suspicious wevtutil Usage]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_wevtutil_usage|Suspicious wevtutil Usage]] -* [[Documentation:ESSOC:detections:Detections#System_Processes_Run_From_Unexpected_Locations|System Processes Run From Unexpected Locations]] +* [[Documentation:ESSOC:detections:Detections#System_processes_run_from_unexpected_locations|System Processes Run From Unexpected Locations]] -* [[Documentation:ESSOC:detections:Detections#TOR_Traffic|TOR Traffic]] +* [[Documentation:ESSOC:detections:Detections#Tor_traffic|TOR Traffic]] -* [[Documentation:ESSOC:detections:Detections#USN_Journal_Deletion|USN Journal Deletion]] +* [[Documentation:ESSOC:detections:Detections#Usn_journal_deletion|USN Journal Deletion]] -* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line|Unusually Long Command Line]] +* [[Documentation:ESSOC:detections:Detections#Unusually_long_command_line|Unusually Long Command Line]] -* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line_-_MLTK|Unusually Long Command Line - MLTK]] +* [[Documentation:ESSOC:detections:Detections#Unusually_long_command_line_-_mltk|Unusually Long Command Line - MLTK]] -* [[Documentation:ESSOC:detections:Detections#WBAdmin_Delete_System_Backups|WBAdmin Delete System Backups]] +* [[Documentation:ESSOC:detections:Detections#Wbadmin_delete_system_backups|WBAdmin Delete System Backups]] + +* [[Documentation:ESSOC:detections:Detections#Windows_event_log_cleared|Windows Event Log Cleared]] -* [[Documentation:ESSOC:detections:Detections#Windows_Event_Log_Cleared|Windows Event Log Cleared]] ====ATT&CK==== @@ -4958,6 +4776,7 @@ Leverage searches that allow you to detect and investigate unusual activities th | Execution |} + ====Kill Chain Phase==== * Actions on Objectives @@ -4980,7 +4799,7 @@ Leverage searches that allow you to detect and investigate unusual activities th ---- -===Ransomware Cloud=== +===Ransomware cloud=== Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware. These searches include cloud related objects that may be targeted by malicious actors via cloud providers own encryption features. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -4993,21 +4812,12 @@ Leverage searches that allow you to detect and investigate unusual activities th ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#AWS_Detect_Users_creating_keys_with_encrypt_policy_without_MFA|AWS Detect Users creating keys with encrypt policy without MFA]] +* [[Documentation:ESSOC:detections:Detections#Aws_detect_users_creating_keys_with_encrypt_policy_without_mfa|AWS Detect Users creating keys with encrypt policy without MFA]] + +* [[Documentation:ESSOC:detections:Detections#Aws_detect_users_with_kms_keys_performing_encryption_s3|AWS Detect Users with KMS keys performing encryption S3]] -* [[Documentation:ESSOC:detections:Detections#AWS_Detect_Users_with_KMS_keys_performing_encryption_S3|AWS Detect Users with KMS keys performing encryption S3]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1486 -| Data Encrypted for Impact -| Impact -|} ====Kill Chain Phase==== @@ -5027,12 +4837,12 @@ Leverage searches that allow you to detect and investigate unusual activities th ---- -===Ryuk Ransomware=== +===Ryuk ransomware=== Leverage searches that allow you to detect and investigate unusual activities that might relate to the Ryuk ransomware, including looking for file writes associated with Ryuk, Stopping Security Access Manager, DisableAntiSpyware registry key modification, suspicious psexec use, and more. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint, Network_Traffic -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1482/ T1482], [https://attack.mitre.org/techniques/T1485/ T1485], [https://attack.mitre.org/techniques/T1486/ T1486], [https://attack.mitre.org/techniques/T1489/ T1489], [https://attack.mitre.org/techniques/T1490/ T1490], [https://attack.mitre.org/techniques/T1562.001/ T1562.001] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1490/ T1490], [https://attack.mitre.org/techniques/T1485/ T1485], [https://attack.mitre.org/techniques/T1482/ T1482], [https://attack.mitre.org/techniques/T1048/ T1048], [https://attack.mitre.org/techniques/T1547.001/ T1547.001], [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1047/ T1047], [https://attack.mitre.org/techniques/T1486/ T1486], [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1053.005/ T1053.005], [https://attack.mitre.org/techniques/T1070.001/ T1070.001], [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1071.001/ T1071.001], [https://attack.mitre.org/techniques/T1070/ T1070], [https://attack.mitre.org/techniques/T1562.001/ T1562.001], [https://attack.mitre.org/techniques/T1489/ T1489], [https://attack.mitre.org/techniques/T1059.003/ T1059.003] * '''Last Updated''': 2020-11-06
@@ -5040,29 +4850,30 @@ Leverage searches that allow you to detect and investigate unusual activities th ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#BCDEdit_Failure_Recovery_Modification|BCDEdit Failure Recovery Modification]] +* [[Documentation:ESSOC:detections:Detections#Bcdedit_failure_recovery_modification|BCDEdit Failure Recovery Modification]] -* [[Documentation:ESSOC:detections:Detections#Common_Ransomware_Notes|Common Ransomware Notes]] +* [[Documentation:ESSOC:detections:Detections#Common_ransomware_notes|Common Ransomware Notes]] -* [[Documentation:ESSOC:detections:Detections#NLTest_Domain_Trust_Discovery|NLTest Domain Trust Discovery]] +* [[Documentation:ESSOC:detections:Detections#Nltest_domain_trust_discovery|NLTest Domain Trust Discovery]] -* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Bruteforce|Remote Desktop Network Bruteforce]] +* [[Documentation:ESSOC:detections:Detections#Remote_desktop_network_bruteforce|Remote Desktop Network Bruteforce]] -* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Traffic|Remote Desktop Network Traffic]] +* [[Documentation:ESSOC:detections:Detections#Remote_desktop_network_traffic|Remote Desktop Network Traffic]] -* [[Documentation:ESSOC:detections:Detections#Ryuk_Test_Files_Detected|Ryuk Test Files Detected]] +* [[Documentation:ESSOC:detections:Detections#Ryuk_test_files_detected|Ryuk Test Files Detected]] -* [[Documentation:ESSOC:detections:Detections#Spike_in_File_Writes|Spike in File Writes]] +* [[Documentation:ESSOC:detections:Detections#Spike_in_file_writes|Spike in File Writes]] -* [[Documentation:ESSOC:detections:Detections#WBAdmin_Delete_System_Backups|WBAdmin Delete System Backups]] +* [[Documentation:ESSOC:detections:Detections#Wbadmin_delete_system_backups|WBAdmin Delete System Backups]] -* [[Documentation:ESSOC:detections:Detections#Windows_DisableAntiSpyware_Registry|Windows DisableAntiSpyware Registry]] +* [[Documentation:ESSOC:detections:Detections#Windows_disableantispyware_registry|Windows DisableAntiSpyware Registry]] -* [[Documentation:ESSOC:detections:Detections#Windows_Security_Account_Manager_Stopped|Windows Security Account Manager Stopped]] +* [[Documentation:ESSOC:detections:Detections#Windows_security_account_manager_stopped|Windows Security Account Manager Stopped]] * [[Documentation:ESSOC:detections:Detections#Windows_connhost_exe_started_forcefully|Windows connhost exe started forcefully]] + ====ATT&CK==== {| ! style="text-align:left;"| ID @@ -5138,6 +4949,7 @@ Leverage searches that allow you to detect and investigate unusual activities th | Execution |} + ====Kill Chain Phase==== * Actions on Objectives @@ -5164,12 +4976,12 @@ Leverage searches that allow you to detect and investigate unusual activities th ---- -===SamSam Ransomware=== +===Samsam ransomware=== Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint, Network_Traffic, Web -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1082/ T1082], [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1485/ T1485], [https://attack.mitre.org/techniques/T1486/ T1486], [https://attack.mitre.org/techniques/T1490/ T1490] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1485/ T1485], [https://attack.mitre.org/techniques/T1490/ T1490], [https://attack.mitre.org/techniques/T1021.002/ T1021.002], [https://attack.mitre.org/techniques/T1082/ T1082], [https://attack.mitre.org/techniques/T1021.001/ T1021.001], [https://attack.mitre.org/techniques/T1486/ T1486] * '''Last Updated''': 2018-12-13
@@ -5177,31 +4989,32 @@ Leverage searches that allow you to detect and investigate unusual activities th ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Batch_File_Write_to_System32|Batch File Write to System32]] +* [[Documentation:ESSOC:detections:Detections#Batch_file_write_to_system32|Batch File Write to System32]] -* [[Documentation:ESSOC:detections:Detections#Common_Ransomware_Extensions|Common Ransomware Extensions]] +* [[Documentation:ESSOC:detections:Detections#Common_ransomware_extensions|Common Ransomware Extensions]] -* [[Documentation:ESSOC:detections:Detections#Common_Ransomware_Notes|Common Ransomware Notes]] +* [[Documentation:ESSOC:detections:Detections#Common_ransomware_notes|Common Ransomware Notes]] -* [[Documentation:ESSOC:detections:Detections#Deleting_Shadow_Copies|Deleting Shadow Copies]] +* [[Documentation:ESSOC:detections:Detections#Deleting_shadow_copies|Deleting Shadow Copies]] -* [[Documentation:ESSOC:detections:Detections#Detect_PsExec_With_accepteula_Flag|Detect PsExec With accepteula Flag]] +* [[Documentation:ESSOC:detections:Detections#Detect_psexec_with_accepteula_flag|Detect PsExec With accepteula Flag]] -* [[Documentation:ESSOC:detections:Detections#Detect_attackers_scanning_for_vulnerable_JBoss_servers|Detect attackers scanning for vulnerable JBoss servers]] +* [[Documentation:ESSOC:detections:Detections#Detect_attackers_scanning_for_vulnerable_jboss_servers|Detect attackers scanning for vulnerable JBoss servers]] -* [[Documentation:ESSOC:detections:Detections#Detect_malicious_requests_to_exploit_JBoss_servers|Detect malicious requests to exploit JBoss servers]] +* [[Documentation:ESSOC:detections:Detections#Detect_malicious_requests_to_exploit_jboss_servers|Detect malicious requests to exploit JBoss servers]] -* [[Documentation:ESSOC:detections:Detections#File_with_Samsam_Extension|File with Samsam Extension]] +* [[Documentation:ESSOC:detections:Detections#File_with_samsam_extension|File with Samsam Extension]] -* [[Documentation:ESSOC:detections:Detections#Prohibited_Software_On_Endpoint|Prohibited Software On Endpoint]] +* [[Documentation:ESSOC:detections:Detections#Prohibited_software_on_endpoint|Prohibited Software On Endpoint]] -* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Bruteforce|Remote Desktop Network Bruteforce]] +* [[Documentation:ESSOC:detections:Detections#Remote_desktop_network_bruteforce|Remote Desktop Network Bruteforce]] -* [[Documentation:ESSOC:detections:Detections#Remote_Desktop_Network_Traffic|Remote Desktop Network Traffic]] +* [[Documentation:ESSOC:detections:Detections#Remote_desktop_network_traffic|Remote Desktop Network Traffic]] -* [[Documentation:ESSOC:detections:Detections#Samsam_Test_File_Write|Samsam Test File Write]] +* [[Documentation:ESSOC:detections:Detections#Samsam_test_file_write|Samsam Test File Write]] + +* [[Documentation:ESSOC:detections:Detections#Spike_in_file_writes|Spike in File Writes]] -* [[Documentation:ESSOC:detections:Detections#Spike_in_File_Writes|Spike in File Writes]] ====ATT&CK==== @@ -5239,6 +5052,7 @@ Leverage searches that allow you to detect and investigate unusual activities th | Impact |} + ====Kill Chain Phase==== * Actions on Objectives @@ -5267,12 +5081,12 @@ Leverage searches that allow you to detect and investigate unusual activities th ---- -===Unusual Processes=== +===Unusual processes=== Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1016/ T1016], [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1204.002/ T1204.002], [https://attack.mitre.org/techniques/T1218.011/ T1218.011] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1016/ T1016], [https://attack.mitre.org/techniques/T1218.011/ T1218.011], [https://attack.mitre.org/techniques/T1036.003/ T1036.003], [https://attack.mitre.org/techniques/T1204.002/ T1204.002] * '''Last Updated''': 2020-02-04
@@ -5280,19 +5094,20 @@ Quickly identify systems running new or unusual processes in your environment th ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_Rare_Executables|Detect Rare Executables]] +* [[Documentation:ESSOC:detections:Detections#Detect_rare_executables|Detect Rare Executables]] -* [[Documentation:ESSOC:detections:Detections#Detect_processes_used_for_System_Network_Configuration_Discovery|Detect processes used for System Network Configuration Discovery]] +* [[Documentation:ESSOC:detections:Detections#Detect_processes_used_for_system_network_configuration_discovery|Detect processes used for System Network Configuration Discovery]] -* [[Documentation:ESSOC:detections:Detections#RunDLL_Loading_DLL_By_Ordinal|RunDLL Loading DLL By Ordinal]] +* [[Documentation:ESSOC:detections:Detections#Rundll_loading_dll_by_ordinal|RunDLL Loading DLL By Ordinal]] -* [[Documentation:ESSOC:detections:Detections#System_Processes_Run_From_Unexpected_Locations|System Processes Run From Unexpected Locations]] +* [[Documentation:ESSOC:detections:Detections#System_processes_run_from_unexpected_locations|System Processes Run From Unexpected Locations]] -* [[Documentation:ESSOC:detections:Detections#Uncommon_Processes_On_Endpoint|Uncommon Processes On Endpoint]] +* [[Documentation:ESSOC:detections:Detections#Uncommon_processes_on_endpoint|Uncommon Processes On Endpoint]] -* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line|Unusually Long Command Line]] +* [[Documentation:ESSOC:detections:Detections#Unusually_long_command_line|Unusually Long Command Line]] + +* [[Documentation:ESSOC:detections:Detections#Unusually_long_command_line_-_mltk|Unusually Long Command Line - MLTK]] -* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Command_Line_-_MLTK|Unusually Long Command Line - MLTK]] ====ATT&CK==== @@ -5318,6 +5133,7 @@ Quickly identify systems running new or unusual processes in your environment th | Execution |} + ====Kill Chain Phase==== * Actions on Objectives @@ -5342,7 +5158,7 @@ Quickly identify systems running new or unusual processes in your environment th ---- -===Windows File Extension and Association Abuse=== +===Windows file extension and association abuse=== Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5355,11 +5171,12 @@ Detect and investigate suspected abuse of file extensions and Windows file assoc ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Execution_of_File_With_Spaces_Before_Extension|Execution of File With Spaces Before Extension]] +* [[Documentation:ESSOC:detections:Detections#Execution_of_file_with_spaces_before_extension|Execution of File With Spaces Before Extension]] -* [[Documentation:ESSOC:detections:Detections#Execution_of_File_with_Multiple_Extensions|Execution of File with Multiple Extensions]] +* [[Documentation:ESSOC:detections:Detections#Execution_of_file_with_multiple_extensions|Execution of File with Multiple Extensions]] + +* [[Documentation:ESSOC:detections:Detections#Suspicious_changes_to_file_associations|Suspicious Changes to File Associations]] -* [[Documentation:ESSOC:detections:Detections#Suspicious_Changes_to_File_Associations|Suspicious Changes to File Associations]] ====ATT&CK==== @@ -5377,6 +5194,7 @@ Detect and investigate suspected abuse of file extensions and Windows file assoc | Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -5395,12 +5213,12 @@ Detect and investigate suspected abuse of file extensions and Windows file assoc ---- -===Windows Service Abuse=== +===Windows service abuse=== Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1543.003/ T1543.003], [https://attack.mitre.org/techniques/T1569.002/ T1569.002], [https://attack.mitre.org/techniques/T1574.011/ T1574.011] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1569.002/ T1569.002], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1059.003/ T1059.003], [https://attack.mitre.org/techniques/T1574.011/ T1574.011], [https://attack.mitre.org/techniques/T1543.003/ T1543.003] * '''Last Updated''': 2017-11-02
@@ -5408,11 +5226,12 @@ Windows services are often used by attackers for persistence and the ability to ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#First_Time_Seen_Running_Windows_Service|First Time Seen Running Windows Service]] +* [[Documentation:ESSOC:detections:Detections#First_time_seen_running_windows_service|First Time Seen Running Windows Service]] -* [[Documentation:ESSOC:detections:Detections#Reg_exe_Manipulating_Windows_Services_Registry_Keys|Reg exe Manipulating Windows Services Registry Keys]] +* [[Documentation:ESSOC:detections:Detections#Reg_exe_manipulating_windows_services_registry_keys|Reg exe Manipulating Windows Services Registry Keys]] + +* [[Documentation:ESSOC:detections:Detections#Sc_exe_manipulating_windows_services|Sc exe Manipulating Windows Services]] -* [[Documentation:ESSOC:detections:Detections#Sc_exe_Manipulating_Windows_Services|Sc exe Manipulating Windows Services]] ====ATT&CK==== @@ -5442,6 +5261,7 @@ Windows services are often used by attackers for persistence and the ability to | Persistence, Privilege Escalation |} + ====Kill Chain Phase==== * Actions on Objectives @@ -5467,7 +5287,7 @@ Windows services are often used by attackers for persistence and the ability to ==Vulnerability== -===Apache Struts Vulnerability=== +===Apache struts vulnerability=== Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5480,23 +5300,14 @@ Detect and investigate activities--such as unusually long `Content-Type` length, ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Suspicious_Java_Classes|Suspicious Java Classes]] +* [[Documentation:ESSOC:detections:Detections#Suspicious_java_classes|Suspicious Java Classes]] -* [[Documentation:ESSOC:detections:Detections#Unusually_Long_Content-Type_Length|Unusually Long Content-Type Length]] +* [[Documentation:ESSOC:detections:Detections#Unusually_long_content-type_length|Unusually Long Content-Type Length]] + +* [[Documentation:ESSOC:detections:Detections#Web_servers_executing_suspicious_processes|Web Servers Executing Suspicious Processes]] -* [[Documentation:ESSOC:detections:Detections#Web_Servers_Executing_Suspicious_Processes|Web Servers Executing Suspicious Processes]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1082 -| System Information Discovery -| Discovery -|} ====Kill Chain Phase==== @@ -5518,7 +5329,7 @@ Detect and investigate activities--such as unusually long `Content-Type` length, ---- -===JBoss Vulnerability=== +===Jboss vulnerability=== In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5531,21 +5342,12 @@ In March of 2016, adversaries were seen using JexBoss--an open-source utility us ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Detect_attackers_scanning_for_vulnerable_JBoss_servers|Detect attackers scanning for vulnerable JBoss servers]] +* [[Documentation:ESSOC:detections:Detections#Detect_attackers_scanning_for_vulnerable_jboss_servers|Detect attackers scanning for vulnerable JBoss servers]] + +* [[Documentation:ESSOC:detections:Detections#Detect_malicious_requests_to_exploit_jboss_servers|Detect malicious requests to exploit JBoss servers]] -* [[Documentation:ESSOC:detections:Detections#Detect_malicious_requests_to_exploit_JBoss_servers|Detect malicious requests to exploit JBoss servers]] -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1082 -| System Information Discovery -| Discovery -|} ====Kill Chain Phase==== @@ -5565,7 +5367,7 @@ In March of 2016, adversaries were seen using JexBoss--an open-source utility us ---- -===Spectre And Meltdown Vulnerabilities=== +===Spectre and meltdown vulnerabilities=== Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5578,15 +5380,10 @@ Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploita ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Spectre_and_Meltdown_Vulnerable_Systems|Spectre and Meltdown Vulnerable Systems]] +* [[Documentation:ESSOC:detections:Detections#Spectre_and_meltdown_vulnerable_systems|Spectre and Meltdown Vulnerable Systems]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -5602,7 +5399,7 @@ Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploita ---- -===Splunk Enterprise Vulnerability=== +===Splunk enterprise vulnerability=== Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5615,15 +5412,10 @@ Keeping your Splunk deployment up to date is critical and may help you reduce th ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Open_Redirect_in_Splunk_Web|Open Redirect in Splunk Web]] +* [[Documentation:ESSOC:detections:Detections#Open_redirect_in_splunk_web|Open Redirect in Splunk Web]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -5643,7 +5435,7 @@ Keeping your Splunk deployment up to date is critical and may help you reduce th ---- -===Splunk Enterprise Vulnerability CVE-2018-11409=== +===Splunk enterprise vulnerability cve-2018-11409=== Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud @@ -5656,15 +5448,10 @@ Reduce the risk of CVE-2018-11409, an information disclosure vulnerability withi ====Detection Profile==== -* [[Documentation:ESSOC:detections:Detections#Splunk_Enterprise_Information_Disclosure|Splunk Enterprise Information Disclosure]] +* [[Documentation:ESSOC:detections:Detections#Splunk_enterprise_information_disclosure|Splunk Enterprise Information Disclosure]] + -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|} ====Kill Chain Phase==== @@ -5689,4 +5476,11 @@ Reduce the risk of CVE-2018-11409, an information disclosure vulnerability withi +''#############'' +''# Automatically generated by doc_gen.py in https://github.com/splunk/security_content'' +''# On Date: UTC'' +''# Author: Splunk Security Research'' +''# Contact: research@splunk.com'' +''#############'' + [[Category:V:ESSOC:drafts]] \ No newline at end of file From fc24718fd865248cae83fb716d0c0479d8561872 Mon Sep 17 00:00:00 2001 From: divious1 Date: Thu, 4 Mar 2021 16:35:03 -0500 Subject: [PATCH 16/62] fixing some bugs --- bin/jinja2_templates/doc_detections_wiki.j2 | 2 +- bin/jinja2_templates/doc_stories_wiki.j2 | 2 +- docs/detections.md | 120 +- docs/detections.wiki | 2979 ++++++++++++++++++- docs/stories.md | 2 +- docs/stories.wiki | 307 +- 6 files changed, 3290 insertions(+), 122 deletions(-) diff --git a/bin/jinja2_templates/doc_detections_wiki.j2 b/bin/jinja2_templates/doc_detections_wiki.j2 index 93f00b2a1c..7b5ece8ad2 100644 --- a/bin/jinja2_templates/doc_detections_wiki.j2 +++ b/bin/jinja2_templates/doc_detections_wiki.j2 @@ -33,7 +33,7 @@ All the detections shipped to different Splunk products. Below is a breakdown by * {{ field }} {% endfor %} -{% if detection.mitre_attacks|length > 1 %} +{% if detection.mitre_attacks|length > 0 %} ====ATT&CK==== {| ! style="text-align:left;"| ID diff --git a/bin/jinja2_templates/doc_stories_wiki.j2 b/bin/jinja2_templates/doc_stories_wiki.j2 index 18b9688a22..67024eb080 100644 --- a/bin/jinja2_templates/doc_stories_wiki.j2 +++ b/bin/jinja2_templates/doc_stories_wiki.j2 @@ -22,7 +22,7 @@ All the Analytic Stories shipped to different Splunk products. Below is a breakd * [[Documentation:ESSOC:detections:Detections#{{ detection|replace(" ", "_")|capitalize }}|{{ detection }}]] {% endfor %} -{% if story.mitre_attacks|length > 1 %} +{% if story.mitre_attacks|length > 0 %} ====ATT&CK==== {| ! style="text-align:left;"| ID diff --git a/docs/detections.md b/docs/detections.md index d7d3480a81..a74ca79e96 100644 --- a/docs/detections.md +++ b/docs/detections.md @@ -6222,66 +6222,6 @@ _version_: 3 ### Attempted Credential Dump From Registry via Reg exe Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: Endpoint -- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/) -- **Last Updated**: 2019-12-02 - -
- details - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `attempted_credential_dump_from_registry_via_reg_exe_filter` -``` -#### Associated Analytic Story - -* Credential Dumping - - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required field - - - -#### ATT&CK - -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| -| T1003.002 | Security Account Manager | Credential Access | - - -#### Kill Chain Phase - -* Actions on Objectives - - -#### Known False Positives -None identified. - -#### Reference - - -#### Test Dataset - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - - -_version_: 4 -
- ---- - -### Attempted Credential Dump From Registry via Reg exe -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - - **Product**: UEBA for Security Cloud - **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) @@ -6351,6 +6291,66 @@ _version_: 1 --- +### Attempted Credential Dump From Registry via Reg exe +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/) +- **Last Updated**: 2019-12-02 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` +``` +#### Associated Analytic Story + +* Credential Dumping + + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required field + + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.002 | Security Account Manager | Credential Access | + + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +None identified. + +#### Reference + + +#### Test Dataset + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + + +_version_: 4 +
+ +--- + ### BCDEdit Failure Recovery Modification This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. diff --git a/docs/detections.wiki b/docs/detections.wiki index 08c3e6fc82..83443c083e 100644 --- a/docs/detections.wiki +++ b/docs/detections.wiki @@ -150,6 +150,17 @@ To successfully implement this search, you must be ingesting data that records t +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.001 +| Local Email Collection +| Collection +|} + ====Kill Chain Phase==== @@ -206,6 +217,17 @@ This search requires you to be ingesting your network traffic and populating the +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.002 +| Remote Email Collection +| Collection +|} + ====Kill Chain Phase==== @@ -318,6 +340,17 @@ This search is specific to Okta and requires Okta logs are being ingested in you +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -421,6 +454,17 @@ This search is specific to Okta and requires Okta logs are being ingested in you +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -470,6 +514,17 @@ This search is specific to Okta and requires Okta logs are being ingested in you +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -520,6 +575,17 @@ This search is specific to Okta and requires Okta logs are being ingested in you +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -574,6 +640,17 @@ Events are fed to DSP contains at least email's sender, subject and its message +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566 +| Phishing +| Initial Access +|} + ====Kill Chain Phase==== @@ -677,6 +754,17 @@ You must be ingesting data from email logs and have Splunk integrated with UBA. +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566 +| Phishing +| Initial Access +|} + ====Kill Chain Phase==== @@ -734,6 +822,17 @@ If Splunk Phantom is also configured in your environment, a Playbook called "Sus +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566.001 +| Spearphishing Attachment +| Initial Access +|} + ====Kill Chain Phase==== @@ -840,6 +939,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1082 +| System Information Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -963,6 +1073,17 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1486 +| Data Encrypted for Impact +| Impact +|} + ====Kill Chain Phase==== @@ -1021,6 +1142,17 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1486 +| Data Encrypted for Impact +| Impact +|} + ====Kill Chain Phase==== @@ -1132,6 +1264,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -1186,6 +1329,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -1239,6 +1393,17 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -1298,6 +1463,17 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -1369,6 +1545,17 @@ You must be ingesting your cloud infrastructure logs. You also must run the base +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -1433,6 +1620,17 @@ You must be ingesting your cloud infrastructure logs. You also must run the base +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -1497,6 +1695,17 @@ You must be ingesting your cloud infrastructure logs. You also must run the base +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -1560,6 +1769,17 @@ You must be ingesting your cloud infrastructure logs. You also must run the base +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -1614,6 +1834,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -1666,6 +1897,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -1724,6 +1966,17 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -1781,6 +2034,17 @@ You must be ingesting the appropriate cloud-infrastructure logs Run the "Previou +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -1838,6 +2102,17 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. Y +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -2015,6 +2290,17 @@ This search has a dependency on other searches to create and update a baseline o +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -2074,6 +2360,17 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -2134,6 +2431,17 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -2192,6 +2500,17 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -2252,6 +2571,17 @@ You must be ingesting your cloud infrastructure logs from your cloud provider. +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -2375,6 +2705,17 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -2441,6 +2782,17 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -2507,6 +2859,17 @@ You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -2574,6 +2937,17 @@ This search relies on the Splunk Add-on for Google Cloud Platform, setting up a +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + ====Kill Chain Phase==== @@ -2631,6 +3005,17 @@ This search relies on the Splunk Add-on for Google Cloud Platform, setting up a +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + ====Kill Chain Phase==== @@ -2684,6 +3069,17 @@ This search looks for CloudTrail events where a user has created an open/public +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + ====Kill Chain Phase==== @@ -2746,6 +3142,17 @@ You must install the AWS App for Splunk. +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + ====Kill Chain Phase==== @@ -2808,6 +3215,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + ====Kill Chain Phase==== @@ -2984,6 +3402,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + ====Kill Chain Phase==== @@ -3102,6 +3531,17 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -3157,6 +3597,17 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -3210,6 +3661,17 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -3267,6 +3729,17 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -3320,6 +3793,17 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -3370,6 +3854,17 @@ This search will detect more than 5 login failures in Office365 Azure Active Dir +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1110.001 +| Password Guessing +| Credential Access +|} + ====Kill Chain Phase==== @@ -4035,6 +4530,17 @@ You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audi +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -4385,6 +4891,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1525 +| Implant Container Image +| Persistence +|} + ====Kill Chain Phase==== @@ -4436,6 +4953,17 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.003 +| Cloud Account +| Persistence +|} + ====Kill Chain Phase==== @@ -4495,6 +5023,17 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.003 +| Cloud Account +| Persistence +|} + ====Kill Chain Phase==== @@ -4561,6 +5100,17 @@ You must install Splunk Microsoft Office 365 add-on. This search works with o365 +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -4618,6 +5168,17 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1556 +| Modify Authentication Process +| Credential Access, Defense Evasion +|} + ====Kill Chain Phase==== @@ -4674,6 +5235,17 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1110 +| Brute Force +| Credential Access +|} + ====Kill Chain Phase==== @@ -4732,6 +5304,17 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1556 +| Modify Authentication Process +| Credential Access, Defense Evasion +|} + ====Kill Chain Phase==== @@ -4789,6 +5372,17 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.003 +| Cloud Account +| Persistence +|} + ====Kill Chain Phase==== @@ -4852,6 +5446,17 @@ You must install splunk Microsoft Office 365 add-on. This search works with o365 +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114 +| Email Collection +| Collection +|} + ====Kill Chain Phase==== @@ -4911,6 +5516,17 @@ This search detects when an admin configured a forwarding rule for multiple mail +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.003 +| Email Forwarding Rule +| Collection +|} + ====Kill Chain Phase==== @@ -4967,6 +5583,17 @@ This search detects the assignment of rights to accesss content from another mai +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.002 +| Remote Email Collection +| Collection +|} + ====Kill Chain Phase==== @@ -5024,6 +5651,17 @@ This search detects when multiple user configured a forwarding rule to the same +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.003 +| Email Forwarding Rule +| Collection +|} + ====Kill Chain Phase==== @@ -5076,6 +5714,17 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -5127,6 +5776,17 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -5176,6 +5836,17 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -5225,6 +5896,17 @@ You must install splunk AWS add on and Splunk App for AWS. This search works wit +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -5276,6 +5958,17 @@ You must install splunk AWS add-on and Splunk App for AWS. This search works wit +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1550 +| Use Alternate Authentication Material +| Defense Evasion, Lateral Movement +|} + ====Kill Chain Phase==== @@ -5325,6 +6018,17 @@ You must install splunk GCP add-on. This search works with gcp:pubsub:message lo +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -5397,6 +6101,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -5459,6 +6174,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -5581,6 +6307,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -5638,6 +6375,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -5692,6 +6440,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -5748,6 +6507,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -5800,6 +6570,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -5859,6 +6640,17 @@ Detailed documentation on how to create a new field within Incident Review may b +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|} + ====Kill Chain Phase==== @@ -5967,6 +6759,17 @@ To successfully implement this search you will need to ensure that DNS data is p +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.004 +| DNS +| Command and Control +|} + ====Kill Chain Phase==== @@ -6091,6 +6894,17 @@ Detailed documentation on how to create a new field within Incident Review may b +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -6158,6 +6972,17 @@ If Splunk>Phantom is also configured in your environment, a Playbook called `Let +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566.003 +| Spearphishing via Service +| Initial Access +|} + ====Kill Chain Phase==== @@ -6218,6 +7043,17 @@ To successfully implement this search you need to ingest data from your DNS logs +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|} + ====Kill Chain Phase==== @@ -6275,6 +7111,17 @@ This search needs Sysmon Logs and a sysmon configuration, which includes EventCo +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + ====Kill Chain Phase==== @@ -6332,6 +7179,17 @@ You must be ingesting Windows Security logs. You must also enable the account ch +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + ====Kill Chain Phase==== @@ -6404,6 +7262,17 @@ Detailed documentation on how to create a new field within Incident Review may b +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -6470,6 +7339,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -6536,6 +7416,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -6651,6 +7542,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -6705,6 +7607,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -6759,6 +7672,17 @@ Detailed documentation on how to create a new field within Incident Review may b +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.001 +| Web Protocols +| Command and Control +|} + ====Kill Chain Phase==== @@ -6824,6 +7748,17 @@ To successfully implement this search, we must ensure that DNS data is being ing +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|} + ====Kill Chain Phase==== @@ -6888,6 +7823,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -6944,6 +7890,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -7127,6 +8084,17 @@ You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add- +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -7177,6 +8145,17 @@ To successfully implement this search, you must be ingesting data that records p +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -7367,6 +8346,17 @@ You must install the GCP App for Splunk (version 2.0.0 or later), then configure +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1525 +| Implant Container Image +| Persistence +|} + ====Kill Chain Phase==== @@ -7419,6 +8409,17 @@ To successfully implement this search, you need to be populating the Enterprise +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.002 +| Domain Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -7470,6 +8471,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|} + ====Kill Chain Phase==== @@ -7681,6 +8693,17 @@ To successfully implement this search, you must be ingesting logs with the proce +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.004 +| Disable or Modify System Firewall +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -7799,6 +8822,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1564.001 +| Hidden Files and Directories +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -7907,6 +8941,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + ====Kill Chain Phase==== @@ -7960,6 +9005,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -8071,6 +9127,17 @@ To successfully implement this search you need to be ingesting information on re +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.001 +| Change Default File Association +| Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -8175,6 +9242,17 @@ You need to be ingesting logs with both the process name and command-line from y +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036 +| Masquerading +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -8230,6 +9308,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1204.002 +| Malicious File +| Execution +|} + ====Kill Chain Phase==== @@ -8282,6 +9371,17 @@ This search needs Sysmon Logs with a sysmon configuration, which includes EventC +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + ====Kill Chain Phase==== @@ -8387,6 +9487,17 @@ You must be ingesting data that records the process-system activity from your ho +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.001 +| Disable or Modify Tools +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -8439,6 +9550,17 @@ You must be ingesting data that records the process-system activity from your ho +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.003 +| Windows Command Shell +| Execution +|} + ====Kill Chain Phase==== @@ -8549,6 +9671,17 @@ This search requires Sysmon Logs and a Sysmon configuration, which includes Even +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + ====Kill Chain Phase==== @@ -8926,6 +10059,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1553.004 +| Install Root Certificate +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -8984,6 +10128,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|} + ====Kill Chain Phase==== @@ -9042,6 +10197,17 @@ You must be ingesting data that records the file-system activity from your hosts +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.001 +| Disable or Modify Tools +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -9070,60 +10236,6 @@ None identified. Attempts to disable security-related services should be identif ===Attempted credential dump from registry via reg exe=== Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.002/ T1003.002] -* '''Last Updated''': 2019-12-02 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `attempted_credential_dump_from_registry_via_reg_exe_filter` - -====Associated Analytic Story==== - -* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] - - -====How To Implement==== -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -====Required field==== - - - - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -None identified. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - - -''version'': 4 -
-
- ----- - -===Attempted credential dump from registry via reg exe=== -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - * '''Product''': UEBA for Security Cloud * '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] @@ -9164,6 +10276,17 @@ You must be ingesting windows endpoint data that tracks process activity, includ +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + ====Kill Chain Phase==== @@ -9187,6 +10310,71 @@ None identified. ---- +===Attempted credential dump from registry via reg exe=== +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.002/ T1003.002] +* '''Last Updated''': 2019-12-02 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.002 +| Security Account Manager +| Credential Access +|} + + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + ===Bcdedit failure recovery modification=== This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. @@ -9220,6 +10408,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1490 +| Inhibit System Recovery +| Impact +|} + ====Kill Chain Phase==== @@ -9278,6 +10477,17 @@ You must be ingesting data that records the file-system activity from your hosts +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1204.002 +| Malicious File +| Execution +|} + ====Kill Chain Phase==== @@ -9388,6 +10598,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + ====Kill Chain Phase==== @@ -9450,6 +10671,17 @@ Detailed documentation on how to create a new field within Incident Review may b +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1485 +| Data Destruction +| Impact +|} + ====Kill Chain Phase==== @@ -9509,6 +10741,17 @@ You must be ingesting data that records file-system activity from your hosts to +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1485 +| Data Destruction +| Impact +|} + ====Kill Chain Phase==== @@ -9563,6 +10806,17 @@ This search needs Sysmon Logs with a Sysmon configuration, which includes EventC +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + ====Kill Chain Phase==== @@ -9619,6 +10873,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.001 +| Local Account +| Persistence +|} + ====Kill Chain Phase==== @@ -9678,6 +10943,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070.005 +| Network Share Connection Removal +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -9734,6 +11010,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + ====Kill Chain Phase==== @@ -9790,6 +11077,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + ====Kill Chain Phase==== @@ -9846,6 +11144,17 @@ This search requires Sysmon Logs and a Sysmon configuration, which includes Even +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + ====Kill Chain Phase==== @@ -9906,6 +11215,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + ====Kill Chain Phase==== @@ -9962,6 +11282,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + ====Kill Chain Phase==== @@ -10032,6 +11363,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + ====Kill Chain Phase==== @@ -10096,6 +11438,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + ====Kill Chain Phase==== @@ -10237,6 +11590,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + ====Kill Chain Phase==== @@ -10305,6 +11669,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + ====Kill Chain Phase==== @@ -10367,6 +11742,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + ====Kill Chain Phase==== @@ -10429,6 +11815,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + ====Kill Chain Phase==== @@ -10495,6 +11892,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + ====Kill Chain Phase==== @@ -10559,6 +11967,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + ====Kill Chain Phase==== @@ -10620,6 +12039,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003 +| OS Credential Dumping +| Credential Access +|} + ====Kill Chain Phase==== @@ -10676,6 +12106,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1490 +| Inhibit System Recovery +| Impact +|} + ====Kill Chain Phase==== @@ -10730,6 +12171,17 @@ To successfully implement this search, you must ingest your Windows Security Eve +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1550.002 +| Pass the Hash +| Defense Evasion, Lateral Movement +|} + ====Kill Chain Phase==== @@ -10781,6 +12233,17 @@ Splunk Universal Forwarder running on Linux systems, capturing logs from the /va +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + ====Kill Chain Phase==== @@ -10834,6 +12297,17 @@ Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + ====Kill Chain Phase==== @@ -10885,6 +12359,17 @@ OSQuery installed and configured to pick up process events (info at https://osqu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + ====Kill Chain Phase==== @@ -10936,6 +12421,17 @@ This search requires audit computer account management to be enabled on the syst +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1210 +| Exploitation of Remote Services +| Lateral Movement +|} + ====Kill Chain Phase==== @@ -10992,6 +12488,17 @@ This search needs Sysmon Logs and a sysmon configuration, which includes EventCo +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + ====Kill Chain Phase==== @@ -11056,6 +12563,17 @@ You must be ingesting endpoint data that tracks process activity, including Wind +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + ====Kill Chain Phase==== @@ -11116,6 +12634,17 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "Exc +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.002 +| Domain Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -11172,6 +12701,17 @@ ou must ingest your Windows security event logs in the `Change` datamodel under +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.003 +| Local Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -11226,6 +12766,17 @@ To successfully implement this search, you need to be ingesting logs with the pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.001 +| Compiled HTML File +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -11286,6 +12837,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.001 +| Compiled HTML File +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -11350,6 +12912,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.001 +| Compiled HTML File +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -11416,6 +12989,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.001 +| Compiled HTML File +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -11495,6 +13079,17 @@ The test data is converted from Windows Security Event logs generated from Attac +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1558.003 +| Kerberoasting +| Credential Access +|} + ====Kill Chain Phase==== @@ -11549,6 +13144,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -11610,6 +13216,17 @@ You must be ingesting Windows event logs using the Splunk Windows TA and collect +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.001 +| Local Account +| Persistence +|} + ====Kill Chain Phase==== @@ -11681,6 +13298,17 @@ You must be ingesting data that records filesystem and process activity from you +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566.001 +| Spearphishing Attachment +| Initial Access +|} + ====Kill Chain Phase==== @@ -11748,6 +13376,17 @@ The test data is converted from Windows Security Event logs generated from Attac +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1550.002 +| Pass the Hash +| Defense Evasion, Lateral Movement +|} + ====Kill Chain Phase==== @@ -11809,6 +13448,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1574.009 +| Path Interception by Unquoted Path +| Defense Evasion, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -11872,6 +13522,17 @@ You must be ingesting data that records process activity from your hosts and pop +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.003 +| Windows Command Shell +| Execution +|} + ====Kill Chain Phase==== @@ -11940,6 +13601,17 @@ You must be ingesting sysmon logs. This search has been modified to process raw +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059 +| Command and Scripting Interpreter +| Execution +|} + ====Kill Chain Phase==== @@ -11994,6 +13666,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|} + ====Kill Chain Phase==== @@ -12115,6 +13798,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12177,6 +13871,17 @@ To successfully implement this search, you need to be ingesting logs with the pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12238,6 +13943,17 @@ To successfully implement this search, you need to be ingesting logs with the pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12298,6 +14014,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12358,6 +14085,17 @@ To successfully implement this search, you need to be ingesting logs with the pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12419,6 +14157,17 @@ To successfully implement this search, you need to be ingesting logs with the pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12480,6 +14229,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.010 +| Regsvr32 +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12542,6 +14302,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12606,6 +14377,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12670,6 +14452,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12734,6 +14527,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12796,6 +14600,17 @@ To successfully implement this search, you must be ingesting data that records p +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.003 +| Windows Command Shell +| Execution +|} + ====Kill Chain Phase==== @@ -12850,6 +14665,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12910,6 +14736,17 @@ To successfully implement this search, you need to be ingesting logs with the pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -12972,6 +14809,17 @@ You must be ingesting data that records registry activity from your hosts to pop +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1016 +| System Network Configuration Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -13030,6 +14878,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1072 +| Software Deployment Tools +| Execution, Lateral Movement +|} + ====Kill Chain Phase==== @@ -13084,6 +14943,17 @@ To successfully implement this search, you must be ingesting data that records r +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1548.002 +| Bypass User Account Control +| Defense Evasion, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -13140,6 +15010,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + ====Kill Chain Phase==== @@ -13199,6 +15080,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + ====Kill Chain Phase==== @@ -13260,6 +15152,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.001 +| LSASS Memory +| Credential Access +|} + ====Kill Chain Phase==== @@ -13320,6 +15223,17 @@ To successfully implement this search, you must be ingesting data that records p +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -13432,6 +15346,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + ====Kill Chain Phase==== @@ -13491,6 +15416,17 @@ While this search does not require you to adhere to Splunk CIM, you must be inge +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1569.002 +| Service Execution +| Execution +|} + ====Kill Chain Phase==== @@ -13634,6 +15570,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1222.001 +| Windows File and Directory Permissions Modification +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -13781,6 +15728,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1585 +| Establish Accounts +| Resource Development +|} + ====Kill Chain Phase==== @@ -13843,6 +15801,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070 +| Indicator Removal on Host +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -14457,6 +16426,17 @@ You must be ingesting endpoint data that tracks process activity, and include th +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1558.003 +| Kerberoasting +| Credential Access +|} + ====Kill Chain Phase==== @@ -14569,6 +16549,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|} + ====Kill Chain Phase==== @@ -14627,6 +16618,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1027 +| Obfuscated Files or Information +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -14683,6 +16685,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|} + ====Kill Chain Phase==== @@ -14741,6 +16754,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1059.001 +| PowerShell +| Execution +|} + ====Kill Chain Phase==== @@ -14797,6 +16821,17 @@ To successfully implement this search, you must be ingesting data that records r +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1547.010 +| Port Monitors +| Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -14930,6 +16965,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1482 +| Domain Trust Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -15000,6 +17046,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.003 +| NTDS +| Credential Access +|} + ====Kill Chain Phase==== @@ -15062,6 +17119,17 @@ You must be ingesting data that records the filesystem activity from your hosts +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.008 +| Accessibility Features +| Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -15200,6 +17268,17 @@ You must be ingesting data that records filesystem and process activity from you +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566.002 +| Spearphishing Link +| Initial Access +|} + ====Kill Chain Phase==== @@ -15260,6 +17339,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + ====Kill Chain Phase==== @@ -15371,6 +17461,17 @@ To successfully implement this search, you must be ingesting data that records p +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.004 +| Disable or Modify System Firewall +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -15857,6 +17958,17 @@ You must be ingesting Windows Security logs from devices of interest, including +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1592 +| Gather Victim Host Information +| Reconnaissance +|} + ====Kill Chain Phase==== @@ -16765,6 +18877,17 @@ To successfully implement this search, you must be ingesting data that records r +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1574.011 +| Services Registry Permissions Weakness +| Defense Evasion, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -16831,6 +18954,17 @@ To successfully implement this search, you must be ingesting data that records r +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1547.001 +| Registry Run Keys / Startup Folder +| Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -16889,6 +19023,17 @@ To successfully implement this search, you must be ingesting data that records r +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.012 +| Image File Execution Options Injection +| Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -16947,6 +19092,17 @@ To successfully implement this search, you must populate the Change_Analysis dat +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.011 +| Application Shimming +| Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -17003,6 +19159,17 @@ To successfully implement this search, you must be ingesting data that records p +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|} + ====Kill Chain Phase==== @@ -17057,6 +19224,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + ====Kill Chain Phase==== @@ -17111,6 +19289,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -17165,6 +19354,17 @@ You must be ingesting data that records the filesystem activity from your hosts +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1486 +| Data Encrypted for Impact +| Impact +|} + ====Kill Chain Phase==== @@ -17219,6 +19419,17 @@ You must be ingesting data that records the file-system activity from your hosts +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1486 +| Data Encrypted for Impact +| Impact +|} + ====Kill Chain Phase==== @@ -17283,6 +19494,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1543.003 +| Windows Service +| Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -17339,6 +19561,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -17395,6 +19628,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -17451,6 +19695,17 @@ To successfully implement this search you need to be ingesting logs with both th +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1053.005 +| Scheduled Task +| Execution, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -17505,6 +19760,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + ====Kill Chain Phase==== @@ -17808,6 +20074,17 @@ You must be ingesting data that records the filesystem activity from your hosts +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.011 +| Application Shimming +| Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -17862,6 +20139,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.011 +| Application Shimming +| Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -17919,6 +20207,17 @@ This search requires you to have enabled your Group Management Audit Logs in you +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136.001 +| Local Account +| Persistence +|} + ====Kill Chain Phase==== @@ -17978,6 +20277,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1204.002 +| Malicious File +| Execution +|} + ====Kill Chain Phase==== @@ -18092,6 +20402,17 @@ This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1203 +| Exploitation for Client Execution +| Execution +|} + ====Kill Chain Phase==== @@ -18221,6 +20542,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1127.001 +| MSBuild +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -18291,6 +20623,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1112 +| Modify Registry +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -18347,6 +20690,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.010 +| Regsvr32 +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -18488,6 +20842,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -18552,6 +20917,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -18623,6 +20999,17 @@ To successfully implement this search, you need to be ingesting logs with the pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -18758,6 +21145,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1127 +| Trusted Developer Utilities Proxy Execution +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -18889,6 +21287,17 @@ To successfully implement this search, you need to be ingesting logs with the pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -18947,6 +21356,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.005 +| Mshta +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -19009,6 +21429,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070.001 +| Clear Windows Event Logs +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -19065,6 +21496,17 @@ To successfully implement this search you need to be ingesting information on fi +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036 +| Masquerading +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -19120,6 +21562,17 @@ To successfully implement this search you need to be ingesting information on pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1082 +| System Information Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -19209,6 +21662,17 @@ Collect endpoint data such as sysmon or 4688 events. +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036 +| Masquerading +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -19266,6 +21730,17 @@ To successfully implement this search you need to ingest details about process e +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1036.003 +| Rename System Utilities +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -19323,6 +21798,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070 +| Indicator Removal on Host +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -19378,6 +21864,17 @@ You must be ingesting data that records process activity from your hosts to popu +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.001 +| Disable or Modify Tools +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -19631,6 +22128,17 @@ You must be ingesting endpoint data that tracks process activity, including pare +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1490 +| Inhibit System Recovery +| Impact +|} + ====Kill Chain Phase==== @@ -19696,6 +22204,17 @@ To successfully implement this search, you must be ingesting the Windows WMI act +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + ====Kill Chain Phase==== @@ -19746,6 +22265,17 @@ To successfully implement this search, you must be collecting Sysmon data using +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1546.003 +| Windows Management Instrumentation Event Subscription +| Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -19802,6 +22332,17 @@ To successfully implement this search, you must be ingesting the Windows WMI act +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1047 +| Windows Management Instrumentation +| Execution +|} + ====Kill Chain Phase==== @@ -19854,6 +22395,17 @@ To successfully implement this search, you need to be ingesting logs with the pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1018 +| Remote System Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -19913,6 +22465,17 @@ To successfully implement this search, you need to be ingesting Windows event lo +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1070.001 +| Clear Windows Event Logs +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -19969,6 +22532,17 @@ You must be ingesting data that records the process-system activity from your ho +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1489 +| Service Stop +| Impact +|} + ====Kill Chain Phase==== @@ -20045,6 +22619,17 @@ Detailed documentation on how to create a new field within Incident Review may b +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.004 +| DNS +| Command and Control +|} + ====Kill Chain Phase==== @@ -20104,6 +22689,17 @@ To successfully implement this search, you will need to ensure that DNS data is +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|} + ====Kill Chain Phase==== @@ -20175,6 +22771,17 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "DNS +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.004 +| DNS +| Command and Control +|} + ====Kill Chain Phase==== @@ -20396,6 +23003,17 @@ In order to run this search effectively, we highly recommend that you leverage t +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1095 +| Non-Application Layer Protocol +| Command and Control +|} + ====Kill Chain Phase==== @@ -20452,6 +23070,17 @@ In order to run this search effectively, we highly recommend that you leverage t +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.002 +| File Transfer Protocols +| Command and Control +|} + ====Kill Chain Phase==== @@ -20667,6 +23296,17 @@ You must be ingesting Zeek SSL data into Splunk. Zeek data should also be gettin +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1041 +| Exfiltration Over C2 Channel +| Exfiltration +|} + ====Kill Chain Phase==== @@ -20725,6 +23365,17 @@ This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1542.005 +| TFTP Boot +| Defense Evasion, Persistence +|} + ====Kill Chain Phase==== @@ -20913,6 +23564,17 @@ You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1203 +| Exploitation for Client Execution +| Execution +|} + ====Kill Chain Phase==== @@ -20971,6 +23633,17 @@ You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1203 +| Exploitation for Client Execution +| Execution +|} + ====Kill Chain Phase==== @@ -21024,6 +23697,17 @@ You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be ge +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + ====Kill Chain Phase==== @@ -21098,6 +23782,17 @@ Detailed documentation on how to create a new field within Incident Review may b +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1189 +| Drive-by Compromise +| Initial Access +|} + ====Kill Chain Phase==== @@ -21161,6 +23856,17 @@ To successfully implement this search you must ensure that DNS data is populatin +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.004 +| DNS +| Command and Control +|} + ====Kill Chain Phase==== @@ -21217,6 +23923,17 @@ This search requires you to be ingesting your network traffic and populating the +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1114.002 +| Remote Email Collection +| Collection +|} + ====Kill Chain Phase==== @@ -21268,6 +23985,17 @@ To successfully implement this search you must ensure that DNS data is populatin +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1498.002 +| Reflection Amplification +| Impact +|} + ====Kill Chain Phase==== @@ -21326,6 +24054,17 @@ In order to properly run this search, Splunk needs to ingest data from firewalls +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048 +| Exfiltration Over Alternative Protocol +| Exfiltration +|} + ====Kill Chain Phase==== @@ -21382,6 +24121,17 @@ Running this search properly requires a technology that can inspect network traf +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1048.003 +| Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol +| Exfiltration +|} + ====Kill Chain Phase==== @@ -21491,6 +24241,17 @@ You must ensure that your network traffic data is populating the Network_Traffic +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|} + ====Kill Chain Phase==== @@ -21551,6 +24312,17 @@ To successfully implement this search you need to identify systems that commonly +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.001 +| Remote Desktop Protocol +| Lateral Movement +|} + ====Kill Chain Phase==== @@ -21612,6 +24384,17 @@ This search requires you to be ingesting your network traffic logs and populatin +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|} + ====Kill Chain Phase==== @@ -21678,6 +24461,17 @@ Detailed documentation on how to create a new field within Incident Review is fo +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1021.002 +| SMB/Windows Admin Shares +| Lateral Movement +|} + ====Kill Chain Phase==== @@ -21736,6 +24530,17 @@ In order to properly run this search, Splunk needs to ingest data from firewalls +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1071.001 +| Web Protocols +| Command and Control +|} + ====Kill Chain Phase==== @@ -21842,6 +24647,17 @@ To consistently detect exploit attempts on F5 devices using the vulnerabilities +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + ====Kill Chain Phase==== @@ -21902,6 +24718,17 @@ You must be ingesting data from the web server or network traffic that contains +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1082 +| System Information Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -22062,6 +24889,17 @@ To successfully implement this search, you need to be monitoring network communi +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + ====Kill Chain Phase==== @@ -22111,6 +24949,17 @@ To successfully implement this search, you need to be monitoring web traffic to +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1505.003 +| Web Shell +| Persistence +|} + ====Kill Chain Phase==== @@ -22170,6 +25019,17 @@ We start with a dataset that provides visibility into the email address used for +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1136 +| Create Account +| Persistence +|} + ====Kill Chain Phase==== @@ -22227,6 +25087,17 @@ Start with a dataset that allows you to see clickstream data for each user click +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== diff --git a/docs/stories.md b/docs/stories.md index 20e5d8db0e..0ad7855dfe 100644 --- a/docs/stories.md +++ b/docs/stories.md @@ -590,8 +590,8 @@ Uncover activity consistent with credential dumping, a technique wherein attacke | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | | T1059.001 | PowerShell | Execution | -| T1003.002 | Security Account Manager | Credential Access | | T1003 | OS Credential Dumping | Credential Access | +| T1003.002 | Security Account Manager | Credential Access | | T1003.003 | NTDS | Credential Access | #### Kill Chain Phase diff --git a/docs/stories.wiki b/docs/stories.wiki index 9130fdf780..3623763c32 100644 --- a/docs/stories.wiki +++ b/docs/stories.wiki @@ -67,6 +67,17 @@ DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it respond +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1498.002 +| Reflection Amplification +| Impact +|} + ====Kill Chain Phase==== @@ -243,6 +254,17 @@ Detect activities and various techniques associated with the abuse of `netsh.exe +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.004 +| Disable or Modify System Firewall +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -345,6 +367,17 @@ Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Researc +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1068 +| Exploitation for Privilege Escalation +| Privilege Escalation +|} + ====Kill Chain Phase==== @@ -381,6 +414,17 @@ Cobalt Strike is threat emulation software. Red teams and penetration testers us +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.011 +| Rundll32 +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -581,6 +625,17 @@ Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1566.003 +| Spearphishing via Service +| Initial Access +|} + ====Kill Chain Phase==== @@ -609,7 +664,7 @@ Uncover activity consistent with credential dumping, a technique wherein attacke * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1003.002/ T1003.002], [https://attack.mitre.org/techniques/T1003/ T1003], [https://attack.mitre.org/techniques/T1003.003/ T1003.003] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1003/ T1003], [https://attack.mitre.org/techniques/T1003.002/ T1003.002], [https://attack.mitre.org/techniques/T1003.003/ T1003.003] * '''Last Updated''': 2020-02-04
@@ -667,14 +722,14 @@ Uncover activity consistent with credential dumping, a technique wherein attacke | PowerShell | Execution |- -| T1003.002 -| Security Account Manager -| Credential Access -|- | T1003 | OS Credential Dumping | Credential Access |- +| T1003.002 +| Security Account Manager +| Credential Access +|- | T1003.003 | NTDS | Credential Access @@ -797,6 +852,17 @@ The stealing of data by an adversary. +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1041 +| Exfiltration Over C2 Channel +| Exfiltration +|} + ====Kill Chain Phase==== @@ -975,6 +1041,17 @@ Uncover activity consistent with CVE-2020-5902. Discovered by Positive Technolog +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + ====Kill Chain Phase==== @@ -1259,6 +1336,17 @@ Use the searches in this Analytic Story to help you detect structured query lang +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1190 +| Exploit Public-Facing Application +| Initial Access +|} + ====Kill Chain Phase==== @@ -1487,6 +1575,17 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.001 +| Compiled HTML File +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -1746,6 +1845,17 @@ Monitor your Okta environment for suspicious activities. Due to the Covid outbre +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.001 +| Default Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -1792,6 +1902,17 @@ Monitor and detect techniques used by attackers who leverage the mshta.exe proce +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.009 +| Regsvcs/Regasm +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -1832,6 +1953,17 @@ Monitor and detect techniques used by attackers who leverage the regsvr32.exe pr +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1218.010 +| Regsvr32 +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -2280,6 +2412,17 @@ Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpo +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1203 +| Exploitation for Client Execution +| Execution +|} + ====Kill Chain Phase==== @@ -3102,6 +3245,17 @@ Monitor your AWS network infrastructure for bad configurations and malicious act +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1562.007 +| Disable or Modify Cloud Firewall +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -3180,6 +3334,17 @@ Monitor your AWS provisioning activities for behaviors originating from unfamili +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1535 +| Unused/Unsupported Cloud Regions +| Defense Evasion +|} + ====Kill Chain Phase==== @@ -3220,6 +3385,17 @@ Detect and investigate dormant user accounts for your AWS environment that have +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -3412,6 +3588,17 @@ Use the searches in this story to monitor your Kubernetes registry repositories +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1525 +| Implant Container Image +| Persistence +|} + ====Kill Chain Phase==== @@ -3450,6 +3637,17 @@ Track when a user assumes an IAM role in another GCP account to obtain cross-acc +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -3494,6 +3692,17 @@ This story addresses detection against Kubernetes cluster fingerprint scan and a +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1526 +| Cloud Service Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -3847,6 +4056,17 @@ Use the searches in this Analytic Story to monitor your AWS S3 buckets for evide +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + ====Kill Chain Phase==== @@ -3982,6 +4202,17 @@ Monitor your cloud infrastructure provisioning activities for behaviors originat +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -4022,6 +4253,17 @@ Monitor your cloud infrastructure provisioning activities for behaviors originat +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078 +| Valid Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -4111,6 +4353,17 @@ Use the searches in this Analytic Story to monitor your GCP Storage buckets for +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1530 +| Data from Cloud Storage Object +| Collection +|} + ====Kill Chain Phase==== @@ -4147,6 +4400,17 @@ Identify unusual changes to your AWS EC2 instances that may indicate malicious a +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1078.004 +| Cloud Accounts +| Defense Evasion, Initial Access, Persistence, Privilege Escalation +|} + ====Kill Chain Phase==== @@ -4818,6 +5082,17 @@ Leverage searches that allow you to detect and investigate unusual activities th +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1486 +| Data Encrypted for Impact +| Impact +|} + ====Kill Chain Phase==== @@ -5308,6 +5583,17 @@ Detect and investigate activities--such as unusually long `Content-Type` length, +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1082 +| System Information Discovery +| Discovery +|} + ====Kill Chain Phase==== @@ -5348,6 +5634,17 @@ In March of 2016, adversaries were seen using JexBoss--an open-source utility us +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1082 +| System Information Discovery +| Discovery +|} + ====Kill Chain Phase==== From 2076087c3cae5669a07b7b05ab8ca9512fda86bc Mon Sep 17 00:00:00 2001 From: Rod Soto Date: Wed, 17 Mar 2021 09:59:23 -0400 Subject: [PATCH 17/62] clopstory --- stories/ransomware_clop.yml | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 stories/ransomware_clop.yml diff --git a/stories/ransomware_clop.yml b/stories/ransomware_clop.yml new file mode 100644 index 0000000000..19a3612ce1 --- /dev/null +++ b/stories/ransomware_clop.yml @@ -0,0 +1,26 @@ +name: Clop Ransomware +id: 5a6f6849-1a26-4fae-aa05-fa730556eeb6 +version: 1 +date: '17-03-2021' +author: Rod Soto, Teoderick Contreras, Splunk +type: batch +description: Leverage searches that allow you to detect and investigate unusual activities + that might relate to the Clop ransomware, including looking for file writes associated + with Clope, encrypting network shares, deleting and resizing shadow volume storage, registry key modification, + deleting of security logs, and more. +narrative: Clop ransomware campaigns targeting healthcare and other vertical sectors, involve the use of + ransomware payloads along with exfiltration of data per HHS bulletin. Malicious actors demand payment for + ransome of data and threaten deletion and exposure of exfiltrated data. +references: +- https://www.hhs.gov/sites/default/files/analyst-note-cl0p-tlp-white.pdf +- https://securityaffairs.co/wordpress/115250/data-breach/qualys-clop-ransomware.html +- https://www.darkreading.com/attacks-breaches/qualys-is-the-latest-victim-of-accellion-data-breach/d/d-id/1340323 +tags: + analytic_story: Clop Ransomware + category: + - Malware + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + usecase: Advanced Threat Detection From 6f49f38cd51a6c06a36d9f2b46d19707a7512d38 Mon Sep 17 00:00:00 2001 From: Rod Soto Date: Wed, 17 Mar 2021 10:06:36 -0400 Subject: [PATCH 18/62] updatedtags --- detections/endpoint/common_ransomware_extensions.yml | 1 + detections/endpoint/common_ransomware_notes.yml | 1 + detections/endpoint/deleting_shadow_copies.yml | 1 + detections/endpoint/suspicious_wevtutil_usage.yml | 1 + detections/endpoint/windows_event_log_cleared.yml | 1 + 5 files changed, 5 insertions(+) diff --git a/detections/endpoint/common_ransomware_extensions.yml b/detections/endpoint/common_ransomware_extensions.yml index c538968c1b..2e0b65de31 100644 --- a/detections/endpoint/common_ransomware_extensions.yml +++ b/detections/endpoint/common_ransomware_extensions.yml @@ -40,6 +40,7 @@ tags: - SamSam Ransomware - Ryuk Ransomware - Ransomware + - Clop Ransomware asset_type: Endpoint automated_detection_testing: passed cis20: diff --git a/detections/endpoint/common_ransomware_notes.yml b/detections/endpoint/common_ransomware_notes.yml index 1f553a2cff..f0fd2e002a 100644 --- a/detections/endpoint/common_ransomware_notes.yml +++ b/detections/endpoint/common_ransomware_notes.yml @@ -26,6 +26,7 @@ tags: - SamSam Ransomware - Ransomware - Ryuk Ransomware + - Clop Ransomware asset_type: Endpoint automated_detection_testing: passed cis20: diff --git a/detections/endpoint/deleting_shadow_copies.yml b/detections/endpoint/deleting_shadow_copies.yml index 3edefd7369..4209216b6d 100644 --- a/detections/endpoint/deleting_shadow_copies.yml +++ b/detections/endpoint/deleting_shadow_copies.yml @@ -29,6 +29,7 @@ tags: - Windows Log Manipulation - SamSam Ransomware - Ransomware + - Clop Ransomware asset_type: Endpoint automated_detection_testing: passed cis20: diff --git a/detections/endpoint/suspicious_wevtutil_usage.yml b/detections/endpoint/suspicious_wevtutil_usage.yml index 3b0d69d03f..a47ef0d432 100644 --- a/detections/endpoint/suspicious_wevtutil_usage.yml +++ b/detections/endpoint/suspicious_wevtutil_usage.yml @@ -28,6 +28,7 @@ tags: analytic_story: - Windows Log Manipulation - Ransomware + - Clop Ransomware asset_type: '' automated_detection_testing: passed cis20: diff --git a/detections/endpoint/windows_event_log_cleared.yml b/detections/endpoint/windows_event_log_cleared.yml index 6ff621fcbd..8066d443e0 100644 --- a/detections/endpoint/windows_event_log_cleared.yml +++ b/detections/endpoint/windows_event_log_cleared.yml @@ -20,6 +20,7 @@ tags: analytic_story: - Windows Log Manipulation - Ransomware + - Clop Ransomware asset_type: Endpoint automated_detection_testing: passed cis20: From 2fc6f1c4c9df69824dfa0f9775d3e439654dc453 Mon Sep 17 00:00:00 2001 From: Rod Soto Date: Wed, 17 Mar 2021 10:22:40 -0400 Subject: [PATCH 19/62] fixeddateandfirstsearch --- .../endpoint/clop_common_exec_parameter.yml | 41 +++++++++++++++++++ stories/ransomware_clop.yml | 2 +- 2 files changed, 42 insertions(+), 1 deletion(-) create mode 100644 detections/endpoint/clop_common_exec_parameter.yml diff --git a/detections/endpoint/clop_common_exec_parameter.yml b/detections/endpoint/clop_common_exec_parameter.yml new file mode 100644 index 0000000000..6818ae67b4 --- /dev/null +++ b/detections/endpoint/clop_common_exec_parameter.yml @@ -0,0 +1,41 @@ +name: Clop Common Exec Parameter +id: 5a8a2a72-8322-11eb-9ee9-acde48001122 +version: 1 +date: '2021-03-17' +author: Teoderick Contreras, Splunk +type: batch +datamodel: +- Endpoint +description: The following analytics are designed to identifies some CLOP ransomware variant that using arguments + to execute its main code or feature of its code. In this variant if the parameter is "runrun", + CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", + it will try to read from some stream pipe or file start encrypting files within the infected local machines. + This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is + waiting for some parameter to execute properly. +search: '`sysmon` EventCode=1 cmdline IN ("*runrun*", "*temp.dat*") | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, + parent_process_name, process_name, OriginalFileName, process_path, CommandLine | + rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `clop_common_exec_parameter_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting + logs with the process name, parent process, and command-line executions from your + endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the + Sysmon TA. +known_false_positives: Operators can execute third party tools using these parameters. +references: +- https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html +- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html +tags: + analytic_story: + - Clop Ransomware + automated_detection_testing: tba + dataset: + - tba + kill_chain_phases: + - Obfuscation + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/stories/ransomware_clop.yml b/stories/ransomware_clop.yml index 19a3612ce1..8177d02ac9 100644 --- a/stories/ransomware_clop.yml +++ b/stories/ransomware_clop.yml @@ -1,7 +1,7 @@ name: Clop Ransomware id: 5a6f6849-1a26-4fae-aa05-fa730556eeb6 version: 1 -date: '17-03-2021' +date: '2021-03-17' author: Rod Soto, Teoderick Contreras, Splunk type: batch description: Leverage searches that allow you to detect and investigate unusual activities From b2c6c6542f1116ae2f742023ec335a55e265dfef Mon Sep 17 00:00:00 2001 From: divious1 Date: Wed, 17 Mar 2021 22:44:46 -0400 Subject: [PATCH 20/62] addiing spec generation --- bin/doc_gen.py | 51 ++- docs/spec/README.md | 49 --- docs/spec/baselines-properties-author.md | 22 - docs/spec/baselines-properties-date.md | 22 - docs/spec/baselines-properties-description.md | 26 -- .../baselines-properties-how_to_implement.md | 24 -- docs/spec/baselines-properties-id.md | 22 - .../baselines-properties-name-of-baseline.md | 22 - docs/spec/baselines-properties-search.md | 24 -- .../spec/baselines-properties-tags-default.md | 15 - docs/spec/baselines-properties-tags.md | 47 --- docs/spec/baselines-properties-version.md | 22 - docs/spec/baselines.md | 280 +------------ docs/spec/deployments-default.md | 15 - ...oyments-properties-alert_action-default.md | 15 - ...s-alert_action-properties-email-default.md | 15 - ...ion-properties-email-properties-message.md | 22 - ...ion-properties-email-properties-subject.md | 22 - ...t_action-properties-email-properties-to.md | 22 - ...roperties-alert_action-properties-email.md | 120 ------ ...s-alert_action-properties-index-default.md | 15 - ...action-properties-index-properties-name.md | 22 - ...roperties-alert_action-properties-index.md | 66 --- ...alert_action-properties-notable-default.md | 15 - ...ies-notable-properties-rule_description.md | 22 - ...roperties-notable-properties-rule_title.md | 22 - ...perties-alert_action-properties-notable.md | 93 ----- .../deployments-properties-alert_action.md | 153 ------- ...ployments-properties-scheduling-default.md | 15 - ...ies-scheduling-properties-cron_schedule.md | 22 - ...ies-scheduling-properties-earliest_time.md | 22 - ...rties-scheduling-properties-latest_time.md | 22 - ...s-scheduling-properties-schedule_window.md | 22 - .../spec/deployments-properties-scheduling.md | 147 ------- docs/spec/deployments.md | 256 +----------- ...ctions-properties-known_false_positives.md | 24 -- ...-properties-references-the-items-schema.md | 23 - docs/spec/detections-properties-references.md | 31 -- docs/spec/detections-properties-type-items.md | 24 -- docs/spec/detections-properties-type.md | 22 - docs/spec/detections.md | 382 +---------------- docs/spec/lookups-oneof-0.md | 15 - docs/spec/lookups-oneof-1.md | 15 - ...lookups-properties-case_sensitive_match.md | 31 -- docs/spec/lookups-properties-collection.md | 22 - docs/spec/lookups-properties-default_match.md | 22 - docs/spec/lookups-properties-description.md | 22 - docs/spec/lookups-properties-fields_list.md | 22 - docs/spec/lookups-properties-filename.md | 22 - docs/spec/lookups-properties-filter.md | 22 - docs/spec/lookups-properties-match_type.md | 22 - docs/spec/lookups-properties-max_matches.md | 22 - docs/spec/lookups-properties-min_matches.md | 22 - docs/spec/lookups-properties-name.md | 22 - docs/spec/lookups.md | 319 +------------- .../spec/macros-properties-arguments-items.md | 15 - docs/spec/macros-properties-arguments.md | 21 - docs/spec/macros-properties-definition.md | 22 - docs/spec/macros-properties-description.md | 22 - docs/spec/macros-properties-name.md | 22 - docs/spec/macros.md | 121 +----- docs/spec/response_tasks-default.md | 15 - ...nse_tasks-properties-automation-default.md | 15 - .../response_tasks-properties-automation.md | 62 --- docs/spec/response_tasks-properties-sla.md | 27 -- .../response_tasks-properties-sla_type.md | 40 -- docs/spec/response_tasks.md | 393 +----------------- docs/spec/responses-default.md | 15 - .../responses-properties-is_note_required.md | 27 -- ...onses-properties-response_phase-default.md | 15 - .../responses-properties-response_phase.md | 51 --- docs/spec/responses.md | 338 +-------------- docs/spec/responses_phase-default.md | 15 - ..._phase-properties-response_task-default.md | 15 - ...esponses_phase-properties-response_task.md | 57 --- docs/spec/responses_phase.md | 387 +---------------- docs/spec/stories-default.md | 15 - docs/spec/stories-properties-narrative.md | 26 -- docs/spec/stories.md | 285 +------------ 79 files changed, 180 insertions(+), 4718 deletions(-) delete mode 100644 docs/spec/README.md delete mode 100644 docs/spec/baselines-properties-author.md delete mode 100644 docs/spec/baselines-properties-date.md delete mode 100644 docs/spec/baselines-properties-description.md delete mode 100644 docs/spec/baselines-properties-how_to_implement.md delete mode 100644 docs/spec/baselines-properties-id.md delete mode 100644 docs/spec/baselines-properties-name-of-baseline.md delete mode 100644 docs/spec/baselines-properties-search.md delete mode 100644 docs/spec/baselines-properties-tags-default.md delete mode 100644 docs/spec/baselines-properties-tags.md delete mode 100644 docs/spec/baselines-properties-version.md delete mode 100644 docs/spec/deployments-default.md delete mode 100644 docs/spec/deployments-properties-alert_action-default.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-email-default.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-email-properties-message.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-email-properties-to.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-email.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-index-default.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-index-properties-name.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-index.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-notable-default.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-notable.md delete mode 100644 docs/spec/deployments-properties-alert_action.md delete mode 100644 docs/spec/deployments-properties-scheduling-default.md delete mode 100644 docs/spec/deployments-properties-scheduling-properties-cron_schedule.md delete mode 100644 docs/spec/deployments-properties-scheduling-properties-earliest_time.md delete mode 100644 docs/spec/deployments-properties-scheduling-properties-latest_time.md delete mode 100644 docs/spec/deployments-properties-scheduling-properties-schedule_window.md delete mode 100644 docs/spec/deployments-properties-scheduling.md delete mode 100644 docs/spec/detections-properties-known_false_positives.md delete mode 100644 docs/spec/detections-properties-references-the-items-schema.md delete mode 100644 docs/spec/detections-properties-references.md delete mode 100644 docs/spec/detections-properties-type-items.md delete mode 100644 docs/spec/detections-properties-type.md delete mode 100644 docs/spec/lookups-oneof-0.md delete mode 100644 docs/spec/lookups-oneof-1.md delete mode 100644 docs/spec/lookups-properties-case_sensitive_match.md delete mode 100644 docs/spec/lookups-properties-collection.md delete mode 100644 docs/spec/lookups-properties-default_match.md delete mode 100644 docs/spec/lookups-properties-description.md delete mode 100644 docs/spec/lookups-properties-fields_list.md delete mode 100644 docs/spec/lookups-properties-filename.md delete mode 100644 docs/spec/lookups-properties-filter.md delete mode 100644 docs/spec/lookups-properties-match_type.md delete mode 100644 docs/spec/lookups-properties-max_matches.md delete mode 100644 docs/spec/lookups-properties-min_matches.md delete mode 100644 docs/spec/lookups-properties-name.md delete mode 100644 docs/spec/macros-properties-arguments-items.md delete mode 100644 docs/spec/macros-properties-arguments.md delete mode 100644 docs/spec/macros-properties-definition.md delete mode 100644 docs/spec/macros-properties-description.md delete mode 100644 docs/spec/macros-properties-name.md delete mode 100644 docs/spec/response_tasks-default.md delete mode 100644 docs/spec/response_tasks-properties-automation-default.md delete mode 100644 docs/spec/response_tasks-properties-automation.md delete mode 100644 docs/spec/response_tasks-properties-sla.md delete mode 100644 docs/spec/response_tasks-properties-sla_type.md delete mode 100644 docs/spec/responses-default.md delete mode 100644 docs/spec/responses-properties-is_note_required.md delete mode 100644 docs/spec/responses-properties-response_phase-default.md delete mode 100644 docs/spec/responses-properties-response_phase.md delete mode 100644 docs/spec/responses_phase-default.md delete mode 100644 docs/spec/responses_phase-properties-response_task-default.md delete mode 100644 docs/spec/responses_phase-properties-response_task.md delete mode 100644 docs/spec/stories-default.md delete mode 100644 docs/spec/stories-properties-narrative.md diff --git a/bin/doc_gen.py b/bin/doc_gen.py index 55057556c1..9f38e1ac82 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -5,6 +5,7 @@ import sys import re from os import path, walk import json +import jsonschema2md from jinja2 import Environment, FileSystemLoader from attackcti import attack_client from pyattck import Attck @@ -37,6 +38,42 @@ def get_mitre_enrichment_new(attack, mitre_attack_id): return mitre_attack return [] +def generate_doc_specs(REPO_PATH, OUTPUT_DIR, messages, VERBOSE): + spec_files = [] + + for root, dirs, files in walk(REPO_PATH + '/spec'): + for file in files: + if file.endswith(".json"): + spec_files.append((path.join(root, file))) + + parser = jsonschema2md.Parser() + spec_objects = [] + for spec in spec_files: + spec_object = dict() + if VERBOSE: + print("processing spec {0}".format(spec)) + with open(spec, 'r') as stream: + try: + markdown_lines = parser.parse_schema(json.load(stream)) + except json.JSONError as exc: + print(exc) + print("Error reading {0}".format(spec)) + sys.exit(1) + + spec_object['file'] = spec + spec_object['markdown'] = markdown_lines + spec_objects.append(spec_object) + + for spec in spec_objects: + # write markdown + file = spec['file'].split("/")[-1].split(".")[0] + output_path = path.join(OUTPUT_DIR + "/" + file + '.md' ) + with open(output_path, 'w', encoding="utf-8") as f: + f.write("\n".join(spec['markdown'])) + messages.append("doc_gen.py wrote {0} spec file documentation in markdown to: {1}".format(file,output_path)) + + return messages + def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE): manifest_files = [] for root, dirs, files in walk(REPO_PATH + '/stories'): @@ -56,8 +93,7 @@ def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_de except yaml.YAMLError as exc: print(exc) print("Error reading {0}".format(manifest_file)) - error = True - continue + sys.exit(1) story_yaml = object # enrich the mitre object @@ -186,8 +222,7 @@ def generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messag except yaml.YAMLError as exc: print(exc) print("Error reading {0}".format(manifest_file)) - error = True - continue + sys.exit(1) detection_yaml = object # enrich the mitre object @@ -273,7 +308,13 @@ if __name__ == "__main__": if type == 'all': sorted_detections, messages = generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE) sorted_stories, messages = generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE) - + elif type == 'detections': + sorted_detections, messages = generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE) + elif type == 'stories': + sorted_detections, messages = generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE) + sorted_stories, messages = generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE) + elif type == 'spec': + messages = generate_doc_specs(REPO_PATH, OUTPUT_DIR, messages, VERBOSE) # print all the messages from generation for m in messages: diff --git a/docs/spec/README.md b/docs/spec/README.md deleted file mode 100644 index 7c4e1f7821..0000000000 --- a/docs/spec/README.md +++ /dev/null @@ -1,49 +0,0 @@ -# README - -## Top-level Schemas - -* [Analytics Story Schema](./stories.md "schema analytics story") – `http://example.com/example.json` - -* [Baseline Schema](./baselines.md "schema for baselines") – `http://example.com/example.json` - -* [Deployment Schema](./deployments.md "schema for deployment") – `http://example.com/example.json` - -* [Detection Schema](./detections.md "schema for detections") – `http://example.com/example.json` - -* [Lookup Manifest](./lookups.md "A object that defines a lookup file and its properties") – `https://api.splunkresearch.com/schemas/lookups.json` - -* [Macro Manifest](./macros.md "An object that defines the parameters for a Splunk Macro") – `https://api.splunkresearch.com/schemas/macros.json` - -* [Response Schema](./response_tasks.md "schema for response task") – `https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response_tasks.spec.json` - -* [Response Schema](./responses.md "schema for response") – `https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response.spec.json` - -* [Response Schema](./responses_phase.md "schema for phase") – `http://example.com/example.json` - -## Other Schemas - -### Objects - -* [Untitled object in Baseline Schema](./baselines-properties-tags.md "An array of key value pairs for tagging") – `#/properties/tags#/properties/tags` - -* [Untitled object in Deployment Schema](./deployments-properties-alert_action.md "Set alert action parameter for search") – `#/properties/alert_action#/properties/alert_action` - -* [Untitled object in Deployment Schema](./deployments-properties-alert_action-properties-email.md "By enabling it, an email is sent with the results") – `#/properties/alert_action/properties/email#/properties/alert_action/properties/email` - -* [Untitled object in Deployment Schema](./deployments-properties-alert_action-properties-index.md "By enabling it, the results are stored in another index") – `#/properties/alert_action/properties/index#/properties/alert_action/properties/index` - -* [Untitled object in Deployment Schema](./deployments-properties-alert_action-properties-notable.md "By enabling it, a notable is generated") – `#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable` - -* [Untitled object in Deployment Schema](./deployments-properties-scheduling.md "allows to set scheduling parameter") – `#/properties/scheduling#/properties/scheduling` - -* [Untitled object in Response Schema](./response_tasks-properties-automation.md "An array of key value pairs for defining actions and playbooks") – `#/properties/automation#/properties/automation` - -### Arrays - -* [Untitled array in Detection Schema](./detections-properties-references.md "A list of references for this detection") – `#/properties/references#/properties/references` - -* [Untitled array in Macro Manifest](./macros-properties-arguments.md "A list of the arguments being passed to this macro") – `https://api.splunkresearch.com/schemas/macros.json#/properties/arguments` - -* [Untitled array in Response Schema](./responses-properties-response_phase.md "Response divided into phases") – `#/properties/response_phases#/properties/response_phase` - -* [Untitled array in Response Schema](./responses_phase-properties-response_task.md "Response phase is divided into task(s) to be completed") – `#/properties/response_task#/properties/response_task` diff --git a/docs/spec/baselines-properties-author.md b/docs/spec/baselines-properties-author.md deleted file mode 100644 index 25b1c05002..0000000000 --- a/docs/spec/baselines-properties-author.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/author#/properties/author -``` - -Author of the baseline - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## author Type - -`string` - -## author Examples - -```yaml -Bahvin Patel, Splunk - -``` diff --git a/docs/spec/baselines-properties-date.md b/docs/spec/baselines-properties-date.md deleted file mode 100644 index f91effacdf..0000000000 --- a/docs/spec/baselines-properties-date.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/date#/properties/date -``` - -date of creation or modification, format yyyy-mm-dd - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## date Type - -`string` - -## date Examples - -```yaml -'2019-12-06' - -``` diff --git a/docs/spec/baselines-properties-description.md b/docs/spec/baselines-properties-description.md deleted file mode 100644 index e61031299b..0000000000 --- a/docs/spec/baselines-properties-description.md +++ /dev/null @@ -1,26 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/description#/properties/description -``` - -A detailed description of the baseline - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## description Type - -`string` - -## description Examples - -```yaml ->- - This search looks for CloudTrail events where an AWS instance is started and - creates a baseline of most recent time (latest) and the first time (earliest) - we've seen this region in our dataset grouped by the value awsRegion for the - last 30 days - -``` diff --git a/docs/spec/baselines-properties-how_to_implement.md b/docs/spec/baselines-properties-how_to_implement.md deleted file mode 100644 index 0bac7d7e11..0000000000 --- a/docs/spec/baselines-properties-how_to_implement.md +++ /dev/null @@ -1,24 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/how_to_implement#/properties/how_to_implement -``` - -information about how to implement. Only needed for non standard implementations. - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## how_to_implement Type - -`string` - -## how_to_implement Examples - -```yaml ->- - This search requires Sysmon Logs and a Sysmon configuration, which includes - EventCode 10 for lsass.exe. - -``` diff --git a/docs/spec/baselines-properties-id.md b/docs/spec/baselines-properties-id.md deleted file mode 100644 index 0ac191b7fb..0000000000 --- a/docs/spec/baselines-properties-id.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/id#/properties/id -``` - -UUID as unique identifier - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## id Type - -`string` - -## id Examples - -```yaml -fc0edc95-ff2b-48b0-9f6f-63da3789fd63 - -``` diff --git a/docs/spec/baselines-properties-name-of-baseline.md b/docs/spec/baselines-properties-name-of-baseline.md deleted file mode 100644 index 46e7b91cba..0000000000 --- a/docs/spec/baselines-properties-name-of-baseline.md +++ /dev/null @@ -1,22 +0,0 @@ -# Name of baseline Schema - -```txt -#/properties/name#/properties/name -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## name Type - -`string` ([Name of baseline](baselines-properties-name-of-baseline.md)) - -## name Examples - -```yaml -Previously Seen AWS Regions - -``` diff --git a/docs/spec/baselines-properties-search.md b/docs/spec/baselines-properties-search.md deleted file mode 100644 index dae22b4ca4..0000000000 --- a/docs/spec/baselines-properties-search.md +++ /dev/null @@ -1,24 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/search#/properties/search -``` - -The Splunk search for the baseline - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## search Type - -`string` - -## search Examples - -```yaml ->- - cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as - latest by awsRegion | outputlookup previously_seen_aws_regions.csv - -``` diff --git a/docs/spec/baselines-properties-tags-default.md b/docs/spec/baselines-properties-tags-default.md deleted file mode 100644 index 6338be86cf..0000000000 --- a/docs/spec/baselines-properties-tags-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Baseline Schema Schema - -```txt -#/properties/tags#/properties/tags/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/baselines-properties-tags.md b/docs/spec/baselines-properties-tags.md deleted file mode 100644 index 622015ce22..0000000000 --- a/docs/spec/baselines-properties-tags.md +++ /dev/null @@ -1,47 +0,0 @@ -# Untitled object in Baseline Schema Schema - -```txt -#/properties/tags#/properties/tags -``` - -An array of key value pairs for tagging - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## tags Type - -`object` ([Details](baselines-properties-tags.md)) - -## tags Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -## tags Default Value - -The default value is: - -```json -{} -``` - -## tags Examples - -```yaml -analytic_story: suspicious_aws_ec2_activities -custom_key: custom_value - -``` - -# tags Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :--- | :------- | :---------- | :--------- | -| Additional Properties | Any | Optional | can be null | | - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/baselines-properties-version.md b/docs/spec/baselines-properties-version.md deleted file mode 100644 index 2d241c5920..0000000000 --- a/docs/spec/baselines-properties-version.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled integer in Baseline Schema Schema - -```txt -#/properties/version#/properties/version -``` - -version of baseline, e.g. 1 or 2 ... - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## version Type - -`integer` - -## version Examples - -```yaml -1 - -``` diff --git a/docs/spec/baselines.md b/docs/spec/baselines.md index 4b120c367a..035cb5a439 100644 --- a/docs/spec/baselines.md +++ b/docs/spec/baselines.md @@ -1,282 +1,30 @@ -# Baseline Schema Schema +# Baseline Schema -```txt -http://example.com/example.json -``` -schema for baselines +*schema for baselines* -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [baselines.spec.json](../../out/baselines.spec.json "open original schema") | -## Baseline Schema Type +## Properties -`object` ([Baseline Schema](baselines.md)) -# Baseline Schema Properties +- **`author`** *(string)*: Author of the baseline. Default: ``. -| Property | Type | Required | Nullable | Defined by | -| :------------------------------------ | :-------- | :------- | :------------- | :----------------------------------------------------------------------------------------------------------------------- | -| [author](#author) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-author.md "#/properties/author#/properties/author") | -| [date](#date) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-date.md "#/properties/date#/properties/date") | -| [description](#description) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-description.md "#/properties/description#/properties/description") | -| [how_to_implement](#how_to_implement) | `string` | Optional | cannot be null | [Baseline Schema](baselines-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement") | -| [id](#id) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-id.md "#/properties/id#/properties/id") | -| [name](#name) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-name-of-baseline.md "#/properties/name#/properties/name") | -| [search](#search) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-search.md "#/properties/search#/properties/search") | -| [tags](#tags) | `object` | Required | cannot be null | [Baseline Schema](baselines-properties-tags.md "#/properties/tags#/properties/tags") | -| [version](#version) | `integer` | Required | cannot be null | [Baseline Schema](baselines-properties-version.md "#/properties/version#/properties/version") | -| Additional Properties | Any | Optional | can be null | | +- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. -## author +- **`description`** *(string)*: A detailed description of the baseline . Default: ``. -Author of the baseline +- **`how_to_implement`** *(string)*: information about how to implement. Only needed for non standard implementations. Default: ``. -`author` +- **`id`** *(string)*: UUID as unique identifier. Default: ``. -* is required +- **`name`** *(string)*: Default: ``. -* Type: `string` +- **`search`** *(string)*: The Splunk search for the baseline. Default: ``. -* cannot be null +- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. -* defined in: [Baseline Schema](baselines-properties-author.md "#/properties/author#/properties/author") +- **`datamodel`** *(array)*: datamodel used in the search. Default: ``. -### author Type + - **Items** *(string)*: Must be one of: `['Endpoint', 'Network_Traffic', 'Authentication', 'Change', 'Change_Analysis', 'Email', 'Endpoint', 'Network_Resolution', 'Network_Sessions', 'Network_Traffic', 'UEBA', 'Updates', 'Vulnerabilities', 'Web']`. -`string` - -### author Examples - -```yaml -Bahvin Patel, Splunk - -``` - -## date - -date of creation or modification, format yyyy-mm-dd - -`date` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Baseline Schema](baselines-properties-date.md "#/properties/date#/properties/date") - -### date Type - -`string` - -### date Examples - -```yaml -'2019-12-06' - -``` - -## description - -A detailed description of the baseline - -`description` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Baseline Schema](baselines-properties-description.md "#/properties/description#/properties/description") - -### description Type - -`string` - -### description Examples - -```yaml ->- - This search looks for CloudTrail events where an AWS instance is started and - creates a baseline of most recent time (latest) and the first time (earliest) - we've seen this region in our dataset grouped by the value awsRegion for the - last 30 days - -``` - -## how_to_implement - -information about how to implement. Only needed for non standard implementations. - -`how_to_implement` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Baseline Schema](baselines-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement") - -### how_to_implement Type - -`string` - -### how_to_implement Examples - -```yaml ->- - This search requires Sysmon Logs and a Sysmon configuration, which includes - EventCode 10 for lsass.exe. - -``` - -## id - -UUID as unique identifier - -`id` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Baseline Schema](baselines-properties-id.md "#/properties/id#/properties/id") - -### id Type - -`string` - -### id Examples - -```yaml -fc0edc95-ff2b-48b0-9f6f-63da3789fd63 - -``` - -## name - - - -`name` - -* is required - -* Type: `string` ([Name of baseline](baselines-properties-name-of-baseline.md)) - -* cannot be null - -* defined in: [Baseline Schema](baselines-properties-name-of-baseline.md "#/properties/name#/properties/name") - -### name Type - -`string` ([Name of baseline](baselines-properties-name-of-baseline.md)) - -### name Examples - -```yaml -Previously Seen AWS Regions - -``` - -## search - -The Splunk search for the baseline - -`search` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Baseline Schema](baselines-properties-search.md "#/properties/search#/properties/search") - -### search Type - -`string` - -### search Examples - -```yaml ->- - cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as - latest by awsRegion | outputlookup previously_seen_aws_regions.csv - -``` - -## tags - -An array of key value pairs for tagging - -`tags` - -* is required - -* Type: `object` ([Details](baselines-properties-tags.md)) - -* cannot be null - -* defined in: [Baseline Schema](baselines-properties-tags.md "#/properties/tags#/properties/tags") - -### tags Type - -`object` ([Details](baselines-properties-tags.md)) - -### tags Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -### tags Default Value - -The default value is: - -```json -{} -``` - -### tags Examples - -```yaml -analytic_story: suspicious_aws_ec2_activities -custom_key: custom_value - -``` - -## version - -version of baseline, e.g. 1 or 2 ... - -`version` - -* is required - -* Type: `integer` - -* cannot be null - -* defined in: [Baseline Schema](baselines-properties-version.md "#/properties/version#/properties/version") - -### version Type - -`integer` - -### version Examples - -```yaml -1 - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema +- **`version`** *(integer)*: version of baseline, e.g. 1 or 2 ... Default: `0`. diff --git a/docs/spec/deployments-default.md b/docs/spec/deployments-default.md deleted file mode 100644 index 1a7b518209..0000000000 --- a/docs/spec/deployments-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -http://example.com/example.json#/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-alert_action-default.md b/docs/spec/deployments-properties-alert_action-default.md deleted file mode 100644 index 55e2308c13..0000000000 --- a/docs/spec/deployments-properties-alert_action-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -#/properties/alert_action#/properties/alert_action/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-email-default.md b/docs/spec/deployments-properties-alert_action-properties-email-default.md deleted file mode 100644 index c8b32006bb..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-email-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/email#/properties/alert_action/properties/email/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md deleted file mode 100644 index e01fa30a39..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message -``` - -message of email - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## message Type - -`string` - -## message Examples - -```yaml -Splunk Alert $name$ triggered %fields% - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md deleted file mode 100644 index e72fd29af3..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject -``` - -Subject of email - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## subject Type - -`string` - -## subject Examples - -```yaml -Splunk Alert $name$ - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md deleted file mode 100644 index 0bef21838f..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to -``` - -Recipient of email - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## to Type - -`string` - -## to Examples - -```yaml -test@test.com - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-email.md b/docs/spec/deployments-properties-alert_action-properties-email.md deleted file mode 100644 index 070e95ee64..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-email.md +++ /dev/null @@ -1,120 +0,0 @@ -# Untitled object in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/email#/properties/alert_action/properties/email -``` - -By enabling it, an email is sent with the results - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## email Type - -`object` ([Details](deployments-properties-alert_action-properties-email.md)) - -## email Default Value - -The default value is: - -```json -{} -``` - -## email Examples - -```yaml -message: Splunk Alert $name$ triggered %fields% -subject: Splunk Alert $name$ -to: test@test.com - -``` - -# email Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :------- | :------- | :------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| [message](#message) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-message.md "#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message") | -| [subject](#subject) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-subject.md "#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject") | -| [to](#to) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-to.md "#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to") | -| Additional Properties | Any | Optional | can be null | | - -## message - -message of email - -`message` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-message.md "#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message") - -### message Type - -`string` - -### message Examples - -```yaml -Splunk Alert $name$ triggered %fields% - -``` - -## subject - -Subject of email - -`subject` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-subject.md "#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject") - -### subject Type - -`string` - -### subject Examples - -```yaml -Splunk Alert $name$ - -``` - -## to - -Recipient of email - -`to` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-to.md "#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to") - -### to Type - -`string` - -### to Examples - -```yaml -test@test.com - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-alert_action-properties-index-default.md b/docs/spec/deployments-properties-alert_action-properties-index-default.md deleted file mode 100644 index 0acb5762ae..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-index-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/index#/properties/alert_action/properties/index/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md b/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md deleted file mode 100644 index be9a681a97..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name -``` - -Name of the index - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## name Type - -`string` - -## name Examples - -```yaml -asx - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-index.md b/docs/spec/deployments-properties-alert_action-properties-index.md deleted file mode 100644 index edc5070f0e..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-index.md +++ /dev/null @@ -1,66 +0,0 @@ -# Untitled object in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/index#/properties/alert_action/properties/index -``` - -By enabling it, the results are stored in another index - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## index Type - -`object` ([Details](deployments-properties-alert_action-properties-index.md)) - -## index Default Value - -The default value is: - -```json -{} -``` - -## index Examples - -```yaml -name: asx - -``` - -# index Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| [name](#name) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-index-properties-name.md "#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name") | -| Additional Properties | Any | Optional | can be null | | - -## name - -Name of the index - -`name` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-index-properties-name.md "#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name") - -### name Type - -`string` - -### name Examples - -```yaml -asx - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-default.md b/docs/spec/deployments-properties-alert_action-properties-notable-default.md deleted file mode 100644 index 84226f6fc7..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-notable-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md deleted file mode 100644 index 23882cd515..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description -``` - -Rule description of the notable event - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## rule_description Type - -`string` - -## rule_description Examples - -```yaml -'%description%' - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md deleted file mode 100644 index 9d5eb151c2..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title -``` - -Rule title of the notable event - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## rule_title Type - -`string` - -## rule_title Examples - -```yaml -'%name%' - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-notable.md b/docs/spec/deployments-properties-alert_action-properties-notable.md deleted file mode 100644 index d117f24ef9..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-notable.md +++ /dev/null @@ -1,93 +0,0 @@ -# Untitled object in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable -``` - -By enabling it, a notable is generated - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## notable Type - -`object` ([Details](deployments-properties-alert_action-properties-notable.md)) - -## notable Default Value - -The default value is: - -```json -{} -``` - -## notable Examples - -```yaml -rule_description: '%description%' -rule_title: '%name%' - -``` - -# notable Properties - -| Property | Type | Required | Nullable | Defined by | -| :------------------------------------ | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| [rule_description](#rule_description) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_description.md "#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description") | -| [rule_title](#rule_title) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_title.md "#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title") | -| Additional Properties | Any | Optional | can be null | | - -## rule_description - -Rule description of the notable event - -`rule_description` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_description.md "#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description") - -### rule_description Type - -`string` - -### rule_description Examples - -```yaml -'%description%' - -``` - -## rule_title - -Rule title of the notable event - -`rule_title` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_title.md "#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title") - -### rule_title Type - -`string` - -### rule_title Examples - -```yaml -'%name%' - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-alert_action.md b/docs/spec/deployments-properties-alert_action.md deleted file mode 100644 index f5fa09670a..0000000000 --- a/docs/spec/deployments-properties-alert_action.md +++ /dev/null @@ -1,153 +0,0 @@ -# Untitled object in Deployment Schema Schema - -```txt -#/properties/alert_action#/properties/alert_action -``` - -Set alert action parameter for search - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## alert_action Type - -`object` ([Details](deployments-properties-alert_action.md)) - -## alert_action Default Value - -The default value is: - -```json -{} -``` - -## alert_action Examples - -```yaml -email: - message: Splunk Alert $name$ triggered %fields% - subject: Splunk Alert $name$ - to: test@test.com -index: - name: asx -notable: - rule_description: '%description%' - rule_title: '%name%' - -``` - -# alert_action Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| [email](#email) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email.md "#/properties/alert_action/properties/email#/properties/alert_action/properties/email") | -| [index](#index) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-index.md "#/properties/alert_action/properties/index#/properties/alert_action/properties/index") | -| [notable](#notable) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable.md "#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable") | -| Additional Properties | Any | Optional | can be null | | - -## email - -By enabling it, an email is sent with the results - -`email` - -* is optional - -* Type: `object` ([Details](deployments-properties-alert_action-properties-email.md)) - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email.md "#/properties/alert_action/properties/email#/properties/alert_action/properties/email") - -### email Type - -`object` ([Details](deployments-properties-alert_action-properties-email.md)) - -### email Default Value - -The default value is: - -```json -{} -``` - -### email Examples - -```yaml -message: Splunk Alert $name$ triggered %fields% -subject: Splunk Alert $name$ -to: test@test.com - -``` - -## index - -By enabling it, the results are stored in another index - -`index` - -* is optional - -* Type: `object` ([Details](deployments-properties-alert_action-properties-index.md)) - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-index.md "#/properties/alert_action/properties/index#/properties/alert_action/properties/index") - -### index Type - -`object` ([Details](deployments-properties-alert_action-properties-index.md)) - -### index Default Value - -The default value is: - -```json -{} -``` - -### index Examples - -```yaml -name: asx - -``` - -## notable - -By enabling it, a notable is generated - -`notable` - -* is optional - -* Type: `object` ([Details](deployments-properties-alert_action-properties-notable.md)) - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable.md "#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable") - -### notable Type - -`object` ([Details](deployments-properties-alert_action-properties-notable.md)) - -### notable Default Value - -The default value is: - -```json -{} -``` - -### notable Examples - -```yaml -rule_description: '%description%' -rule_title: '%name%' - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-scheduling-default.md b/docs/spec/deployments-properties-scheduling-default.md deleted file mode 100644 index f39294b0a3..0000000000 --- a/docs/spec/deployments-properties-scheduling-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -#/properties/scheduling#/properties/scheduling/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md b/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md deleted file mode 100644 index 52cbbfae51..0000000000 --- a/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule -``` - -Cron schedule to schedule the Splunk searches. - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## cron_schedule Type - -`string` - -## cron_schedule Examples - -```yaml -'*/10 * * * *' - -``` diff --git a/docs/spec/deployments-properties-scheduling-properties-earliest_time.md b/docs/spec/deployments-properties-scheduling-properties-earliest_time.md deleted file mode 100644 index 5808ddd97f..0000000000 --- a/docs/spec/deployments-properties-scheduling-properties-earliest_time.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time -``` - -earliest time of search - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## earliest_time Type - -`string` - -## earliest_time Examples - -```yaml -'-10m' - -``` diff --git a/docs/spec/deployments-properties-scheduling-properties-latest_time.md b/docs/spec/deployments-properties-scheduling-properties-latest_time.md deleted file mode 100644 index 566141825e..0000000000 --- a/docs/spec/deployments-properties-scheduling-properties-latest_time.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time -``` - -latest time of search - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## latest_time Type - -`string` - -## latest_time Examples - -```yaml -now - -``` diff --git a/docs/spec/deployments-properties-scheduling-properties-schedule_window.md b/docs/spec/deployments-properties-scheduling-properties-schedule_window.md deleted file mode 100644 index 41aad873d4..0000000000 --- a/docs/spec/deployments-properties-scheduling-properties-schedule_window.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window -``` - -schedule window for search - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## schedule_window Type - -`string` - -## schedule_window Examples - -```yaml -auto - -``` diff --git a/docs/spec/deployments-properties-scheduling.md b/docs/spec/deployments-properties-scheduling.md deleted file mode 100644 index cf64fb3727..0000000000 --- a/docs/spec/deployments-properties-scheduling.md +++ /dev/null @@ -1,147 +0,0 @@ -# Untitled object in Deployment Schema Schema - -```txt -#/properties/scheduling#/properties/scheduling -``` - -allows to set scheduling parameter - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## scheduling Type - -`object` ([Details](deployments-properties-scheduling.md)) - -## scheduling Default Value - -The default value is: - -```json -{} -``` - -## scheduling Examples - -```yaml -cron_schedule: '*/10 * * * *' -earliest_time: '-10m' -latest_time: now -schedule_window: auto - -``` - -# scheduling Properties - -| Property | Type | Required | Nullable | Defined by | -| :---------------------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| [cron_schedule](#cron_schedule) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-cron_schedule.md "#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule") | -| [earliest_time](#earliest_time) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-earliest_time.md "#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time") | -| [latest_time](#latest_time) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-latest_time.md "#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time") | -| [schedule_window](#schedule_window) | `string` | Optional | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-schedule_window.md "#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window") | -| Additional Properties | Any | Optional | can be null | | - -## cron_schedule - -Cron schedule to schedule the Splunk searches. - -`cron_schedule` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-scheduling-properties-cron_schedule.md "#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule") - -### cron_schedule Type - -`string` - -### cron_schedule Examples - -```yaml -'*/10 * * * *' - -``` - -## earliest_time - -earliest time of search - -`earliest_time` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-scheduling-properties-earliest_time.md "#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time") - -### earliest_time Type - -`string` - -### earliest_time Examples - -```yaml -'-10m' - -``` - -## latest_time - -latest time of search - -`latest_time` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-scheduling-properties-latest_time.md "#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time") - -### latest_time Type - -`string` - -### latest_time Examples - -```yaml -now - -``` - -## schedule_window - -schedule window for search - -`schedule_window` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-scheduling-properties-schedule_window.md "#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window") - -### schedule_window Type - -`string` - -### schedule_window Examples - -```yaml -auto - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments.md b/docs/spec/deployments.md index 4d1597e99c..ac112057c9 100644 --- a/docs/spec/deployments.md +++ b/docs/spec/deployments.md @@ -1,258 +1,48 @@ -# Deployment Schema Schema +# Deployment Schema -```txt -http://example.com/example.json -``` -schema for deployment +*schema for deployment* -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------ | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json](../../out/deployments.spec.json "open original schema") | -## Deployment Schema Type +## Properties -`object` ([Deployment Schema](deployments.md)) -## Deployment Schema Default Value +- **`alert_action`** *(object)*: Set alert action parameter for search. Can contain additional properties. Default: `{}`. -The default value is: + - **`email`** *(object)*: By enabling it, an email is sent with the results. Can contain additional properties. Default: `{}`. -```json -{} -``` + - **`message`** *(string)*: message of email. Default: ``. -# Deployment Schema Properties + - **`subject`** *(string)*: Subject of email. Default: ``. -| Property | Type | Required | Nullable | Defined by | -| :---------------------------- | :------- | :------- | :------------- | :--------------------------------------------------------------------------------------------------------------- | -| [alert_action](#alert_action) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action.md "#/properties/alert_action#/properties/alert_action") | -| [date](#date) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-date.md "#/properties/date#/properties/date") | -| [description](#description) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-description.md "#/properties/description#/properties/description") | -| [id](#id) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-id.md "#/properties/id#/properties/id") | -| [name](#name) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-name.md "#/properties/name#/properties/name") | -| [scheduling](#scheduling) | `object` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling.md "#/properties/scheduling#/properties/scheduling") | -| [tags](#tags) | `object` | Required | cannot be null | [Deployment Schema](deployments-properties-tags.md "#/properties/tags#/properties/tags") | -| Additional Properties | Any | Optional | can be null | | + - **`to`** *(string)*: Recipient of email. Default: ``. -## alert_action + - **`index`** *(object)*: By enabling it, the results are stored in another index. Can contain additional properties. Default: `{}`. -Set alert action parameter for search + - **`name`** *(string)*: Name of the index. Default: ``. -`alert_action` + - **`notable`** *(object)*: By enabling it, a notable is generated. Can contain additional properties. Default: `{}`. -* is optional + - **`rule_description`** *(string)*: Rule description of the notable event. Default: ``. -* Type: `object` ([Details](deployments-properties-alert_action.md)) + - **`rule_title`** *(string)*: Rule title of the notable event. Default: ``. -* cannot be null +- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. -* defined in: [Deployment Schema](deployments-properties-alert_action.md "#/properties/alert_action#/properties/alert_action") +- **`description`** *(string)*: description of the deployment configuration. Default: ``. -### alert_action Type +- **`id`** *(string)*: uuid as unique identifier. Default: ``. -`object` ([Details](deployments-properties-alert_action.md)) +- **`name`** *(string)*: Name of deployment configuration. Default: ``. -### alert_action Default Value +- **`scheduling`** *(object)*: allows to set scheduling parameter. Can contain additional properties. Default: `{}`. -The default value is: + - **`cron_schedule`** *(string)*: Cron schedule to schedule the Splunk searches. Default: ``. -```json -{} -``` + - **`earliest_time`** *(string)*: earliest time of search. Default: ``. -### alert_action Examples + - **`latest_time`** *(string)*: latest time of search. Default: ``. -```yaml -email: - message: Splunk Alert $name$ triggered %fields% - subject: Splunk Alert $name$ - to: test@test.com -index: - name: asx -notable: - rule_description: '%description%' - rule_title: '%name%' + - **`schedule_window`** *(string)*: schedule window for search. Default: ``. -``` - -## date - -date of creation or modification, format yyyy-mm-dd - -`date` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-date.md "#/properties/date#/properties/date") - -### date Type - -`string` - -### date Examples - -```yaml -'2019-12-06' - -``` - -## description - -description of the deployment configuration - -`description` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-description.md "#/properties/description#/properties/description") - -### description Type - -`string` - -### description Examples - -```yaml ->- - This deployment configuration provides a standard scheduling policy over all - rules. - -``` - -## id - -uuid as unique identifier - -`id` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-id.md "#/properties/id#/properties/id") - -### id Type - -`string` - -### id Examples - -```yaml -fb4c31b0-13e8-4155-8aa5-24de4b8d6717 - -``` - -## name - -Name of deployment configuration - -`name` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-name.md "#/properties/name#/properties/name") - -### name Type - -`string` - -### name Examples - -```yaml -Deployment Configuration all Detections - -``` - -## scheduling - -allows to set scheduling parameter - -`scheduling` - -* is required - -* Type: `object` ([Details](deployments-properties-scheduling.md)) - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-scheduling.md "#/properties/scheduling#/properties/scheduling") - -### scheduling Type - -`object` ([Details](deployments-properties-scheduling.md)) - -### scheduling Default Value - -The default value is: - -```json -{} -``` - -### scheduling Examples - -```yaml -cron_schedule: '*/10 * * * *' -earliest_time: '-10m' -latest_time: now -schedule_window: auto - -``` - -## tags - -An array of key value pairs for tagging - -`tags` - -* is required - -* Type: `object` ([Details](deployments-properties-tags.md)) - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-tags.md "#/properties/tags#/properties/tags") - -### tags Type - -`object` ([Details](deployments-properties-tags.md)) - -### tags Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -### tags Default Value - -The default value is: - -```json -{} -``` - -### tags Examples - -```yaml -analytic_story: credential_dumping - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema +- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. diff --git a/docs/spec/detections-properties-known_false_positives.md b/docs/spec/detections-properties-known_false_positives.md deleted file mode 100644 index ae7c1bafbd..0000000000 --- a/docs/spec/detections-properties-known_false_positives.md +++ /dev/null @@ -1,24 +0,0 @@ -# Untitled string in Detection Schema Schema - -```txt -#/properties/knwon_false_positives#/properties/known_false_positives -``` - -known false postives - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | - -## known_false_positives Type - -`string` - -## known_false_positives Examples - -```yaml ->- - Administrators can create memory dumps for debugging purposes, but memory - dumps of the LSASS process would be unusual. - -``` diff --git a/docs/spec/detections-properties-references-the-items-schema.md b/docs/spec/detections-properties-references-the-items-schema.md deleted file mode 100644 index c184428f27..0000000000 --- a/docs/spec/detections-properties-references-the-items-schema.md +++ /dev/null @@ -1,23 +0,0 @@ -# The Items Schema Schema - -```txt -#/properties/references/items#/properties/references/items -``` - -An explanation about the purpose of this instance. - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | - -## items Type - -`string` ([The Items Schema](detections-properties-references-the-items-schema.md)) - -## items Examples - -```yaml ->- - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -``` diff --git a/docs/spec/detections-properties-references.md b/docs/spec/detections-properties-references.md deleted file mode 100644 index 07618fc3f0..0000000000 --- a/docs/spec/detections-properties-references.md +++ /dev/null @@ -1,31 +0,0 @@ -# Untitled array in Detection Schema Schema - -```txt -#/properties/references#/properties/references -``` - -A list of references for this detection - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | - -## references Type - -`string[]` ([The Items Schema](detections-properties-references-the-items-schema.md)) - -## references Default Value - -The default value is: - -```json -[] -``` - -## references Examples - -```yaml -- >- - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -``` diff --git a/docs/spec/detections-properties-type-items.md b/docs/spec/detections-properties-type-items.md deleted file mode 100644 index 226980c0e6..0000000000 --- a/docs/spec/detections-properties-type-items.md +++ /dev/null @@ -1,24 +0,0 @@ -# Untitled string in Detection Schema Schema - -```txt -#/properties/type#/properties/type/items -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | - -## items Type - -`string` - -## items Constraints - -**enum**: the value of this property must be equal to one of the following values: - -| Value | Explanation | -| :------------ | :---------- | -| `"batch"` | | -| `"streaming"` | | diff --git a/docs/spec/detections-properties-type.md b/docs/spec/detections-properties-type.md deleted file mode 100644 index 98c36f20ac..0000000000 --- a/docs/spec/detections-properties-type.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Detection Schema Schema - -```txt -#/properties/type#/properties/type -``` - -type of detection - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | - -## type Type - -`string` - -## type Examples - -```yaml -streaming - -``` diff --git a/docs/spec/detections.md b/docs/spec/detections.md index a07f254a09..ce5455c13c 100644 --- a/docs/spec/detections.md +++ b/docs/spec/detections.md @@ -1,384 +1,40 @@ -# Detection Schema Schema +# Detection Schema -```txt -http://example.com/example.json -``` -schema for detections +*schema for detections* -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [detections.spec.json](../../out/detections.spec.json "open original schema") | -## Detection Schema Type +## Properties -`object` ([Detection Schema](detections.md)) -# Detection Schema Properties +- **`author`** *(string)*: Author of the detection. Default: ``. -| Property | Type | Required | Nullable | Defined by | -| :---------------------------------------------- | :-------- | :------- | :------------- | :---------------------------------------------------------------------------------------------------------------------------------------- | -| [author](#author) | `string` | Required | cannot be null | [Detection Schema](detections-properties-author.md "#/properties/author#/properties/author") | -| [date](#date) | `string` | Required | cannot be null | [Detection Schema](detections-properties-date.md "#/properties/date#/properties/date") | -| [description](#description) | `string` | Required | cannot be null | [Detection Schema](detections-properties-description.md "#/properties/description#/properties/description") | -| [how_to_implement](#how_to_implement) | `string` | Optional | cannot be null | [Detection Schema](detections-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement") | -| [id](#id) | `string` | Required | cannot be null | [Detection Schema](detections-properties-id.md "#/properties/id#/properties/id") | -| [known_false_positives](#known_false_positives) | `string` | Required | cannot be null | [Detection Schema](detections-properties-known_false_positives.md "#/properties/knwon_false_positives#/properties/known_false_positives") | -| [name](#name) | `string` | Required | cannot be null | [Detection Schema](detections-properties-name-of-detection.md "#/properties/name#/properties/name") | -| [references](#references) | `array` | Optional | cannot be null | [Detection Schema](detections-properties-references.md "#/properties/references#/properties/references") | -| [search](#search) | `string` | Required | cannot be null | [Detection Schema](detections-properties-search.md "#/properties/search#/properties/search") | -| [tags](#tags) | `object` | Required | cannot be null | [Detection Schema](detections-properties-tags.md "#/properties/tags#/properties/tags") | -| [type](#type) | `string` | Required | cannot be null | [Detection Schema](detections-properties-type.md "#/properties/type#/properties/type") | -| [version](#version) | `integer` | Required | cannot be null | [Detection Schema](detections-properties-version.md "#/properties/version#/properties/version") | -| Additional Properties | Any | Optional | can be null | | +- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. -## author +- **`description`** *(string)*: A detailed description of the detection. Default: ``. -Author of the detection +- **`how_to_implement`** *(string)*: information about how to implement. Only needed for non standard implementations. Default: ``. -`author` +- **`id`** *(string)*: UUID as unique identifier. Default: ``. -* is required +- **`known_false_positives`** *(string)*: known false postives. Default: ``. -* Type: `string` +- **`name`** *(string)*: Default: ``. -* cannot be null +- **`references`** *(array)*: A list of references for this detection. Default: `[]`. -* defined in: [Detection Schema](detections-properties-author.md "#/properties/author#/properties/author") + - **Items** *(string)*: An explanation about the purpose of this instance. Default: ``. -### author Type +- **`search`** *(string)*: The Splunk search for the detection. Default: ``. -`string` +- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. -### author Examples +- **`type`** *(string)*: type of detection. Default: ``. -```yaml -Patrick Bareiss, Splunk + - **Items** *(string)*: Must be one of: `['batch', 'streaming']`. -``` +- **`datamodel`** *(array)*: datamodel used in the search. Default: ``. -## date + - **Items** *(string)*: Must be one of: `['Endpoint', 'Network_Traffic', 'Authentication', 'Change', 'Change_Analysis', 'Email', 'Endpoint', 'Network_Resolution', 'Network_Sessions', 'Network_Traffic', 'UEBA', 'Updates', 'Vulnerabilities', 'Web']`. -date of creation or modification, format yyyy-mm-dd - -`date` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Detection Schema](detections-properties-date.md "#/properties/date#/properties/date") - -### date Type - -`string` - -### date Examples - -```yaml -'2019-12-06' - -``` - -## description - -A detailed description of the detection - -`description` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Detection Schema](detections-properties-description.md "#/properties/description#/properties/description") - -### description Type - -`string` - -### description Examples - -```yaml ->- - dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain - a memory dump of a process. This search detects the usage of this DLL for - creating a memory dump of LSASS process. Memory dumps of the LSASS process can - be created with tools such as Windows Task Manager or procdump. - -``` - -## how_to_implement - -information about how to implement. Only needed for non standard implementations. - -`how_to_implement` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Detection Schema](detections-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement") - -### how_to_implement Type - -`string` - -### how_to_implement Examples - -```yaml ->- - This search requires Sysmon Logs and a Sysmon configuration, which includes - EventCode 10 for lsass.exe. - -``` - -## id - -UUID as unique identifier - -`id` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Detection Schema](detections-properties-id.md "#/properties/id#/properties/id") - -### id Type - -`string` - -### id Examples - -```yaml -fb4c31b0-13e8-4155-8aa5-24de4b8d6717 - -``` - -## known_false_positives - -known false postives - -`known_false_positives` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Detection Schema](detections-properties-known_false_positives.md "#/properties/knwon_false_positives#/properties/known_false_positives") - -### known_false_positives Type - -`string` - -### known_false_positives Examples - -```yaml ->- - Administrators can create memory dumps for debugging purposes, but memory - dumps of the LSASS process would be unusual. - -``` - -## name - - - -`name` - -* is required - -* Type: `string` ([Name of detection](detections-properties-name-of-detection.md)) - -* cannot be null - -* defined in: [Detection Schema](detections-properties-name-of-detection.md "#/properties/name#/properties/name") - -### name Type - -`string` ([Name of detection](detections-properties-name-of-detection.md)) - -### name Examples - -```yaml -Access LSASS Memory for Dump Creation - -``` - -## references - -A list of references for this detection - -`references` - -* is optional - -* Type: `string[]` ([The Items Schema](detections-properties-references-the-items-schema.md)) - -* cannot be null - -* defined in: [Detection Schema](detections-properties-references.md "#/properties/references#/properties/references") - -### references Type - -`string[]` ([The Items Schema](detections-properties-references-the-items-schema.md)) - -### references Default Value - -The default value is: - -```json -[] -``` - -### references Examples - -```yaml -- >- - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -``` - -## search - -The Splunk search for the detection - -`search` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Detection Schema](detections-properties-search.md "#/properties/search#/properties/search") - -### search Type - -`string` - -### search Examples - -```yaml ->- - `sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR - CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as - lastTime by Computer, TargetImage, TargetProcessId, SourceImage, - SourceProcessId | rename Computer as dest | - `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | - `access_lsass_memory_for_dump_creation_filter` - -``` - -## tags - -An array of key value pairs for tagging - -`tags` - -* is required - -* Type: `object` ([Details](detections-properties-tags.md)) - -* cannot be null - -* defined in: [Detection Schema](detections-properties-tags.md "#/properties/tags#/properties/tags") - -### tags Type - -`object` ([Details](detections-properties-tags.md)) - -### tags Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -### tags Default Value - -The default value is: - -```json -{} -``` - -### tags Examples - -```yaml -analytic_story: credential_dumping -kill_chain_phases: Action on Objectives -mitre_attack_id: T1078.004 -cis20: CIS 13 -nist: DE.DP -security domain: network -asset_type: AWS Instance -risk_object: user -risk_object_type: network_artifacts -risk score: '60' -custom_key: custom_value - -``` - -## type - -type of detection - -`type` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Detection Schema](detections-properties-type.md "#/properties/type#/properties/type") - -### type Type - -`string` - -### type Examples - -```yaml -streaming - -``` - -## version - -version of detection, e.g. 1 or 2 ... - -`version` - -* is required - -* Type: `integer` - -* cannot be null - -* defined in: [Detection Schema](detections-properties-version.md "#/properties/version#/properties/version") - -### version Type - -`integer` - -### version Examples - -```yaml -2 - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema +- **`version`** *(integer)*: version of detection, e.g. 1 or 2 ... Default: `0`. diff --git a/docs/spec/lookups-oneof-0.md b/docs/spec/lookups-oneof-0.md deleted file mode 100644 index 78403ade45..0000000000 --- a/docs/spec/lookups-oneof-0.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/oneOf/0 -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## 0 Type - -unknown diff --git a/docs/spec/lookups-oneof-1.md b/docs/spec/lookups-oneof-1.md deleted file mode 100644 index 4b493c4f17..0000000000 --- a/docs/spec/lookups-oneof-1.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/oneOf/1 -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## 1 Type - -unknown diff --git a/docs/spec/lookups-properties-case_sensitive_match.md b/docs/spec/lookups-properties-case_sensitive_match.md deleted file mode 100644 index b067278e67..0000000000 --- a/docs/spec/lookups-properties-case_sensitive_match.md +++ /dev/null @@ -1,31 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/case_sensitive_match -``` - -What the macro is intended to filter - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## case_sensitive_match Type - -`string` - -## case_sensitive_match Constraints - -**enum**: the value of this property must be equal to one of the following values: - -| Value | Explanation | -| :-------- | :---------- | -| `"true"` | | -| `"false"` | | - -## case_sensitive_match Examples - -```yaml -'true' - -``` diff --git a/docs/spec/lookups-properties-collection.md b/docs/spec/lookups-properties-collection.md deleted file mode 100644 index 62b7028b4d..0000000000 --- a/docs/spec/lookups-properties-collection.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/collection -``` - -Name of the collection to use for this lookup - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## collection Type - -`string` - -## collection Examples - -```yaml -prohibited_apps_launching_cmd - -``` diff --git a/docs/spec/lookups-properties-default_match.md b/docs/spec/lookups-properties-default_match.md deleted file mode 100644 index 46369bbdda..0000000000 --- a/docs/spec/lookups-properties-default_match.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/default_match -``` - -The default value if no match is found - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## default_match Type - -`string` - -## default_match Examples - -```yaml -'true' - -``` diff --git a/docs/spec/lookups-properties-description.md b/docs/spec/lookups-properties-description.md deleted file mode 100644 index e9fc0cfe11..0000000000 --- a/docs/spec/lookups-properties-description.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/description -``` - -The description of this lookup - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## description Type - -`string` - -## description Examples - -```yaml -This lookup contains file names that exist in the Windows\System32 directory - -``` diff --git a/docs/spec/lookups-properties-fields_list.md b/docs/spec/lookups-properties-fields_list.md deleted file mode 100644 index e0a699ee17..0000000000 --- a/docs/spec/lookups-properties-fields_list.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/fields_list -``` - -A comma and space separated list of field names - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## fields_list Type - -`string` - -## fields_list Examples - -```yaml -_key, dest, process_name - -``` diff --git a/docs/spec/lookups-properties-filename.md b/docs/spec/lookups-properties-filename.md deleted file mode 100644 index 80584c0215..0000000000 --- a/docs/spec/lookups-properties-filename.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/filename -``` - -The name of the file to use for this lookup - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## filename Type - -`string` - -## filename Examples - -```yaml -prohibited_apps_launching_cmd.csv - -``` diff --git a/docs/spec/lookups-properties-filter.md b/docs/spec/lookups-properties-filter.md deleted file mode 100644 index ad1a68ff22..0000000000 --- a/docs/spec/lookups-properties-filter.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/filter -``` - -Use this attribute to improve search performance when working with significantly large KV - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## filter Type - -`string` - -## filter Examples - -```yaml -dest="SPLK_*" - -``` diff --git a/docs/spec/lookups-properties-match_type.md b/docs/spec/lookups-properties-match_type.md deleted file mode 100644 index 3ca467f6a8..0000000000 --- a/docs/spec/lookups-properties-match_type.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/match_type -``` - -A comma and space-delimited list of \(\) specification to allow for non-exact matching - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## match_type Type - -`string` - -## match_type Examples - -```yaml -WILDCARD(process) - -``` diff --git a/docs/spec/lookups-properties-max_matches.md b/docs/spec/lookups-properties-max_matches.md deleted file mode 100644 index 652395498b..0000000000 --- a/docs/spec/lookups-properties-max_matches.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled integer in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/max_matches -``` - -The maximum number of possible matches for each input lookup value - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## max_matches Type - -`integer` - -## max_matches Examples - -```yaml -'100' - -``` diff --git a/docs/spec/lookups-properties-min_matches.md b/docs/spec/lookups-properties-min_matches.md deleted file mode 100644 index e950b97fbd..0000000000 --- a/docs/spec/lookups-properties-min_matches.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled integer in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/min_matches -``` - -Minimum number of possible matches for each input lookup value - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## min_matches Type - -`integer` - -## min_matches Examples - -```yaml -'1' - -``` diff --git a/docs/spec/lookups-properties-name.md b/docs/spec/lookups-properties-name.md deleted file mode 100644 index ca27b5144c..0000000000 --- a/docs/spec/lookups-properties-name.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/name -``` - -The name of the lookup to be used in searches - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## name Type - -`string` - -## name Examples - -```yaml -isWindowsSystemFile_lookup - -``` diff --git a/docs/spec/lookups.md b/docs/spec/lookups.md index 79b6e284e3..b4008bda0f 100644 --- a/docs/spec/lookups.md +++ b/docs/spec/lookups.md @@ -1,321 +1,30 @@ -# Lookup Manifest Schema +# Lookup Manifest -```txt -https://api.splunkresearch.com/schemas/lookups.json -``` -A object that defines a lookup file and its properties. +*A object that defines a lookup file and its properties.* -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [lookups.spec.json](../../out/lookups.spec.json "open original schema") | -## Lookup Manifest Type +## Properties -`object` ([Lookup Manifest](lookups.md)) -one (and only one) of +- **`case_sensitive_match`** *(string)*: What the macro is intended to filter. Must be one of: `['true', 'false']`. -* [Untitled undefined type in Lookup Manifest](lookups-oneof-0.md "check type definition") +- **`collection`** *(string)*: Name of the collection to use for this lookup. -* [Untitled undefined type in Lookup Manifest](lookups-oneof-1.md "check type definition") +- **`default_match`** *(string)*: The default value if no match is found. -# Lookup Manifest Properties +- **`description`** *(string)*: The description of this lookup. -| Property | Type | Required | Nullable | Defined by | -| :-------------------------------------------- | :-------- | :------- | :------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------- | -| [case_sensitive_match](#case_sensitive_match) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-case_sensitive_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/case_sensitive_match") | -| [collection](#collection) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-collection.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/collection") | -| [default_match](#default_match) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-default_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/default_match") | -| [description](#description) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-description.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/description") | -| [fields_list](#fields_list) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-fields_list.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/fields_list") | -| [filename](#filename) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-filename.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filename") | -| [filter](#filter) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-filter.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filter") | -| [match_type](#match_type) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-match_type.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/match_type") | -| [max_matches](#max_matches) | `integer` | Optional | cannot be null | [Lookup Manifest](lookups-properties-max_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/max_matches") | -| [min_matches](#min_matches) | `integer` | Optional | cannot be null | [Lookup Manifest](lookups-properties-min_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/min_matches") | -| [name](#name) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-name.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/name") | +- **`fields_list`** *(string)*: A comma and space separated list of field names. -## case_sensitive_match +- **`filename`** *(string)*: The name of the file to use for this lookup. -What the macro is intended to filter +- **`filter`** *(string)*: Use this attribute to improve search performance when working with significantly large KV. -`case_sensitive_match` +- **`match_type`** *(string)*: A comma and space-delimited list of () specification to allow for non-exact matching. -* is optional +- **`max_matches`** *(integer)*: The maximum number of possible matches for each input lookup value. -* Type: `string` +- **`min_matches`** *(integer)*: Minimum number of possible matches for each input lookup value. -* cannot be null - -* defined in: [Lookup Manifest](lookups-properties-case_sensitive_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/case_sensitive_match") - -### case_sensitive_match Type - -`string` - -### case_sensitive_match Constraints - -**enum**: the value of this property must be equal to one of the following values: - -| Value | Explanation | -| :-------- | :---------- | -| `"true"` | | -| `"false"` | | - -### case_sensitive_match Examples - -```yaml -'true' - -``` - -## collection - -Name of the collection to use for this lookup - -`collection` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Lookup Manifest](lookups-properties-collection.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/collection") - -### collection Type - -`string` - -### collection Examples - -```yaml -prohibited_apps_launching_cmd - -``` - -## default_match - -The default value if no match is found - -`default_match` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Lookup Manifest](lookups-properties-default_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/default_match") - -### default_match Type - -`string` - -### default_match Examples - -```yaml -'true' - -``` - -## description - -The description of this lookup - -`description` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Lookup Manifest](lookups-properties-description.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/description") - -### description Type - -`string` - -### description Examples - -```yaml -This lookup contains file names that exist in the Windows\System32 directory - -``` - -## fields_list - -A comma and space separated list of field names - -`fields_list` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Lookup Manifest](lookups-properties-fields_list.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/fields_list") - -### fields_list Type - -`string` - -### fields_list Examples - -```yaml -_key, dest, process_name - -``` - -## filename - -The name of the file to use for this lookup - -`filename` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Lookup Manifest](lookups-properties-filename.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filename") - -### filename Type - -`string` - -### filename Examples - -```yaml -prohibited_apps_launching_cmd.csv - -``` - -## filter - -Use this attribute to improve search performance when working with significantly large KV - -`filter` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Lookup Manifest](lookups-properties-filter.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filter") - -### filter Type - -`string` - -### filter Examples - -```yaml -dest="SPLK_*" - -``` - -## match_type - -A comma and space-delimited list of \(\) specification to allow for non-exact matching - -`match_type` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Lookup Manifest](lookups-properties-match_type.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/match_type") - -### match_type Type - -`string` - -### match_type Examples - -```yaml -WILDCARD(process) - -``` - -## max_matches - -The maximum number of possible matches for each input lookup value - -`max_matches` - -* is optional - -* Type: `integer` - -* cannot be null - -* defined in: [Lookup Manifest](lookups-properties-max_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/max_matches") - -### max_matches Type - -`integer` - -### max_matches Examples - -```yaml -'100' - -``` - -## min_matches - -Minimum number of possible matches for each input lookup value - -`min_matches` - -* is optional - -* Type: `integer` - -* cannot be null - -* defined in: [Lookup Manifest](lookups-properties-min_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/min_matches") - -### min_matches Type - -`integer` - -### min_matches Examples - -```yaml -'1' - -``` - -## name - -The name of the lookup to be used in searches - -`name` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Lookup Manifest](lookups-properties-name.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/name") - -### name Type - -`string` - -### name Examples - -```yaml -isWindowsSystemFile_lookup - -``` +- **`name`** *(string)*: The name of the lookup to be used in searches. diff --git a/docs/spec/macros-properties-arguments-items.md b/docs/spec/macros-properties-arguments-items.md deleted file mode 100644 index 34f73377e8..0000000000 --- a/docs/spec/macros-properties-arguments-items.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled string in Macro Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/macros.json#/properties/arguments/items -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | - -## items Type - -`string` diff --git a/docs/spec/macros-properties-arguments.md b/docs/spec/macros-properties-arguments.md deleted file mode 100644 index 1334acd3c1..0000000000 --- a/docs/spec/macros-properties-arguments.md +++ /dev/null @@ -1,21 +0,0 @@ -# Untitled array in Macro Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/macros.json#/properties/arguments -``` - -A list of the arguments being passed to this macro - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | - -## arguments Type - -`string[]` - -## arguments Constraints - -**minimum number of items**: the minimum number of items for this array is: `0` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. diff --git a/docs/spec/macros-properties-definition.md b/docs/spec/macros-properties-definition.md deleted file mode 100644 index 128ea17364..0000000000 --- a/docs/spec/macros-properties-definition.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Macro Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/macros.json#/properties/definition -``` - -The macro definition - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | - -## definition Type - -`string` - -## definition Examples - -```yaml -(query=fls-na* AND query = www* AND query=images*) - -``` diff --git a/docs/spec/macros-properties-description.md b/docs/spec/macros-properties-description.md deleted file mode 100644 index e5f8aed632..0000000000 --- a/docs/spec/macros-properties-description.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Macro Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/macros.json#/properties/description -``` - -What the macro is intended to filter - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | - -## description Type - -`string` - -## description Examples - -```yaml -Use this macro to filter out known good objects - -``` diff --git a/docs/spec/macros-properties-name.md b/docs/spec/macros-properties-name.md deleted file mode 100644 index 484c1e4e3f..0000000000 --- a/docs/spec/macros-properties-name.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Macro Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/macros.json#/properties/name -``` - -The name of the macro - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | - -## name Type - -`string` - -## name Examples - -```yaml -detection_search_output_filter - -``` diff --git a/docs/spec/macros.md b/docs/spec/macros.md index c3cb8dc57b..8238f4e9bb 100644 --- a/docs/spec/macros.md +++ b/docs/spec/macros.md @@ -1,123 +1,18 @@ -# Macro Manifest Schema +# Macro Manifest -```txt -https://api.splunkresearch.com/schemas/macros.json -``` -An object that defines the parameters for a Splunk Macro +*An object that defines the parameters for a Splunk Macro* -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [macros.spec.json](../../out/macros.spec.json "open original schema") | -## Macro Manifest Type +## Properties -`object` ([Macro Manifest](macros.md)) -# Macro Manifest Properties +- **`arguments`** *(array)*: A list of the arguments being passed to this macro. -| Property | Type | Required | Nullable | Defined by | -| :-------------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------ | -| [arguments](#arguments) | `array` | Optional | cannot be null | [Macro Manifest](macros-properties-arguments.md "https://api.splunkresearch.com/schemas/macros.json#/properties/arguments") | -| [definition](#definition) | `string` | Optional | cannot be null | [Macro Manifest](macros-properties-definition.md "https://api.splunkresearch.com/schemas/macros.json#/properties/definition") | -| [description](#description) | `string` | Required | cannot be null | [Macro Manifest](macros-properties-description.md "https://api.splunkresearch.com/schemas/macros.json#/properties/description") | -| [name](#name) | `string` | Required | cannot be null | [Macro Manifest](macros-properties-name.md "https://api.splunkresearch.com/schemas/macros.json#/properties/name") | + - **Items** *(string)* -## arguments +- **`definition`** *(string)*: The macro definition. -A list of the arguments being passed to this macro +- **`description`** *(string)*: What the macro is intended to filter. -`arguments` - -* is optional - -* Type: `string[]` - -* cannot be null - -* defined in: [Macro Manifest](macros-properties-arguments.md "https://api.splunkresearch.com/schemas/macros.json#/properties/arguments") - -### arguments Type - -`string[]` - -### arguments Constraints - -**minimum number of items**: the minimum number of items for this array is: `0` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -## definition - -The macro definition - -`definition` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Macro Manifest](macros-properties-definition.md "https://api.splunkresearch.com/schemas/macros.json#/properties/definition") - -### definition Type - -`string` - -### definition Examples - -```yaml -(query=fls-na* AND query = www* AND query=images*) - -``` - -## description - -What the macro is intended to filter - -`description` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Macro Manifest](macros-properties-description.md "https://api.splunkresearch.com/schemas/macros.json#/properties/description") - -### description Type - -`string` - -### description Examples - -```yaml -Use this macro to filter out known good objects - -``` - -## name - -The name of the macro - -`name` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Macro Manifest](macros-properties-name.md "https://api.splunkresearch.com/schemas/macros.json#/properties/name") - -### name Type - -`string` - -### name Examples - -```yaml -detection_search_output_filter - -``` +- **`name`** *(string)*: The name of the macro. diff --git a/docs/spec/response_tasks-default.md b/docs/spec/response_tasks-default.md deleted file mode 100644 index 31296217d9..0000000000 --- a/docs/spec/response_tasks-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response_tasks.spec.json#/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/response_tasks-properties-automation-default.md b/docs/spec/response_tasks-properties-automation-default.md deleted file mode 100644 index b2cdd45428..0000000000 --- a/docs/spec/response_tasks-properties-automation-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -#/properties/automation#/properties/automation/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/response_tasks-properties-automation.md b/docs/spec/response_tasks-properties-automation.md deleted file mode 100644 index bdd1eb66d4..0000000000 --- a/docs/spec/response_tasks-properties-automation.md +++ /dev/null @@ -1,62 +0,0 @@ -# Untitled object in Response Schema Schema - -```txt -#/properties/automation#/properties/automation -``` - -An array of key value pairs for defining actions and playbooks - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | - -## automation Type - -`object` ([Details](response_tasks-properties-automation.md)) - -## automation Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -## automation Default Value - -The default value is: - -```json -{ - "is_note_required": false, - "sla_type": "minutes", - "sla": "", - "role": "", - "action": [], - "playbooks": [] -} -``` - -## automation Examples - -```yaml -is_note_required: false -sla_type: minutes -sla: 30 -action: - - run_query -playbooks: - - scm: local - playbook: automate something - - scm: local - playbook: automate something else - -``` - -# automation Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :--- | :------- | :---------- | :--------- | -| Additional Properties | Any | Optional | can be null | | - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/response_tasks-properties-sla.md b/docs/spec/response_tasks-properties-sla.md deleted file mode 100644 index 9de4e3e98f..0000000000 --- a/docs/spec/response_tasks-properties-sla.md +++ /dev/null @@ -1,27 +0,0 @@ -# Untitled integer in Response Schema Schema - -```txt -#/properties/sla#/properties/sla -``` - -Measured integer for Service Level Agreement for completion of the phase - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | - -## sla Type - -`integer` - -## sla Examples - -```yaml -5 - -``` - -```yaml -30 - -``` diff --git a/docs/spec/response_tasks-properties-sla_type.md b/docs/spec/response_tasks-properties-sla_type.md deleted file mode 100644 index 28319602eb..0000000000 --- a/docs/spec/response_tasks-properties-sla_type.md +++ /dev/null @@ -1,40 +0,0 @@ -# Untitled string in Response Schema Schema - -```txt -#/properties/sla_type#/properties/sla_type -``` - -Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc) - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | - -## sla_type Type - -`string` - -## sla_type Default Value - -The default value is: - -```json -"minutes" -``` - -## sla_type Examples - -```yaml -minutes - -``` - -```yaml -hours - -``` - -```yaml -days - -``` diff --git a/docs/spec/response_tasks.md b/docs/spec/response_tasks.md index 6ee8b796ed..fc51ab06a6 100644 --- a/docs/spec/response_tasks.md +++ b/docs/spec/response_tasks.md @@ -1,395 +1,32 @@ -# Response Schema Schema +# Response Schema -```txt -https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response_tasks.spec.json -``` -schema for response task +*schema for response task* -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------ | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [response_tasks.spec.json](../../out/response_tasks.spec.json "open original schema") | -## Response Schema Type +## Properties -`object` ([Response Schema](response_tasks.md)) -## Response Schema Default Value +- **`author`** *(string)*: Author of the response task. Default: ``. -The default value is: +- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. -```json -{} -``` +- **`description`** *(string)*: Description of response task. Default: ``. -# Response Schema Properties +- **`id`** *(string)*: UUID as unique identifier. Default: ``. -| Property | Type | Required | Nullable | Defined by | -| :-------------------------- | :-------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------- | -| [author](#author) | `string` | Required | cannot be null | [Response Schema](response_tasks-properties-author.md "#/properties/author#/properties/author") | -| [date](#date) | `string` | Required | cannot be null | [Response Schema](response_tasks-properties-date.md "#/properties/date#/properties/date") | -| [description](#description) | `string` | Required | cannot be null | [Response Schema](response_tasks-properties-description.md "#/properties/description#/properties/description") | -| [id](#id) | `string` | Required | cannot be null | [Response Schema](response_tasks-properties-id.md "#/properties/id#/properties/id") | -| [name](#name) | `string` | Required | cannot be null | [Response Schema](response_tasks-properties-name.md "#/properties/name#/properties/name") | -| [sla](#sla) | `integer` | Optional | cannot be null | [Response Schema](response_tasks-properties-sla.md "#/properties/sla#/properties/sla") | -| [sla_type](#sla_type) | `string` | Optional | cannot be null | [Response Schema](response_tasks-properties-sla_type.md "#/properties/sla_type#/properties/sla_type") | -| [automation](#automation) | `object` | Optional | cannot be null | [Response Schema](response_tasks-properties-automation.md "#/properties/automation#/properties/automation") | -| [tags](#tags) | `object` | Required | cannot be null | [Response Schema](response_tasks-properties-tags.md "#/properties/tags#/properties/tags") | -| [version](#version) | `integer` | Required | cannot be null | [Response Schema](response_tasks-properties-version.md "#/properties/version#/properties/version") | -| [references](#references) | `array` | Optional | cannot be null | [Response Schema](response_tasks-properties-references.md "#/properties/references#/properties/references") | -| Additional Properties | Any | Optional | can be null | | +- **`name`** *(string)*: Name of response task. Default: ``. -## author +- **`sla`** *(integer)*: Measured integer for Service Level Agreement for completion of the phase. Default: `0`. -Author of the response task +- **`sla_type`** *(string)*: Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc). Default: `minutes`. -`author` +- **`automation`** *(object)*: An array of key value pairs for defining actions and playbooks. Can contain additional properties. Default: `{'is_note_required': False, 'sla_type': 'minutes', 'sla': '', 'role': '', 'action': [], 'playbooks': []}`. -* is required +- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. -* Type: `string` +- **`version`** *(integer)*: version of detection, e.g. 1 or 2 ... Default: `0`. -* cannot be null +- **`references`** *(array)*: A list of references for this response, phase or task (e.g. web or printed citation). Default: `[]`. -* defined in: [Response Schema](response_tasks-properties-author.md "#/properties/author#/properties/author") - -### author Type - -`string` - -### author Examples - -```yaml -ButterCup, Splunk - -``` - -## date - -date of creation or modification, format yyyy-mm-dd - -`date` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](response_tasks-properties-date.md "#/properties/date#/properties/date") - -### date Type - -`string` - -### date Examples - -```yaml -'2019-12-06' - -``` - -## description - -Description of response task - -`description` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](response_tasks-properties-description.md "#/properties/description#/properties/description") - -### description Type - -`string` - -### description Examples - -```yaml -Response example. - -``` - -## id - -UUID as unique identifier - -`id` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](response_tasks-properties-id.md "#/properties/id#/properties/id") - -### id Type - -`string` - -### id Examples - -```yaml -fb4c31b0-13e8-4155-8aa5-24de4b8d6717 - -``` - -## name - -Name of response task - -`name` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](response_tasks-properties-name.md "#/properties/name#/properties/name") - -### name Type - -`string` - -### name Examples - -```yaml -Response Example - -``` - -## sla - -Measured integer for Service Level Agreement for completion of the phase - -`sla` - -* is optional - -* Type: `integer` - -* cannot be null - -* defined in: [Response Schema](response_tasks-properties-sla.md "#/properties/sla#/properties/sla") - -### sla Type - -`integer` - -### sla Examples - -```yaml -5 - -``` - -```yaml -30 - -``` - -## sla_type - -Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc) - -`sla_type` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](response_tasks-properties-sla_type.md "#/properties/sla_type#/properties/sla_type") - -### sla_type Type - -`string` - -### sla_type Default Value - -The default value is: - -```json -"minutes" -``` - -### sla_type Examples - -```yaml -minutes - -``` - -```yaml -hours - -``` - -```yaml -days - -``` - -## automation - -An array of key value pairs for defining actions and playbooks - -`automation` - -* is optional - -* Type: `object` ([Details](response_tasks-properties-automation.md)) - -* cannot be null - -* defined in: [Response Schema](response_tasks-properties-automation.md "#/properties/automation#/properties/automation") - -### automation Type - -`object` ([Details](response_tasks-properties-automation.md)) - -### automation Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -### automation Default Value - -The default value is: - -```json -{ - "is_note_required": false, - "sla_type": "minutes", - "sla": "", - "role": "", - "action": [], - "playbooks": [] -} -``` - -### automation Examples - -```yaml -is_note_required: false -sla_type: minutes -sla: 30 -action: - - run_query -playbooks: - - scm: local - playbook: automate something - - scm: local - playbook: automate something else - -``` - -## tags - -An array of key value pairs for tagging - -`tags` - -* is required - -* Type: `object` ([Details](response_tasks-properties-tags.md)) - -* cannot be null - -* defined in: [Response Schema](response_tasks-properties-tags.md "#/properties/tags#/properties/tags") - -### tags Type - -`object` ([Details](response_tasks-properties-tags.md)) - -### tags Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -### tags Default Value - -The default value is: - -```json -{} -``` - -### tags Examples - -```yaml -analytic_story: credential_dumping - -``` - -## version - -version of detection, e.g. 1 or 2 ... - -`version` - -* is required - -* Type: `integer` - -* cannot be null - -* defined in: [Response Schema](response_tasks-properties-version.md "#/properties/version#/properties/version") - -### version Type - -`integer` - -### version Examples - -```yaml -1 - -``` - -## references - -A list of references for this response, phase or task (e.g. web or printed citation) - -`references` - -* is optional - -* Type: `string[]` ([Blue Team Handbook by Don Murdoch - Amazon](response_tasks-properties-references-blue-team-handbook-by-don-murdoch---amazon.md)) - -* cannot be null - -* defined in: [Response Schema](response_tasks-properties-references.md "#/properties/references#/properties/references") - -### references Type - -`string[]` ([Blue Team Handbook by Don Murdoch - Amazon](response_tasks-properties-references-blue-team-handbook-by-don-murdoch---amazon.md)) - -### references Default Value - -The default value is: - -```json -[] -``` - -### references Examples - -```yaml -- Blue Team Handbook by Don Murdoch - Alarm Triage Overview pages 146-148 -- https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema + - **Items** *(string)*: An explanation about the purpose of this instance. Default: ``. diff --git a/docs/spec/responses-default.md b/docs/spec/responses-default.md deleted file mode 100644 index 3478b338bd..0000000000 --- a/docs/spec/responses-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response.spec.json#/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/responses-properties-is_note_required.md b/docs/spec/responses-properties-is_note_required.md deleted file mode 100644 index 26aed247e2..0000000000 --- a/docs/spec/responses-properties-is_note_required.md +++ /dev/null @@ -1,27 +0,0 @@ -# Untitled boolean in Response Schema Schema - -```txt -#/properties/is_note_required#/properties/is_note_required -``` - -Global assignment for notes being required for tasks, can be individually set in the task - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | - -## is_note_required Type - -`boolean` - -## is_note_required Examples - -```yaml -true - -``` - -```yaml -false - -``` diff --git a/docs/spec/responses-properties-response_phase-default.md b/docs/spec/responses-properties-response_phase-default.md deleted file mode 100644 index e773e97379..0000000000 --- a/docs/spec/responses-properties-response_phase-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -#/properties/response_phases#/properties/response_phase/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/responses-properties-response_phase.md b/docs/spec/responses-properties-response_phase.md deleted file mode 100644 index bdd06286c0..0000000000 --- a/docs/spec/responses-properties-response_phase.md +++ /dev/null @@ -1,51 +0,0 @@ -# Untitled array in Response Schema Schema - -```txt -#/properties/response_phases#/properties/response_phase -``` - -Response divided into phases. These will used to referenced known response_phase parameters - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | - -## response_phase Type - -`array` - -## response_phase Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -## response_phase Default Value - -The default value is: - -```json -{} -``` - -## response_phase Examples - -```yaml -preparation: - - id: 7c72d944-3995-4485-8e57-67b4c353989b - name: Preparation NIST -identification: - - id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892 - name: Detection and Analysis - - id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0 - name: Analysis - -``` - -# response_phase Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :--- | :------- | :---------- | :--------- | -| Additional Properties | Any | Optional | can be null | | - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/responses.md b/docs/spec/responses.md index 314ad93450..4e21a5f6fc 100644 --- a/docs/spec/responses.md +++ b/docs/spec/responses.md @@ -1,340 +1,30 @@ -# Response Schema Schema +# Response Schema -```txt -https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response.spec.json -``` -schema for response +*schema for response* -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [responses.spec.json](../../out/responses.spec.json "open original schema") | -## Response Schema Type +## Properties -`object` ([Response Schema](responses.md)) -## Response Schema Default Value +- **`author`** *(string)*: Author of the response. Default: ``. -The default value is: +- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. -```json -{} -``` +- **`description`** *(string)*: Description of response. Default: ``. -# Response Schema Properties +- **`id`** *(string)*: UUID as unique identifier. Default: ``. -| Property | Type | Required | Nullable | Defined by | -| :------------------------------------ | :-------- | :------- | :------------- | :----------------------------------------------------------------------------------------------------------------------- | -| [author](#author) | `string` | Required | cannot be null | [Response Schema](responses-properties-author.md "#/properties/author#/properties/author") | -| [date](#date) | `string` | Required | cannot be null | [Response Schema](responses-properties-date.md "#/properties/date#/properties/date") | -| [description](#description) | `string` | Required | cannot be null | [Response Schema](responses-properties-description.md "#/properties/description#/properties/description") | -| [id](#id) | `string` | Required | cannot be null | [Response Schema](responses-properties-id.md "#/properties/id#/properties/id") | -| [name](#name) | `string` | Required | cannot be null | [Response Schema](responses-properties-name.md "#/properties/name#/properties/name") | -| [response_phase](#response_phase) | `array` | Required | cannot be null | [Response Schema](responses-properties-response_phase.md "#/properties/response_phases#/properties/response_phase") | -| [tags](#tags) | `object` | Required | cannot be null | [Response Schema](responses-properties-tags.md "#/properties/tags#/properties/tags") | -| [version](#version) | `integer` | Required | cannot be null | [Response Schema](responses-properties-version.md "#/properties/version#/properties/version") | -| [is_note_required](#is_note_required) | `boolean` | Optional | cannot be null | [Response Schema](responses-properties-is_note_required.md "#/properties/is_note_required#/properties/is_note_required") | -| [references](#references) | `array` | Optional | cannot be null | [Response Schema](responses-properties-references.md "#/properties/references#/properties/references") | -| Additional Properties | Any | Optional | can be null | | +- **`name`** *(string)*: Name of response. Default: ``. -## author +- **`response_phase`** *(array)*: Response divided into phases. These will used to referenced known response_phase parameters. Can contain additional properties. Default: `{}`. -Author of the response +- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. -`author` +- **`version`** *(integer)*: version of detection, e.g. 1 or 2 ... Default: `0`. -* is required +- **`is_note_required`** *(boolean)*: Global assignment for notes being required for tasks, can be individually set in the task. Default: `False`. -* Type: `string` +- **`references`** *(array)*: A list of references for this response, phase or task (e.g. web or printed citation). Default: `[]`. -* cannot be null - -* defined in: [Response Schema](responses-properties-author.md "#/properties/author#/properties/author") - -### author Type - -`string` - -### author Examples - -```yaml -Rico Valdez, Patrick Bareiß, Splunk - -``` - -## date - -date of creation or modification, format yyyy-mm-dd - -`date` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](responses-properties-date.md "#/properties/date#/properties/date") - -### date Type - -`string` - -### date Examples - -```yaml -'2019-12-06' - -``` - -## description - -Description of response - -`description` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](responses-properties-description.md "#/properties/description#/properties/description") - -### description Type - -`string` - -### description Examples - -```yaml -Response example. - -``` - -## id - -UUID as unique identifier - -`id` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](responses-properties-id.md "#/properties/id#/properties/id") - -### id Type - -`string` - -### id Examples - -```yaml -fb4c31b0-13e8-4155-8aa5-24de4b8d6717 - -``` - -## name - -Name of response - -`name` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](responses-properties-name.md "#/properties/name#/properties/name") - -### name Type - -`string` - -### name Examples - -```yaml -Response Example - -``` - -## response_phase - -Response divided into phases. These will used to referenced known response_phase parameters - -`response_phase` - -* is required - -* Type: `array` - -* cannot be null - -* defined in: [Response Schema](responses-properties-response_phase.md "#/properties/response_phases#/properties/response_phase") - -### response_phase Type - -`array` - -### response_phase Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -### response_phase Default Value - -The default value is: - -```json -{} -``` - -### response_phase Examples - -```yaml -preparation: - - id: 7c72d944-3995-4485-8e57-67b4c353989b - name: Preparation NIST -identification: - - id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892 - name: Detection and Analysis - - id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0 - name: Analysis - -``` - -## tags - -An array of key value pairs for tagging - -`tags` - -* is required - -* Type: `object` ([Details](responses-properties-tags.md)) - -* cannot be null - -* defined in: [Response Schema](responses-properties-tags.md "#/properties/tags#/properties/tags") - -### tags Type - -`object` ([Details](responses-properties-tags.md)) - -### tags Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -### tags Default Value - -The default value is: - -```json -{} -``` - -### tags Examples - -```yaml -analytic_story: credential_dumping - -``` - -## version - -version of detection, e.g. 1 or 2 ... - -`version` - -* is required - -* Type: `integer` - -* cannot be null - -* defined in: [Response Schema](responses-properties-version.md "#/properties/version#/properties/version") - -### version Type - -`integer` - -### version Examples - -```yaml -1 - -``` - -## is_note_required - -Global assignment for notes being required for tasks, can be individually set in the task - -`is_note_required` - -* is optional - -* Type: `boolean` - -* cannot be null - -* defined in: [Response Schema](responses-properties-is_note_required.md "#/properties/is_note_required#/properties/is_note_required") - -### is_note_required Type - -`boolean` - -### is_note_required Examples - -```yaml -true - -``` - -```yaml -false - -``` - -## references - -A list of references for this response, phase or task (e.g. web or printed citation) - -`references` - -* is optional - -* Type: `string[]` ([Blue Team Handbook by Don Murdoch - Amazon](responses-properties-references-blue-team-handbook-by-don-murdoch---amazon.md)) - -* cannot be null - -* defined in: [Response Schema](responses-properties-references.md "#/properties/references#/properties/references") - -### references Type - -`string[]` ([Blue Team Handbook by Don Murdoch - Amazon](responses-properties-references-blue-team-handbook-by-don-murdoch---amazon.md)) - -### references Default Value - -The default value is: - -```json -[] -``` - -### references Examples - -```yaml -- Blue Team Handbook by Don Murdoch - Alarm Triage Overview pages 146-148 -- https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema + - **Items** *(string)*: An explanation about the purpose of this instance. Default: ``. diff --git a/docs/spec/responses_phase-default.md b/docs/spec/responses_phase-default.md deleted file mode 100644 index c1576f1e4e..0000000000 --- a/docs/spec/responses_phase-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -http://example.com/example.json#/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/responses_phase-properties-response_task-default.md b/docs/spec/responses_phase-properties-response_task-default.md deleted file mode 100644 index 9ede2dfaf9..0000000000 --- a/docs/spec/responses_phase-properties-response_task-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -#/properties/response_task#/properties/response_task/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/responses_phase-properties-response_task.md b/docs/spec/responses_phase-properties-response_task.md deleted file mode 100644 index 780e4b3d5b..0000000000 --- a/docs/spec/responses_phase-properties-response_task.md +++ /dev/null @@ -1,57 +0,0 @@ -# Untitled array in Response Schema Schema - -```txt -#/properties/response_task#/properties/response_task -``` - -Response phase is divided into task(s) to be completed. These will used to referenced known response_task parameters. Order is as positioned and with unique name. - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") | - -## response_task Type - -`array` - -## response_task Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -## response_task Default Value - -The default value is: - -```json -{} -``` - -## response_task Examples - -```yaml -id: 7c72d944-3995-4485-8e57-67b4c353989b -name: Prepare for Incident Handling - -``` - -```yaml -id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892 -name: Preventing Incidents - -``` - -```yaml -id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0 -name: Practice - -``` - -# response_task Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :--- | :------- | :---------- | :--------- | -| Additional Properties | Any | Optional | can be null | | - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/responses_phase.md b/docs/spec/responses_phase.md index 09d1b3be2a..c17e3e5e51 100644 --- a/docs/spec/responses_phase.md +++ b/docs/spec/responses_phase.md @@ -1,389 +1,32 @@ -# Response Schema Schema +# Response Schema -```txt -http://example.com/example.json -``` -schema for phase +*schema for phase* -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [responses_phase.spec.json](../../out/responses_phase.spec.json "open original schema") | -## Response Schema Type +## Properties -`object` ([Response Schema](responses_phase.md)) -## Response Schema Default Value +- **`author`** *(string)*: Author of the phase. Default: ``. -The default value is: +- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. -```json -{} -``` +- **`description`** *(string)*: Description of phase. Default: ``. -# Response Schema Properties +- **`id`** *(string)*: UUID as unique identifier. Default: ``. -| Property | Type | Required | Nullable | Defined by | -| :------------------------------ | :-------- | :------- | :------------- | :-------------------------------------------------------------------------------------------------------------------- | -| [author](#author) | `string` | Required | cannot be null | [Response Schema](responses_phase-properties-author.md "#/properties/author#/properties/author") | -| [date](#date) | `string` | Required | cannot be null | [Response Schema](responses_phase-properties-date.md "#/properties/date#/properties/date") | -| [description](#description) | `string` | Required | cannot be null | [Response Schema](responses_phase-properties-description.md "#/properties/description#/properties/description") | -| [id](#id) | `string` | Required | cannot be null | [Response Schema](responses_phase-properties-id.md "#/properties/id#/properties/id") | -| [name](#name) | `string` | Required | cannot be null | [Response Schema](responses_phase-properties-name.md "#/properties/name#/properties/name") | -| [response_task](#response_task) | `array` | Required | cannot be null | [Response Schema](responses_phase-properties-response_task.md "#/properties/response_task#/properties/response_task") | -| [tags](#tags) | `object` | Required | cannot be null | [Response Schema](responses_phase-properties-tags.md "#/properties/tags#/properties/tags") | -| [version](#version) | `integer` | Required | cannot be null | [Response Schema](responses_phase-properties-version.md "#/properties/version#/properties/version") | -| [sla](#sla) | `integer` | Optional | cannot be null | [Response Schema](responses_phase-properties-sla.md "#/properties/sla#/properties/sla") | -| [sla_type](#sla_type) | `string` | Optional | cannot be null | [Response Schema](responses_phase-properties-sla_type.md "#/properties/sla_type#/properties/sla_type") | -| [references](#references) | `array` | Optional | cannot be null | [Response Schema](responses_phase-properties-references.md "#/properties/references#/properties/references") | -| Additional Properties | Any | Optional | can be null | | +- **`name`** *(string)*: Name of phase. Default: ``. -## author +- **`response_task`** *(array)*: Response phase is divided into task(s) to be completed. These will used to referenced known response_task parameters. Order is as positioned and with unique name. Can contain additional properties. Default: `{}`. -Author of the phase +- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. -`author` +- **`version`** *(integer)*: version of detection, e.g. 1 or 2 ... Default: `0`. -* is required +- **`sla`** *(integer)*: Measured integer for Service Level Agreement for completion of the phase. Default: `None`. -* Type: `string` +- **`sla_type`** *(string)*: Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc). Default: `minutes`. -* cannot be null +- **`references`** *(array)*: A list of references for this response, phase or task (e.g. web or printed citation). Default: `[]`. -* defined in: [Response Schema](responses_phase-properties-author.md "#/properties/author#/properties/author") - -### author Type - -`string` - -### author Examples - -```yaml -Rico Valdez, Patrick Bareiß, Splunk - -``` - -## date - -date of creation or modification, format yyyy-mm-dd - -`date` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](responses_phase-properties-date.md "#/properties/date#/properties/date") - -### date Type - -`string` - -### date Examples - -```yaml -'2019-12-06' - -``` - -## description - -Description of phase - -`description` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](responses_phase-properties-description.md "#/properties/description#/properties/description") - -### description Type - -`string` - -### description Examples - -```yaml -Response phase descripion. - -``` - -## id - -UUID as unique identifier - -`id` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](responses_phase-properties-id.md "#/properties/id#/properties/id") - -### id Type - -`string` - -### id Examples - -```yaml -fb4c31b0-13e8-4155-8aa5-24de4b8d6717 - -``` - -## name - -Name of phase - -`name` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](responses_phase-properties-name.md "#/properties/name#/properties/name") - -### name Type - -`string` - -### name Examples - -```yaml -Preparation - -``` - -## response_task - -Response phase is divided into task(s) to be completed. These will used to referenced known response_task parameters. Order is as positioned and with unique name. - -`response_task` - -* is required - -* Type: `array` - -* cannot be null - -* defined in: [Response Schema](responses_phase-properties-response_task.md "#/properties/response_task#/properties/response_task") - -### response_task Type - -`array` - -### response_task Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -### response_task Default Value - -The default value is: - -```json -{} -``` - -### response_task Examples - -```yaml -id: 7c72d944-3995-4485-8e57-67b4c353989b -name: Prepare for Incident Handling - -``` - -```yaml -id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892 -name: Preventing Incidents - -``` - -```yaml -id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0 -name: Practice - -``` - -## tags - -An array of key value pairs for tagging - -`tags` - -* is required - -* Type: `object` ([Details](responses_phase-properties-tags.md)) - -* cannot be null - -* defined in: [Response Schema](responses_phase-properties-tags.md "#/properties/tags#/properties/tags") - -### tags Type - -`object` ([Details](responses_phase-properties-tags.md)) - -### tags Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -### tags Default Value - -The default value is: - -```json -{} -``` - -### tags Examples - -```yaml -analytic_story: credential_dumping - -``` - -## version - -version of detection, e.g. 1 or 2 ... - -`version` - -* is required - -* Type: `integer` - -* cannot be null - -* defined in: [Response Schema](responses_phase-properties-version.md "#/properties/version#/properties/version") - -### version Type - -`integer` - -### version Examples - -```yaml -1 - -``` - -## sla - -Measured integer for Service Level Agreement for completion of the phase - -`sla` - -* is optional - -* Type: `integer` - -* cannot be null - -* defined in: [Response Schema](responses_phase-properties-sla.md "#/properties/sla#/properties/sla") - -### sla Type - -`integer` - -### sla Examples - -```yaml -5 - -``` - -```yaml -30 - -``` - -## sla_type - -Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc) - -`sla_type` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Response Schema](responses_phase-properties-sla_type.md "#/properties/sla_type#/properties/sla_type") - -### sla_type Type - -`string` - -### sla_type Default Value - -The default value is: - -```json -"minutes" -``` - -### sla_type Examples - -```yaml -minutes - -``` - -```yaml -hours - -``` - -```yaml -days - -``` - -## references - -A list of references for this response, phase or task (e.g. web or printed citation) - -`references` - -* is optional - -* Type: `string[]` ([3.1 Preparation](responses_phase-properties-references-31-preparation.md)) - -* cannot be null - -* defined in: [Response Schema](responses_phase-properties-references.md "#/properties/references#/properties/references") - -### references Type - -`string[]` ([3.1 Preparation](responses_phase-properties-references-31-preparation.md)) - -### references Default Value - -The default value is: - -```json -[] -``` - -### references Examples - -```yaml -https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema + - **Items** *(string)*: An explanation about the purpose of this instance. Default: ``. diff --git a/docs/spec/stories-default.md b/docs/spec/stories-default.md deleted file mode 100644 index 9abc854a40..0000000000 --- a/docs/spec/stories-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Analytics Story Schema Schema - -```txt -http://example.com/example.json#/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [stories.spec.json*](../../out/stories.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/stories-properties-narrative.md b/docs/spec/stories-properties-narrative.md deleted file mode 100644 index cc4532dd7b..0000000000 --- a/docs/spec/stories-properties-narrative.md +++ /dev/null @@ -1,26 +0,0 @@ -# Untitled string in Analytics Story Schema Schema - -```txt -#/properties/narrative#/properties/narrative -``` - -narrative of the analytics story - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [stories.spec.json*](../../out/stories.spec.json "open original schema") | - -## narrative Type - -`string` - -## narrative Examples - -```yaml ->- - gathering credentials from a target system, often hashed or encrypted, is a - common attack technique. Even though the credentials may not be in plain text, - an attacker can still exfiltrate the data and set to cracking it offline, on - their own systems. - -``` diff --git a/docs/spec/stories.md b/docs/spec/stories.md index 50b565a2fe..27f32da5f3 100644 --- a/docs/spec/stories.md +++ b/docs/spec/stories.md @@ -1,287 +1,26 @@ -# Analytics Story Schema Schema +# Analytics Story Schema -```txt -http://example.com/example.json -``` -schema analytics story +*schema analytics story* -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [stories.spec.json](../../out/stories.spec.json "open original schema") | -## Analytics Story Schema Type +## Properties -`object` ([Analytics Story Schema](stories.md)) -## Analytics Story Schema Default Value +- **`author`** *(string)*: Author of the analytics story. Default: ``. -The default value is: +- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. -```json -{} -``` +- **`description`** *(string)*: description of the analytics story. Default: ``. -# Analytics Story Schema Properties +- **`id`** *(string)*: UUID as unique identifier. Default: ``. -| Property | Type | Required | Nullable | Defined by | -| :-------------------------- | :-------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------- | -| [author](#author) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-author.md "#/properties/author#/properties/author") | -| [date](#date) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-date.md "#/properties/date#/properties/date") | -| [description](#description) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-description.md "#/properties/description#/properties/description") | -| [id](#id) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-id.md "#/properties/id#/properties/id") | -| [name](#name) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-name.md "#/properties/name#/properties/name") | -| [narrative](#narrative) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-narrative.md "#/properties/narrative#/properties/narrative") | -| [search](#search) | `string` | Optional | cannot be null | [Analytics Story Schema](stories-properties-search.md "#/properties/search#/properties/search") | -| [tags](#tags) | `object` | Required | cannot be null | [Analytics Story Schema](stories-properties-tags.md "#/properties/tags#/properties/tags") | -| [version](#version) | `integer` | Required | cannot be null | [Analytics Story Schema](stories-properties-version.md "#/properties/version#/properties/version") | -| Additional Properties | Any | Optional | can be null | | +- **`name`** *(string)*: Name of the Analytics Story. Default: ``. -## author +- **`narrative`** *(string)*: narrative of the analytics story. Default: ``. -Author of the analytics story +- **`search`** *(string)*: An additional Splunk search, which uses the result of the detections. Default: ``. -`author` +- **`tags`** *(object)*: An explanation about the purpose of this instance. Can contain additional properties. Default: `{}`. -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Analytics Story Schema](stories-properties-author.md "#/properties/author#/properties/author") - -### author Type - -`string` - -### author Examples - -```yaml -Rico Valdez, Patrick Bareiß, Splunk - -``` - -## date - -date of creation or modification, format yyyy-mm-dd - -`date` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Analytics Story Schema](stories-properties-date.md "#/properties/date#/properties/date") - -### date Type - -`string` - -### date Examples - -```yaml -'2019-12-06' - -``` - -## description - -description of the analytics story - -`description` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Analytics Story Schema](stories-properties-description.md "#/properties/description#/properties/description") - -### description Type - -`string` - -### description Examples - -```yaml ->- - Uncover activity consistent with credential dumping, a technique where - attackers compromise systems and attempt to obtain and exfiltrate passwords. - -``` - -## id - -UUID as unique identifier - -`id` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Analytics Story Schema](stories-properties-id.md "#/properties/id#/properties/id") - -### id Type - -`string` - -### id Examples - -```yaml -fb4c31b0-13e8-4155-8aa5-24de4b8d6717 - -``` - -## name - -Name of the Analytics Story - -`name` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Analytics Story Schema](stories-properties-name.md "#/properties/name#/properties/name") - -### name Type - -`string` - -### name Examples - -```yaml -Credential Dumping - -``` - -## narrative - -narrative of the analytics story - -`narrative` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Analytics Story Schema](stories-properties-narrative.md "#/properties/narrative#/properties/narrative") - -### narrative Type - -`string` - -### narrative Examples - -```yaml ->- - gathering credentials from a target system, often hashed or encrypted, is a - common attack technique. Even though the credentials may not be in plain text, - an attacker can still exfiltrate the data and set to cracking it offline, on - their own systems. - -``` - -## search - -An additional Splunk search, which uses the result of the detections - -`search` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Analytics Story Schema](stories-properties-search.md "#/properties/search#/properties/search") - -### search Type - -`string` - -### search Examples - -```yaml ->- - index=asx mitre_id=t1003 | stats values(source) as detections values(process) - as processes values(user) as users values(_time) as time count by dest - -``` - -## tags - -An explanation about the purpose of this instance. - -`tags` - -* is required - -* Type: `object` ([Details](stories-properties-tags.md)) - -* cannot be null - -* defined in: [Analytics Story Schema](stories-properties-tags.md "#/properties/tags#/properties/tags") - -### tags Type - -`object` ([Details](stories-properties-tags.md)) - -### tags Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -### tags Default Value - -The default value is: - -```json -{} -``` - -### tags Examples - -```yaml -analytic_story: credential_dumping - -``` - -## version - -version of analytics story, e.g. 1 or 2 ... - -`version` - -* is required - -* Type: `integer` - -* cannot be null - -* defined in: [Analytics Story Schema](stories-properties-version.md "#/properties/version#/properties/version") - -### version Type - -`integer` - -### version Examples - -```yaml -1 - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema +- **`version`** *(integer)*: version of analytics story, e.g. 1 or 2 ... Default: `0`. From a21476d8e3447fd6f21819147cc8b7e8a00196f4 Mon Sep 17 00:00:00 2001 From: divious1 Date: Thu, 18 Mar 2021 14:53:40 -0400 Subject: [PATCH 21/62] cleaned up code a bit made it simpler --- bin/doc_gen.py | 58 +-- docs/spec/README.md | 51 +++ docs/spec/baselines-properties-author.md | 22 + .../baselines-properties-datamodel-items.md | 36 ++ docs/spec/baselines-properties-datamodel.md | 22 + docs/spec/baselines-properties-date.md | 22 + docs/spec/baselines-properties-description.md | 26 ++ .../baselines-properties-how_to_implement.md | 24 ++ docs/spec/baselines-properties-id.md | 22 + .../baselines-properties-name-of-baseline.md | 22 + docs/spec/baselines-properties-search.md | 24 ++ .../spec/baselines-properties-tags-default.md | 15 + docs/spec/baselines-properties-tags.md | 47 ++ docs/spec/baselines-properties-version.md | 22 + docs/spec/baselines.md | 306 ++++++++++++- docs/spec/deployments-default.md | 15 + ...oyments-properties-alert_action-default.md | 15 + ...s-alert_action-properties-email-default.md | 15 + ...ion-properties-email-properties-message.md | 22 + ...ion-properties-email-properties-subject.md | 22 + ...t_action-properties-email-properties-to.md | 22 + ...roperties-alert_action-properties-email.md | 120 ++++++ ...s-alert_action-properties-index-default.md | 15 + ...action-properties-index-properties-name.md | 22 + ...roperties-alert_action-properties-index.md | 66 +++ ...alert_action-properties-notable-default.md | 15 + ...ies-notable-properties-rule_description.md | 22 + ...roperties-notable-properties-rule_title.md | 22 + ...perties-alert_action-properties-notable.md | 93 ++++ .../deployments-properties-alert_action.md | 153 +++++++ ...ployments-properties-scheduling-default.md | 15 + ...ies-scheduling-properties-cron_schedule.md | 22 + ...ies-scheduling-properties-earliest_time.md | 22 + ...rties-scheduling-properties-latest_time.md | 22 + ...s-scheduling-properties-schedule_window.md | 22 + .../spec/deployments-properties-scheduling.md | 147 +++++++ docs/spec/deployments.md | 256 ++++++++++- ...ctions-properties-known_false_positives.md | 24 ++ ...-properties-references-the-items-schema.md | 23 + docs/spec/detections-properties-references.md | 31 ++ docs/spec/detections-properties-type-items.md | 24 ++ docs/spec/detections-properties-type.md | 22 + docs/spec/detections.md | 408 +++++++++++++++++- docs/spec/lookups-oneof-0.md | 15 + docs/spec/lookups-oneof-1.md | 15 + ...lookups-properties-case_sensitive_match.md | 31 ++ docs/spec/lookups-properties-collection.md | 22 + docs/spec/lookups-properties-default_match.md | 22 + docs/spec/lookups-properties-description.md | 22 + docs/spec/lookups-properties-fields_list.md | 22 + docs/spec/lookups-properties-filename.md | 22 + docs/spec/lookups-properties-filter.md | 22 + docs/spec/lookups-properties-match_type.md | 22 + docs/spec/lookups-properties-max_matches.md | 22 + docs/spec/lookups-properties-min_matches.md | 22 + docs/spec/lookups-properties-name.md | 22 + docs/spec/lookups.md | 319 +++++++++++++- .../spec/macros-properties-arguments-items.md | 15 + docs/spec/macros-properties-arguments.md | 21 + docs/spec/macros-properties-definition.md | 22 + docs/spec/macros-properties-description.md | 22 + docs/spec/macros-properties-name.md | 22 + docs/spec/macros.md | 121 +++++- docs/spec/response_tasks-default.md | 15 + ...nse_tasks-properties-automation-default.md | 15 + .../response_tasks-properties-automation.md | 62 +++ docs/spec/response_tasks-properties-sla.md | 27 ++ .../response_tasks-properties-sla_type.md | 40 ++ docs/spec/response_tasks.md | 393 ++++++++++++++++- docs/spec/responses-default.md | 15 + .../responses-properties-is_note_required.md | 27 ++ ...onses-properties-response_phase-default.md | 15 + .../responses-properties-response_phase.md | 51 +++ docs/spec/responses.md | 338 ++++++++++++++- docs/spec/responses_phase-default.md | 15 + ..._phase-properties-response_task-default.md | 15 + ...esponses_phase-properties-response_task.md | 57 +++ docs/spec/responses_phase.md | 387 ++++++++++++++++- docs/spec/stories-default.md | 15 + docs/spec/stories-properties-narrative.md | 26 ++ docs/spec/stories.md | 285 +++++++++++- 81 files changed, 4827 insertions(+), 190 deletions(-) create mode 100644 docs/spec/README.md create mode 100644 docs/spec/baselines-properties-author.md create mode 100644 docs/spec/baselines-properties-datamodel-items.md create mode 100644 docs/spec/baselines-properties-datamodel.md create mode 100644 docs/spec/baselines-properties-date.md create mode 100644 docs/spec/baselines-properties-description.md create mode 100644 docs/spec/baselines-properties-how_to_implement.md create mode 100644 docs/spec/baselines-properties-id.md create mode 100644 docs/spec/baselines-properties-name-of-baseline.md create mode 100644 docs/spec/baselines-properties-search.md create mode 100644 docs/spec/baselines-properties-tags-default.md create mode 100644 docs/spec/baselines-properties-tags.md create mode 100644 docs/spec/baselines-properties-version.md create mode 100644 docs/spec/deployments-default.md create mode 100644 docs/spec/deployments-properties-alert_action-default.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-email-default.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-email-properties-message.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-email-properties-to.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-email.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-index-default.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-index-properties-name.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-index.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-notable-default.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md create mode 100644 docs/spec/deployments-properties-alert_action-properties-notable.md create mode 100644 docs/spec/deployments-properties-alert_action.md create mode 100644 docs/spec/deployments-properties-scheduling-default.md create mode 100644 docs/spec/deployments-properties-scheduling-properties-cron_schedule.md create mode 100644 docs/spec/deployments-properties-scheduling-properties-earliest_time.md create mode 100644 docs/spec/deployments-properties-scheduling-properties-latest_time.md create mode 100644 docs/spec/deployments-properties-scheduling-properties-schedule_window.md create mode 100644 docs/spec/deployments-properties-scheduling.md create mode 100644 docs/spec/detections-properties-known_false_positives.md create mode 100644 docs/spec/detections-properties-references-the-items-schema.md create mode 100644 docs/spec/detections-properties-references.md create mode 100644 docs/spec/detections-properties-type-items.md create mode 100644 docs/spec/detections-properties-type.md create mode 100644 docs/spec/lookups-oneof-0.md create mode 100644 docs/spec/lookups-oneof-1.md create mode 100644 docs/spec/lookups-properties-case_sensitive_match.md create mode 100644 docs/spec/lookups-properties-collection.md create mode 100644 docs/spec/lookups-properties-default_match.md create mode 100644 docs/spec/lookups-properties-description.md create mode 100644 docs/spec/lookups-properties-fields_list.md create mode 100644 docs/spec/lookups-properties-filename.md create mode 100644 docs/spec/lookups-properties-filter.md create mode 100644 docs/spec/lookups-properties-match_type.md create mode 100644 docs/spec/lookups-properties-max_matches.md create mode 100644 docs/spec/lookups-properties-min_matches.md create mode 100644 docs/spec/lookups-properties-name.md create mode 100644 docs/spec/macros-properties-arguments-items.md create mode 100644 docs/spec/macros-properties-arguments.md create mode 100644 docs/spec/macros-properties-definition.md create mode 100644 docs/spec/macros-properties-description.md create mode 100644 docs/spec/macros-properties-name.md create mode 100644 docs/spec/response_tasks-default.md create mode 100644 docs/spec/response_tasks-properties-automation-default.md create mode 100644 docs/spec/response_tasks-properties-automation.md create mode 100644 docs/spec/response_tasks-properties-sla.md create mode 100644 docs/spec/response_tasks-properties-sla_type.md create mode 100644 docs/spec/responses-default.md create mode 100644 docs/spec/responses-properties-is_note_required.md create mode 100644 docs/spec/responses-properties-response_phase-default.md create mode 100644 docs/spec/responses-properties-response_phase.md create mode 100644 docs/spec/responses_phase-default.md create mode 100644 docs/spec/responses_phase-properties-response_task-default.md create mode 100644 docs/spec/responses_phase-properties-response_task.md create mode 100644 docs/spec/stories-default.md create mode 100644 docs/spec/stories-properties-narrative.md diff --git a/bin/doc_gen.py b/bin/doc_gen.py index 9f38e1ac82..ddad81d99f 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -5,7 +5,6 @@ import sys import re from os import path, walk import json -import jsonschema2md from jinja2 import Environment, FileSystemLoader from attackcti import attack_client from pyattck import Attck @@ -38,42 +37,6 @@ def get_mitre_enrichment_new(attack, mitre_attack_id): return mitre_attack return [] -def generate_doc_specs(REPO_PATH, OUTPUT_DIR, messages, VERBOSE): - spec_files = [] - - for root, dirs, files in walk(REPO_PATH + '/spec'): - for file in files: - if file.endswith(".json"): - spec_files.append((path.join(root, file))) - - parser = jsonschema2md.Parser() - spec_objects = [] - for spec in spec_files: - spec_object = dict() - if VERBOSE: - print("processing spec {0}".format(spec)) - with open(spec, 'r') as stream: - try: - markdown_lines = parser.parse_schema(json.load(stream)) - except json.JSONError as exc: - print(exc) - print("Error reading {0}".format(spec)) - sys.exit(1) - - spec_object['file'] = spec - spec_object['markdown'] = markdown_lines - spec_objects.append(spec_object) - - for spec in spec_objects: - # write markdown - file = spec['file'].split("/")[-1].split(".")[0] - output_path = path.join(OUTPUT_DIR + "/" + file + '.md' ) - with open(output_path, 'w', encoding="utf-8") as f: - f.write("\n".join(spec['markdown'])) - messages.append("doc_gen.py wrote {0} spec file documentation in markdown to: {1}".format(file,output_path)) - - return messages - def generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE): manifest_files = [] for root, dirs, files in walk(REPO_PATH + '/stories'): @@ -283,20 +246,11 @@ if __name__ == "__main__": parser.add_argument("-p", "--path", required=True, help="path to security_content repo") parser.add_argument("-o", "--output", required=True, help="path to the output directory for the docs") parser.add_argument("-v", "--verbose", required=False, default=False, action='store_true', help="prints verbose output") - parser.add_argument("-t", "--type", required=False, default="all", help="type of content to generate documentation for, supports `detections`, `stories`, `spec`, and `all`, defaults to `all`" ) - # parse them args = parser.parse_args() REPO_PATH = args.path OUTPUT_DIR = args.output VERBOSE = args.verbose - type = args.type - - allowed_types = ['stories', 'detections', 'spec', 'all'] - if type not in allowed_types: - print("ERROR: the type {0} is not support, the current support types are: {1}".format(type,allowed_types)) - parser.print_help() - sys.exit(1) TEMPLATE_PATH = path.join(REPO_PATH, 'bin/jinja2_templates') @@ -305,16 +259,8 @@ if __name__ == "__main__": attack = Attck() messages = [] - if type == 'all': - sorted_detections, messages = generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE) - sorted_stories, messages = generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE) - elif type == 'detections': - sorted_detections, messages = generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE) - elif type == 'stories': - sorted_detections, messages = generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE) - sorted_stories, messages = generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE) - elif type == 'spec': - messages = generate_doc_specs(REPO_PATH, OUTPUT_DIR, messages, VERBOSE) + sorted_detections, messages = generate_doc_detections(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, messages, VERBOSE) + sorted_stories, messages = generate_doc_stories(REPO_PATH, OUTPUT_DIR, TEMPLATE_PATH, attack, sorted_detections, messages, VERBOSE) # print all the messages from generation for m in messages: diff --git a/docs/spec/README.md b/docs/spec/README.md new file mode 100644 index 0000000000..9f2e056264 --- /dev/null +++ b/docs/spec/README.md @@ -0,0 +1,51 @@ +# README + +## Top-level Schemas + +* [Analytics Story Schema](./stories.md "schema analytics story") – `http://example.com/example.json` + +* [Baseline Schema](./baselines.md "schema for baselines") – `http://example.com/example.json` + +* [Deployment Schema](./deployments.md "schema for deployment") – `http://example.com/example.json` + +* [Detection Schema](./detections.md "schema for detections") – `http://example.com/example.json` + +* [Lookup Manifest](./lookups.md "A object that defines a lookup file and its properties") – `https://api.splunkresearch.com/schemas/lookups.json` + +* [Macro Manifest](./macros.md "An object that defines the parameters for a Splunk Macro") – `https://api.splunkresearch.com/schemas/macros.json` + +* [Response Schema](./response_tasks.md "schema for response task") – `https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response_tasks.spec.json` + +* [Response Schema](./responses.md "schema for response") – `https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response.spec.json` + +* [Response Schema](./responses_phase.md "schema for phase") – `http://example.com/example.json` + +## Other Schemas + +### Objects + +* [Untitled object in Baseline Schema](./baselines-properties-tags.md "An array of key value pairs for tagging") – `#/properties/tags#/properties/tags` + +* [Untitled object in Deployment Schema](./deployments-properties-alert_action.md "Set alert action parameter for search") – `#/properties/alert_action#/properties/alert_action` + +* [Untitled object in Deployment Schema](./deployments-properties-alert_action-properties-email.md "By enabling it, an email is sent with the results") – `#/properties/alert_action/properties/email#/properties/alert_action/properties/email` + +* [Untitled object in Deployment Schema](./deployments-properties-alert_action-properties-index.md "By enabling it, the results are stored in another index") – `#/properties/alert_action/properties/index#/properties/alert_action/properties/index` + +* [Untitled object in Deployment Schema](./deployments-properties-alert_action-properties-notable.md "By enabling it, a notable is generated") – `#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable` + +* [Untitled object in Deployment Schema](./deployments-properties-scheduling.md "allows to set scheduling parameter") – `#/properties/scheduling#/properties/scheduling` + +* [Untitled object in Response Schema](./response_tasks-properties-automation.md "An array of key value pairs for defining actions and playbooks") – `#/properties/automation#/properties/automation` + +### Arrays + +* [Untitled array in Baseline Schema](./baselines-properties-datamodel.md "datamodel used in the search") – `#/properties/datamodel#/properties/datamodel` + +* [Untitled array in Detection Schema](./detections-properties-references.md "A list of references for this detection") – `#/properties/references#/properties/references` + +* [Untitled array in Macro Manifest](./macros-properties-arguments.md "A list of the arguments being passed to this macro") – `https://api.splunkresearch.com/schemas/macros.json#/properties/arguments` + +* [Untitled array in Response Schema](./responses-properties-response_phase.md "Response divided into phases") – `#/properties/response_phases#/properties/response_phase` + +* [Untitled array in Response Schema](./responses_phase-properties-response_task.md "Response phase is divided into task(s) to be completed") – `#/properties/response_task#/properties/response_task` diff --git a/docs/spec/baselines-properties-author.md b/docs/spec/baselines-properties-author.md new file mode 100644 index 0000000000..25b1c05002 --- /dev/null +++ b/docs/spec/baselines-properties-author.md @@ -0,0 +1,22 @@ +# Untitled string in Baseline Schema Schema + +```txt +#/properties/author#/properties/author +``` + +Author of the baseline + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## author Type + +`string` + +## author Examples + +```yaml +Bahvin Patel, Splunk + +``` diff --git a/docs/spec/baselines-properties-datamodel-items.md b/docs/spec/baselines-properties-datamodel-items.md new file mode 100644 index 0000000000..c8e5070bfe --- /dev/null +++ b/docs/spec/baselines-properties-datamodel-items.md @@ -0,0 +1,36 @@ +# Untitled string in Baseline Schema Schema + +```txt +#/properties/datamodel#/properties/datamodel/items +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## items Type + +`string` + +## items Constraints + +**enum**: the value of this property must be equal to one of the following values: + +| Value | Explanation | +| :--------------------- | :---------- | +| `"Endpoint"` | | +| `"Network_Traffic"` | | +| `"Authentication"` | | +| `"Change"` | | +| `"Change_Analysis"` | | +| `"Email"` | | +| `"Endpoint"` | | +| `"Network_Resolution"` | | +| `"Network_Sessions"` | | +| `"Network_Traffic"` | | +| `"UEBA"` | | +| `"Updates"` | | +| `"Vulnerabilities"` | | +| `"Web"` | | diff --git a/docs/spec/baselines-properties-datamodel.md b/docs/spec/baselines-properties-datamodel.md new file mode 100644 index 0000000000..8ee4de9690 --- /dev/null +++ b/docs/spec/baselines-properties-datamodel.md @@ -0,0 +1,22 @@ +# Untitled array in Baseline Schema Schema + +```txt +#/properties/datamodel#/properties/datamodel +``` + +datamodel used in the search + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## datamodel Type + +`string[]` + +## datamodel Examples + +```yaml +Endpoint + +``` diff --git a/docs/spec/baselines-properties-date.md b/docs/spec/baselines-properties-date.md new file mode 100644 index 0000000000..f91effacdf --- /dev/null +++ b/docs/spec/baselines-properties-date.md @@ -0,0 +1,22 @@ +# Untitled string in Baseline Schema Schema + +```txt +#/properties/date#/properties/date +``` + +date of creation or modification, format yyyy-mm-dd + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## date Type + +`string` + +## date Examples + +```yaml +'2019-12-06' + +``` diff --git a/docs/spec/baselines-properties-description.md b/docs/spec/baselines-properties-description.md new file mode 100644 index 0000000000..e61031299b --- /dev/null +++ b/docs/spec/baselines-properties-description.md @@ -0,0 +1,26 @@ +# Untitled string in Baseline Schema Schema + +```txt +#/properties/description#/properties/description +``` + +A detailed description of the baseline + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## description Type + +`string` + +## description Examples + +```yaml +>- + This search looks for CloudTrail events where an AWS instance is started and + creates a baseline of most recent time (latest) and the first time (earliest) + we've seen this region in our dataset grouped by the value awsRegion for the + last 30 days + +``` diff --git a/docs/spec/baselines-properties-how_to_implement.md b/docs/spec/baselines-properties-how_to_implement.md new file mode 100644 index 0000000000..0bac7d7e11 --- /dev/null +++ b/docs/spec/baselines-properties-how_to_implement.md @@ -0,0 +1,24 @@ +# Untitled string in Baseline Schema Schema + +```txt +#/properties/how_to_implement#/properties/how_to_implement +``` + +information about how to implement. Only needed for non standard implementations. + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## how_to_implement Type + +`string` + +## how_to_implement Examples + +```yaml +>- + This search requires Sysmon Logs and a Sysmon configuration, which includes + EventCode 10 for lsass.exe. + +``` diff --git a/docs/spec/baselines-properties-id.md b/docs/spec/baselines-properties-id.md new file mode 100644 index 0000000000..0ac191b7fb --- /dev/null +++ b/docs/spec/baselines-properties-id.md @@ -0,0 +1,22 @@ +# Untitled string in Baseline Schema Schema + +```txt +#/properties/id#/properties/id +``` + +UUID as unique identifier + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## id Type + +`string` + +## id Examples + +```yaml +fc0edc95-ff2b-48b0-9f6f-63da3789fd63 + +``` diff --git a/docs/spec/baselines-properties-name-of-baseline.md b/docs/spec/baselines-properties-name-of-baseline.md new file mode 100644 index 0000000000..46e7b91cba --- /dev/null +++ b/docs/spec/baselines-properties-name-of-baseline.md @@ -0,0 +1,22 @@ +# Name of baseline Schema + +```txt +#/properties/name#/properties/name +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## name Type + +`string` ([Name of baseline](baselines-properties-name-of-baseline.md)) + +## name Examples + +```yaml +Previously Seen AWS Regions + +``` diff --git a/docs/spec/baselines-properties-search.md b/docs/spec/baselines-properties-search.md new file mode 100644 index 0000000000..dae22b4ca4 --- /dev/null +++ b/docs/spec/baselines-properties-search.md @@ -0,0 +1,24 @@ +# Untitled string in Baseline Schema Schema + +```txt +#/properties/search#/properties/search +``` + +The Splunk search for the baseline + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## search Type + +`string` + +## search Examples + +```yaml +>- + cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as + latest by awsRegion | outputlookup previously_seen_aws_regions.csv + +``` diff --git a/docs/spec/baselines-properties-tags-default.md b/docs/spec/baselines-properties-tags-default.md new file mode 100644 index 0000000000..6338be86cf --- /dev/null +++ b/docs/spec/baselines-properties-tags-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Baseline Schema Schema + +```txt +#/properties/tags#/properties/tags/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/baselines-properties-tags.md b/docs/spec/baselines-properties-tags.md new file mode 100644 index 0000000000..622015ce22 --- /dev/null +++ b/docs/spec/baselines-properties-tags.md @@ -0,0 +1,47 @@ +# Untitled object in Baseline Schema Schema + +```txt +#/properties/tags#/properties/tags +``` + +An array of key value pairs for tagging + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## tags Type + +`object` ([Details](baselines-properties-tags.md)) + +## tags Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +**unique items**: all items in this array must be unique. Duplicates are not allowed. + +## tags Default Value + +The default value is: + +```json +{} +``` + +## tags Examples + +```yaml +analytic_story: suspicious_aws_ec2_activities +custom_key: custom_value + +``` + +# tags Properties + +| Property | Type | Required | Nullable | Defined by | +| :-------------------- | :--- | :------- | :---------- | :--------- | +| Additional Properties | Any | Optional | can be null | | + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/baselines-properties-version.md b/docs/spec/baselines-properties-version.md new file mode 100644 index 0000000000..2d241c5920 --- /dev/null +++ b/docs/spec/baselines-properties-version.md @@ -0,0 +1,22 @@ +# Untitled integer in Baseline Schema Schema + +```txt +#/properties/version#/properties/version +``` + +version of baseline, e.g. 1 or 2 ... + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | + +## version Type + +`integer` + +## version Examples + +```yaml +1 + +``` diff --git a/docs/spec/baselines.md b/docs/spec/baselines.md index 035cb5a439..133ef8a080 100644 --- a/docs/spec/baselines.md +++ b/docs/spec/baselines.md @@ -1,30 +1,308 @@ -# Baseline Schema +# Baseline Schema Schema + +```txt +http://example.com/example.json +``` + +schema for baselines + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [baselines.spec.json](../../out/baselines.spec.json "open original schema") | + +## Baseline Schema Type + +`object` ([Baseline Schema](baselines.md)) + +# Baseline Schema Properties + +| Property | Type | Required | Nullable | Defined by | +| :------------------------------------ | :-------- | :------- | :------------- | :----------------------------------------------------------------------------------------------------------------------- | +| [author](#author) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-author.md "#/properties/author#/properties/author") | +| [date](#date) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-date.md "#/properties/date#/properties/date") | +| [description](#description) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-description.md "#/properties/description#/properties/description") | +| [how_to_implement](#how_to_implement) | `string` | Optional | cannot be null | [Baseline Schema](baselines-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement") | +| [id](#id) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-id.md "#/properties/id#/properties/id") | +| [name](#name) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-name-of-baseline.md "#/properties/name#/properties/name") | +| [search](#search) | `string` | Required | cannot be null | [Baseline Schema](baselines-properties-search.md "#/properties/search#/properties/search") | +| [tags](#tags) | `object` | Required | cannot be null | [Baseline Schema](baselines-properties-tags.md "#/properties/tags#/properties/tags") | +| [datamodel](#datamodel) | `array` | Optional | cannot be null | [Baseline Schema](baselines-properties-datamodel.md "#/properties/datamodel#/properties/datamodel") | +| [version](#version) | `integer` | Required | cannot be null | [Baseline Schema](baselines-properties-version.md "#/properties/version#/properties/version") | +| Additional Properties | Any | Optional | can be null | | + +## author + +Author of the baseline + +`author` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Baseline Schema](baselines-properties-author.md "#/properties/author#/properties/author") + +### author Type + +`string` + +### author Examples + +```yaml +Bahvin Patel, Splunk + +``` + +## date + +date of creation or modification, format yyyy-mm-dd + +`date` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Baseline Schema](baselines-properties-date.md "#/properties/date#/properties/date") + +### date Type + +`string` + +### date Examples + +```yaml +'2019-12-06' + +``` + +## description + +A detailed description of the baseline + +`description` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Baseline Schema](baselines-properties-description.md "#/properties/description#/properties/description") + +### description Type + +`string` + +### description Examples + +```yaml +>- + This search looks for CloudTrail events where an AWS instance is started and + creates a baseline of most recent time (latest) and the first time (earliest) + we've seen this region in our dataset grouped by the value awsRegion for the + last 30 days + +``` + +## how_to_implement + +information about how to implement. Only needed for non standard implementations. + +`how_to_implement` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Baseline Schema](baselines-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement") + +### how_to_implement Type + +`string` + +### how_to_implement Examples + +```yaml +>- + This search requires Sysmon Logs and a Sysmon configuration, which includes + EventCode 10 for lsass.exe. + +``` + +## id + +UUID as unique identifier + +`id` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Baseline Schema](baselines-properties-id.md "#/properties/id#/properties/id") + +### id Type + +`string` + +### id Examples + +```yaml +fc0edc95-ff2b-48b0-9f6f-63da3789fd63 + +``` + +## name -*schema for baselines* +`name` -## Properties +* is required +* Type: `string` ([Name of baseline](baselines-properties-name-of-baseline.md)) -- **`author`** *(string)*: Author of the baseline. Default: ``. +* cannot be null -- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. +* defined in: [Baseline Schema](baselines-properties-name-of-baseline.md "#/properties/name#/properties/name") -- **`description`** *(string)*: A detailed description of the baseline . Default: ``. +### name Type -- **`how_to_implement`** *(string)*: information about how to implement. Only needed for non standard implementations. Default: ``. +`string` ([Name of baseline](baselines-properties-name-of-baseline.md)) -- **`id`** *(string)*: UUID as unique identifier. Default: ``. +### name Examples -- **`name`** *(string)*: Default: ``. +```yaml +Previously Seen AWS Regions -- **`search`** *(string)*: The Splunk search for the baseline. Default: ``. +``` -- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. +## search -- **`datamodel`** *(array)*: datamodel used in the search. Default: ``. +The Splunk search for the baseline - - **Items** *(string)*: Must be one of: `['Endpoint', 'Network_Traffic', 'Authentication', 'Change', 'Change_Analysis', 'Email', 'Endpoint', 'Network_Resolution', 'Network_Sessions', 'Network_Traffic', 'UEBA', 'Updates', 'Vulnerabilities', 'Web']`. +`search` -- **`version`** *(integer)*: version of baseline, e.g. 1 or 2 ... Default: `0`. +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Baseline Schema](baselines-properties-search.md "#/properties/search#/properties/search") + +### search Type + +`string` + +### search Examples + +```yaml +>- + cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as + latest by awsRegion | outputlookup previously_seen_aws_regions.csv + +``` + +## tags + +An array of key value pairs for tagging + +`tags` + +* is required + +* Type: `object` ([Details](baselines-properties-tags.md)) + +* cannot be null + +* defined in: [Baseline Schema](baselines-properties-tags.md "#/properties/tags#/properties/tags") + +### tags Type + +`object` ([Details](baselines-properties-tags.md)) + +### tags Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +**unique items**: all items in this array must be unique. Duplicates are not allowed. + +### tags Default Value + +The default value is: + +```json +{} +``` + +### tags Examples + +```yaml +analytic_story: suspicious_aws_ec2_activities +custom_key: custom_value + +``` + +## datamodel + +datamodel used in the search + +`datamodel` + +* is optional + +* Type: `string[]` + +* cannot be null + +* defined in: [Baseline Schema](baselines-properties-datamodel.md "#/properties/datamodel#/properties/datamodel") + +### datamodel Type + +`string[]` + +### datamodel Examples + +```yaml +Endpoint + +``` + +## version + +version of baseline, e.g. 1 or 2 ... + +`version` + +* is required + +* Type: `integer` + +* cannot be null + +* defined in: [Baseline Schema](baselines-properties-version.md "#/properties/version#/properties/version") + +### version Type + +`integer` + +### version Examples + +```yaml +1 + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-default.md b/docs/spec/deployments-default.md new file mode 100644 index 0000000000..1a7b518209 --- /dev/null +++ b/docs/spec/deployments-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Deployment Schema Schema + +```txt +http://example.com/example.json#/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/deployments-properties-alert_action-default.md b/docs/spec/deployments-properties-alert_action-default.md new file mode 100644 index 0000000000..55e2308c13 --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Deployment Schema Schema + +```txt +#/properties/alert_action#/properties/alert_action/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-email-default.md b/docs/spec/deployments-properties-alert_action-properties-email-default.md new file mode 100644 index 0000000000..c8b32006bb --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-email-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/email#/properties/alert_action/properties/email/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md new file mode 100644 index 0000000000..e01fa30a39 --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md @@ -0,0 +1,22 @@ +# Untitled string in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message +``` + +message of email + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## message Type + +`string` + +## message Examples + +```yaml +Splunk Alert $name$ triggered %fields% + +``` diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md new file mode 100644 index 0000000000..e72fd29af3 --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md @@ -0,0 +1,22 @@ +# Untitled string in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject +``` + +Subject of email + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## subject Type + +`string` + +## subject Examples + +```yaml +Splunk Alert $name$ + +``` diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md new file mode 100644 index 0000000000..0bef21838f --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md @@ -0,0 +1,22 @@ +# Untitled string in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to +``` + +Recipient of email + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## to Type + +`string` + +## to Examples + +```yaml +test@test.com + +``` diff --git a/docs/spec/deployments-properties-alert_action-properties-email.md b/docs/spec/deployments-properties-alert_action-properties-email.md new file mode 100644 index 0000000000..070e95ee64 --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-email.md @@ -0,0 +1,120 @@ +# Untitled object in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/email#/properties/alert_action/properties/email +``` + +By enabling it, an email is sent with the results + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## email Type + +`object` ([Details](deployments-properties-alert_action-properties-email.md)) + +## email Default Value + +The default value is: + +```json +{} +``` + +## email Examples + +```yaml +message: Splunk Alert $name$ triggered %fields% +subject: Splunk Alert $name$ +to: test@test.com + +``` + +# email Properties + +| Property | Type | Required | Nullable | Defined by | +| :-------------------- | :------- | :------- | :------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| [message](#message) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-message.md "#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message") | +| [subject](#subject) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-subject.md "#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject") | +| [to](#to) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-to.md "#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to") | +| Additional Properties | Any | Optional | can be null | | + +## message + +message of email + +`message` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-message.md "#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message") + +### message Type + +`string` + +### message Examples + +```yaml +Splunk Alert $name$ triggered %fields% + +``` + +## subject + +Subject of email + +`subject` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-subject.md "#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject") + +### subject Type + +`string` + +### subject Examples + +```yaml +Splunk Alert $name$ + +``` + +## to + +Recipient of email + +`to` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-to.md "#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to") + +### to Type + +`string` + +### to Examples + +```yaml +test@test.com + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-alert_action-properties-index-default.md b/docs/spec/deployments-properties-alert_action-properties-index-default.md new file mode 100644 index 0000000000..0acb5762ae --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-index-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/index#/properties/alert_action/properties/index/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md b/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md new file mode 100644 index 0000000000..be9a681a97 --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md @@ -0,0 +1,22 @@ +# Untitled string in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name +``` + +Name of the index + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## name Type + +`string` + +## name Examples + +```yaml +asx + +``` diff --git a/docs/spec/deployments-properties-alert_action-properties-index.md b/docs/spec/deployments-properties-alert_action-properties-index.md new file mode 100644 index 0000000000..edc5070f0e --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-index.md @@ -0,0 +1,66 @@ +# Untitled object in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/index#/properties/alert_action/properties/index +``` + +By enabling it, the results are stored in another index + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## index Type + +`object` ([Details](deployments-properties-alert_action-properties-index.md)) + +## index Default Value + +The default value is: + +```json +{} +``` + +## index Examples + +```yaml +name: asx + +``` + +# index Properties + +| Property | Type | Required | Nullable | Defined by | +| :-------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| [name](#name) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-index-properties-name.md "#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name") | +| Additional Properties | Any | Optional | can be null | | + +## name + +Name of the index + +`name` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-alert_action-properties-index-properties-name.md "#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name") + +### name Type + +`string` + +### name Examples + +```yaml +asx + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-default.md b/docs/spec/deployments-properties-alert_action-properties-notable-default.md new file mode 100644 index 0000000000..84226f6fc7 --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-notable-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md new file mode 100644 index 0000000000..23882cd515 --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md @@ -0,0 +1,22 @@ +# Untitled string in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description +``` + +Rule description of the notable event + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## rule_description Type + +`string` + +## rule_description Examples + +```yaml +'%description%' + +``` diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md new file mode 100644 index 0000000000..9d5eb151c2 --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md @@ -0,0 +1,22 @@ +# Untitled string in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title +``` + +Rule title of the notable event + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## rule_title Type + +`string` + +## rule_title Examples + +```yaml +'%name%' + +``` diff --git a/docs/spec/deployments-properties-alert_action-properties-notable.md b/docs/spec/deployments-properties-alert_action-properties-notable.md new file mode 100644 index 0000000000..d117f24ef9 --- /dev/null +++ b/docs/spec/deployments-properties-alert_action-properties-notable.md @@ -0,0 +1,93 @@ +# Untitled object in Deployment Schema Schema + +```txt +#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable +``` + +By enabling it, a notable is generated + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## notable Type + +`object` ([Details](deployments-properties-alert_action-properties-notable.md)) + +## notable Default Value + +The default value is: + +```json +{} +``` + +## notable Examples + +```yaml +rule_description: '%description%' +rule_title: '%name%' + +``` + +# notable Properties + +| Property | Type | Required | Nullable | Defined by | +| :------------------------------------ | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| [rule_description](#rule_description) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_description.md "#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description") | +| [rule_title](#rule_title) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_title.md "#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title") | +| Additional Properties | Any | Optional | can be null | | + +## rule_description + +Rule description of the notable event + +`rule_description` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_description.md "#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description") + +### rule_description Type + +`string` + +### rule_description Examples + +```yaml +'%description%' + +``` + +## rule_title + +Rule title of the notable event + +`rule_title` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_title.md "#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title") + +### rule_title Type + +`string` + +### rule_title Examples + +```yaml +'%name%' + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-alert_action.md b/docs/spec/deployments-properties-alert_action.md new file mode 100644 index 0000000000..f5fa09670a --- /dev/null +++ b/docs/spec/deployments-properties-alert_action.md @@ -0,0 +1,153 @@ +# Untitled object in Deployment Schema Schema + +```txt +#/properties/alert_action#/properties/alert_action +``` + +Set alert action parameter for search + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## alert_action Type + +`object` ([Details](deployments-properties-alert_action.md)) + +## alert_action Default Value + +The default value is: + +```json +{} +``` + +## alert_action Examples + +```yaml +email: + message: Splunk Alert $name$ triggered %fields% + subject: Splunk Alert $name$ + to: test@test.com +index: + name: asx +notable: + rule_description: '%description%' + rule_title: '%name%' + +``` + +# alert_action Properties + +| Property | Type | Required | Nullable | Defined by | +| :-------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| [email](#email) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email.md "#/properties/alert_action/properties/email#/properties/alert_action/properties/email") | +| [index](#index) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-index.md "#/properties/alert_action/properties/index#/properties/alert_action/properties/index") | +| [notable](#notable) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable.md "#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable") | +| Additional Properties | Any | Optional | can be null | | + +## email + +By enabling it, an email is sent with the results + +`email` + +* is optional + +* Type: `object` ([Details](deployments-properties-alert_action-properties-email.md)) + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email.md "#/properties/alert_action/properties/email#/properties/alert_action/properties/email") + +### email Type + +`object` ([Details](deployments-properties-alert_action-properties-email.md)) + +### email Default Value + +The default value is: + +```json +{} +``` + +### email Examples + +```yaml +message: Splunk Alert $name$ triggered %fields% +subject: Splunk Alert $name$ +to: test@test.com + +``` + +## index + +By enabling it, the results are stored in another index + +`index` + +* is optional + +* Type: `object` ([Details](deployments-properties-alert_action-properties-index.md)) + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-alert_action-properties-index.md "#/properties/alert_action/properties/index#/properties/alert_action/properties/index") + +### index Type + +`object` ([Details](deployments-properties-alert_action-properties-index.md)) + +### index Default Value + +The default value is: + +```json +{} +``` + +### index Examples + +```yaml +name: asx + +``` + +## notable + +By enabling it, a notable is generated + +`notable` + +* is optional + +* Type: `object` ([Details](deployments-properties-alert_action-properties-notable.md)) + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable.md "#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable") + +### notable Type + +`object` ([Details](deployments-properties-alert_action-properties-notable.md)) + +### notable Default Value + +The default value is: + +```json +{} +``` + +### notable Examples + +```yaml +rule_description: '%description%' +rule_title: '%name%' + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-scheduling-default.md b/docs/spec/deployments-properties-scheduling-default.md new file mode 100644 index 0000000000..f39294b0a3 --- /dev/null +++ b/docs/spec/deployments-properties-scheduling-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Deployment Schema Schema + +```txt +#/properties/scheduling#/properties/scheduling/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md b/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md new file mode 100644 index 0000000000..52cbbfae51 --- /dev/null +++ b/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md @@ -0,0 +1,22 @@ +# Untitled string in Deployment Schema Schema + +```txt +#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule +``` + +Cron schedule to schedule the Splunk searches. + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## cron_schedule Type + +`string` + +## cron_schedule Examples + +```yaml +'*/10 * * * *' + +``` diff --git a/docs/spec/deployments-properties-scheduling-properties-earliest_time.md b/docs/spec/deployments-properties-scheduling-properties-earliest_time.md new file mode 100644 index 0000000000..5808ddd97f --- /dev/null +++ b/docs/spec/deployments-properties-scheduling-properties-earliest_time.md @@ -0,0 +1,22 @@ +# Untitled string in Deployment Schema Schema + +```txt +#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time +``` + +earliest time of search + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## earliest_time Type + +`string` + +## earliest_time Examples + +```yaml +'-10m' + +``` diff --git a/docs/spec/deployments-properties-scheduling-properties-latest_time.md b/docs/spec/deployments-properties-scheduling-properties-latest_time.md new file mode 100644 index 0000000000..566141825e --- /dev/null +++ b/docs/spec/deployments-properties-scheduling-properties-latest_time.md @@ -0,0 +1,22 @@ +# Untitled string in Deployment Schema Schema + +```txt +#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time +``` + +latest time of search + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## latest_time Type + +`string` + +## latest_time Examples + +```yaml +now + +``` diff --git a/docs/spec/deployments-properties-scheduling-properties-schedule_window.md b/docs/spec/deployments-properties-scheduling-properties-schedule_window.md new file mode 100644 index 0000000000..41aad873d4 --- /dev/null +++ b/docs/spec/deployments-properties-scheduling-properties-schedule_window.md @@ -0,0 +1,22 @@ +# Untitled string in Deployment Schema Schema + +```txt +#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window +``` + +schedule window for search + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## schedule_window Type + +`string` + +## schedule_window Examples + +```yaml +auto + +``` diff --git a/docs/spec/deployments-properties-scheduling.md b/docs/spec/deployments-properties-scheduling.md new file mode 100644 index 0000000000..cf64fb3727 --- /dev/null +++ b/docs/spec/deployments-properties-scheduling.md @@ -0,0 +1,147 @@ +# Untitled object in Deployment Schema Schema + +```txt +#/properties/scheduling#/properties/scheduling +``` + +allows to set scheduling parameter + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | + +## scheduling Type + +`object` ([Details](deployments-properties-scheduling.md)) + +## scheduling Default Value + +The default value is: + +```json +{} +``` + +## scheduling Examples + +```yaml +cron_schedule: '*/10 * * * *' +earliest_time: '-10m' +latest_time: now +schedule_window: auto + +``` + +# scheduling Properties + +| Property | Type | Required | Nullable | Defined by | +| :---------------------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| [cron_schedule](#cron_schedule) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-cron_schedule.md "#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule") | +| [earliest_time](#earliest_time) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-earliest_time.md "#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time") | +| [latest_time](#latest_time) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-latest_time.md "#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time") | +| [schedule_window](#schedule_window) | `string` | Optional | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-schedule_window.md "#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window") | +| Additional Properties | Any | Optional | can be null | | + +## cron_schedule + +Cron schedule to schedule the Splunk searches. + +`cron_schedule` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-scheduling-properties-cron_schedule.md "#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule") + +### cron_schedule Type + +`string` + +### cron_schedule Examples + +```yaml +'*/10 * * * *' + +``` + +## earliest_time + +earliest time of search + +`earliest_time` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-scheduling-properties-earliest_time.md "#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time") + +### earliest_time Type + +`string` + +### earliest_time Examples + +```yaml +'-10m' + +``` + +## latest_time + +latest time of search + +`latest_time` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-scheduling-properties-latest_time.md "#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time") + +### latest_time Type + +`string` + +### latest_time Examples + +```yaml +now + +``` + +## schedule_window + +schedule window for search + +`schedule_window` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-scheduling-properties-schedule_window.md "#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window") + +### schedule_window Type + +`string` + +### schedule_window Examples + +```yaml +auto + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments.md b/docs/spec/deployments.md index ac112057c9..4d1597e99c 100644 --- a/docs/spec/deployments.md +++ b/docs/spec/deployments.md @@ -1,48 +1,258 @@ -# Deployment Schema +# Deployment Schema Schema +```txt +http://example.com/example.json +``` -*schema for deployment* +schema for deployment +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------ | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json](../../out/deployments.spec.json "open original schema") | -## Properties +## Deployment Schema Type +`object` ([Deployment Schema](deployments.md)) -- **`alert_action`** *(object)*: Set alert action parameter for search. Can contain additional properties. Default: `{}`. +## Deployment Schema Default Value - - **`email`** *(object)*: By enabling it, an email is sent with the results. Can contain additional properties. Default: `{}`. +The default value is: - - **`message`** *(string)*: message of email. Default: ``. +```json +{} +``` - - **`subject`** *(string)*: Subject of email. Default: ``. +# Deployment Schema Properties - - **`to`** *(string)*: Recipient of email. Default: ``. +| Property | Type | Required | Nullable | Defined by | +| :---------------------------- | :------- | :------- | :------------- | :--------------------------------------------------------------------------------------------------------------- | +| [alert_action](#alert_action) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action.md "#/properties/alert_action#/properties/alert_action") | +| [date](#date) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-date.md "#/properties/date#/properties/date") | +| [description](#description) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-description.md "#/properties/description#/properties/description") | +| [id](#id) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-id.md "#/properties/id#/properties/id") | +| [name](#name) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-name.md "#/properties/name#/properties/name") | +| [scheduling](#scheduling) | `object` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling.md "#/properties/scheduling#/properties/scheduling") | +| [tags](#tags) | `object` | Required | cannot be null | [Deployment Schema](deployments-properties-tags.md "#/properties/tags#/properties/tags") | +| Additional Properties | Any | Optional | can be null | | - - **`index`** *(object)*: By enabling it, the results are stored in another index. Can contain additional properties. Default: `{}`. +## alert_action - - **`name`** *(string)*: Name of the index. Default: ``. +Set alert action parameter for search - - **`notable`** *(object)*: By enabling it, a notable is generated. Can contain additional properties. Default: `{}`. +`alert_action` - - **`rule_description`** *(string)*: Rule description of the notable event. Default: ``. +* is optional - - **`rule_title`** *(string)*: Rule title of the notable event. Default: ``. +* Type: `object` ([Details](deployments-properties-alert_action.md)) -- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. +* cannot be null -- **`description`** *(string)*: description of the deployment configuration. Default: ``. +* defined in: [Deployment Schema](deployments-properties-alert_action.md "#/properties/alert_action#/properties/alert_action") -- **`id`** *(string)*: uuid as unique identifier. Default: ``. +### alert_action Type -- **`name`** *(string)*: Name of deployment configuration. Default: ``. +`object` ([Details](deployments-properties-alert_action.md)) -- **`scheduling`** *(object)*: allows to set scheduling parameter. Can contain additional properties. Default: `{}`. +### alert_action Default Value - - **`cron_schedule`** *(string)*: Cron schedule to schedule the Splunk searches. Default: ``. +The default value is: - - **`earliest_time`** *(string)*: earliest time of search. Default: ``. +```json +{} +``` - - **`latest_time`** *(string)*: latest time of search. Default: ``. +### alert_action Examples - - **`schedule_window`** *(string)*: schedule window for search. Default: ``. +```yaml +email: + message: Splunk Alert $name$ triggered %fields% + subject: Splunk Alert $name$ + to: test@test.com +index: + name: asx +notable: + rule_description: '%description%' + rule_title: '%name%' -- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. +``` + +## date + +date of creation or modification, format yyyy-mm-dd + +`date` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-date.md "#/properties/date#/properties/date") + +### date Type + +`string` + +### date Examples + +```yaml +'2019-12-06' + +``` + +## description + +description of the deployment configuration + +`description` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-description.md "#/properties/description#/properties/description") + +### description Type + +`string` + +### description Examples + +```yaml +>- + This deployment configuration provides a standard scheduling policy over all + rules. + +``` + +## id + +uuid as unique identifier + +`id` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-id.md "#/properties/id#/properties/id") + +### id Type + +`string` + +### id Examples + +```yaml +fb4c31b0-13e8-4155-8aa5-24de4b8d6717 + +``` + +## name + +Name of deployment configuration + +`name` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-name.md "#/properties/name#/properties/name") + +### name Type + +`string` + +### name Examples + +```yaml +Deployment Configuration all Detections + +``` + +## scheduling + +allows to set scheduling parameter + +`scheduling` + +* is required + +* Type: `object` ([Details](deployments-properties-scheduling.md)) + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-scheduling.md "#/properties/scheduling#/properties/scheduling") + +### scheduling Type + +`object` ([Details](deployments-properties-scheduling.md)) + +### scheduling Default Value + +The default value is: + +```json +{} +``` + +### scheduling Examples + +```yaml +cron_schedule: '*/10 * * * *' +earliest_time: '-10m' +latest_time: now +schedule_window: auto + +``` + +## tags + +An array of key value pairs for tagging + +`tags` + +* is required + +* Type: `object` ([Details](deployments-properties-tags.md)) + +* cannot be null + +* defined in: [Deployment Schema](deployments-properties-tags.md "#/properties/tags#/properties/tags") + +### tags Type + +`object` ([Details](deployments-properties-tags.md)) + +### tags Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +**unique items**: all items in this array must be unique. Duplicates are not allowed. + +### tags Default Value + +The default value is: + +```json +{} +``` + +### tags Examples + +```yaml +analytic_story: credential_dumping + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/detections-properties-known_false_positives.md b/docs/spec/detections-properties-known_false_positives.md new file mode 100644 index 0000000000..ae7c1bafbd --- /dev/null +++ b/docs/spec/detections-properties-known_false_positives.md @@ -0,0 +1,24 @@ +# Untitled string in Detection Schema Schema + +```txt +#/properties/knwon_false_positives#/properties/known_false_positives +``` + +known false postives + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | + +## known_false_positives Type + +`string` + +## known_false_positives Examples + +```yaml +>- + Administrators can create memory dumps for debugging purposes, but memory + dumps of the LSASS process would be unusual. + +``` diff --git a/docs/spec/detections-properties-references-the-items-schema.md b/docs/spec/detections-properties-references-the-items-schema.md new file mode 100644 index 0000000000..c184428f27 --- /dev/null +++ b/docs/spec/detections-properties-references-the-items-schema.md @@ -0,0 +1,23 @@ +# The Items Schema Schema + +```txt +#/properties/references/items#/properties/references/items +``` + +An explanation about the purpose of this instance. + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | + +## items Type + +`string` ([The Items Schema](detections-properties-references-the-items-schema.md)) + +## items Examples + +```yaml +>- + https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +``` diff --git a/docs/spec/detections-properties-references.md b/docs/spec/detections-properties-references.md new file mode 100644 index 0000000000..07618fc3f0 --- /dev/null +++ b/docs/spec/detections-properties-references.md @@ -0,0 +1,31 @@ +# Untitled array in Detection Schema Schema + +```txt +#/properties/references#/properties/references +``` + +A list of references for this detection + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | + +## references Type + +`string[]` ([The Items Schema](detections-properties-references-the-items-schema.md)) + +## references Default Value + +The default value is: + +```json +[] +``` + +## references Examples + +```yaml +- >- + https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +``` diff --git a/docs/spec/detections-properties-type-items.md b/docs/spec/detections-properties-type-items.md new file mode 100644 index 0000000000..226980c0e6 --- /dev/null +++ b/docs/spec/detections-properties-type-items.md @@ -0,0 +1,24 @@ +# Untitled string in Detection Schema Schema + +```txt +#/properties/type#/properties/type/items +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | + +## items Type + +`string` + +## items Constraints + +**enum**: the value of this property must be equal to one of the following values: + +| Value | Explanation | +| :------------ | :---------- | +| `"batch"` | | +| `"streaming"` | | diff --git a/docs/spec/detections-properties-type.md b/docs/spec/detections-properties-type.md new file mode 100644 index 0000000000..98c36f20ac --- /dev/null +++ b/docs/spec/detections-properties-type.md @@ -0,0 +1,22 @@ +# Untitled string in Detection Schema Schema + +```txt +#/properties/type#/properties/type +``` + +type of detection + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | + +## type Type + +`string` + +## type Examples + +```yaml +streaming + +``` diff --git a/docs/spec/detections.md b/docs/spec/detections.md index ce5455c13c..e2d8a0a501 100644 --- a/docs/spec/detections.md +++ b/docs/spec/detections.md @@ -1,40 +1,410 @@ -# Detection Schema +# Detection Schema Schema + +```txt +http://example.com/example.json +``` + +schema for detections + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [detections.spec.json](../../out/detections.spec.json "open original schema") | + +## Detection Schema Type + +`object` ([Detection Schema](detections.md)) + +# Detection Schema Properties + +| Property | Type | Required | Nullable | Defined by | +| :---------------------------------------------- | :-------- | :------- | :------------- | :---------------------------------------------------------------------------------------------------------------------------------------- | +| [author](#author) | `string` | Required | cannot be null | [Detection Schema](detections-properties-author.md "#/properties/author#/properties/author") | +| [date](#date) | `string` | Required | cannot be null | [Detection Schema](detections-properties-date.md "#/properties/date#/properties/date") | +| [description](#description) | `string` | Required | cannot be null | [Detection Schema](detections-properties-description.md "#/properties/description#/properties/description") | +| [how_to_implement](#how_to_implement) | `string` | Optional | cannot be null | [Detection Schema](detections-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement") | +| [id](#id) | `string` | Required | cannot be null | [Detection Schema](detections-properties-id.md "#/properties/id#/properties/id") | +| [known_false_positives](#known_false_positives) | `string` | Required | cannot be null | [Detection Schema](detections-properties-known_false_positives.md "#/properties/knwon_false_positives#/properties/known_false_positives") | +| [name](#name) | `string` | Required | cannot be null | [Detection Schema](detections-properties-name-of-detection.md "#/properties/name#/properties/name") | +| [references](#references) | `array` | Optional | cannot be null | [Detection Schema](detections-properties-references.md "#/properties/references#/properties/references") | +| [search](#search) | `string` | Required | cannot be null | [Detection Schema](detections-properties-search.md "#/properties/search#/properties/search") | +| [tags](#tags) | `object` | Required | cannot be null | [Detection Schema](detections-properties-tags.md "#/properties/tags#/properties/tags") | +| [type](#type) | `string` | Required | cannot be null | [Detection Schema](detections-properties-type.md "#/properties/type#/properties/type") | +| [datamodel](#datamodel) | `array` | Optional | cannot be null | [Detection Schema](detections-properties-datamodel.md "#/properties/datamodel#/properties/datamodel") | +| [version](#version) | `integer` | Required | cannot be null | [Detection Schema](detections-properties-version.md "#/properties/version#/properties/version") | +| Additional Properties | Any | Optional | can be null | | + +## author + +Author of the detection + +`author` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Detection Schema](detections-properties-author.md "#/properties/author#/properties/author") + +### author Type + +`string` + +### author Examples + +```yaml +Patrick Bareiss, Splunk + +``` + +## date + +date of creation or modification, format yyyy-mm-dd + +`date` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Detection Schema](detections-properties-date.md "#/properties/date#/properties/date") + +### date Type + +`string` + +### date Examples + +```yaml +'2019-12-06' + +``` + +## description + +A detailed description of the detection + +`description` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Detection Schema](detections-properties-description.md "#/properties/description#/properties/description") + +### description Type + +`string` + +### description Examples + +```yaml +>- + dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain + a memory dump of a process. This search detects the usage of this DLL for + creating a memory dump of LSASS process. Memory dumps of the LSASS process can + be created with tools such as Windows Task Manager or procdump. + +``` + +## how_to_implement + +information about how to implement. Only needed for non standard implementations. + +`how_to_implement` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Detection Schema](detections-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement") + +### how_to_implement Type + +`string` + +### how_to_implement Examples + +```yaml +>- + This search requires Sysmon Logs and a Sysmon configuration, which includes + EventCode 10 for lsass.exe. + +``` + +## id + +UUID as unique identifier + +`id` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Detection Schema](detections-properties-id.md "#/properties/id#/properties/id") + +### id Type + +`string` + +### id Examples + +```yaml +fb4c31b0-13e8-4155-8aa5-24de4b8d6717 + +``` + +## known_false_positives + +known false postives + +`known_false_positives` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Detection Schema](detections-properties-known_false_positives.md "#/properties/knwon_false_positives#/properties/known_false_positives") + +### known_false_positives Type + +`string` + +### known_false_positives Examples + +```yaml +>- + Administrators can create memory dumps for debugging purposes, but memory + dumps of the LSASS process would be unusual. + +``` + +## name -*schema for detections* +`name` -## Properties +* is required +* Type: `string` ([Name of detection](detections-properties-name-of-detection.md)) -- **`author`** *(string)*: Author of the detection. Default: ``. +* cannot be null -- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. +* defined in: [Detection Schema](detections-properties-name-of-detection.md "#/properties/name#/properties/name") -- **`description`** *(string)*: A detailed description of the detection. Default: ``. +### name Type -- **`how_to_implement`** *(string)*: information about how to implement. Only needed for non standard implementations. Default: ``. +`string` ([Name of detection](detections-properties-name-of-detection.md)) -- **`id`** *(string)*: UUID as unique identifier. Default: ``. +### name Examples -- **`known_false_positives`** *(string)*: known false postives. Default: ``. +```yaml +Access LSASS Memory for Dump Creation -- **`name`** *(string)*: Default: ``. +``` -- **`references`** *(array)*: A list of references for this detection. Default: `[]`. +## references - - **Items** *(string)*: An explanation about the purpose of this instance. Default: ``. +A list of references for this detection -- **`search`** *(string)*: The Splunk search for the detection. Default: ``. +`references` -- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. +* is optional -- **`type`** *(string)*: type of detection. Default: ``. +* Type: `string[]` ([The Items Schema](detections-properties-references-the-items-schema.md)) - - **Items** *(string)*: Must be one of: `['batch', 'streaming']`. +* cannot be null -- **`datamodel`** *(array)*: datamodel used in the search. Default: ``. +* defined in: [Detection Schema](detections-properties-references.md "#/properties/references#/properties/references") - - **Items** *(string)*: Must be one of: `['Endpoint', 'Network_Traffic', 'Authentication', 'Change', 'Change_Analysis', 'Email', 'Endpoint', 'Network_Resolution', 'Network_Sessions', 'Network_Traffic', 'UEBA', 'Updates', 'Vulnerabilities', 'Web']`. +### references Type -- **`version`** *(integer)*: version of detection, e.g. 1 or 2 ... Default: `0`. +`string[]` ([The Items Schema](detections-properties-references-the-items-schema.md)) + +### references Default Value + +The default value is: + +```json +[] +``` + +### references Examples + +```yaml +- >- + https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + +``` + +## search + +The Splunk search for the detection + +`search` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Detection Schema](detections-properties-search.md "#/properties/search#/properties/search") + +### search Type + +`string` + +### search Examples + +```yaml +>- + `sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR + CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as + lastTime by Computer, TargetImage, TargetProcessId, SourceImage, + SourceProcessId | rename Computer as dest | + `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | + `access_lsass_memory_for_dump_creation_filter` + +``` + +## tags + +An array of key value pairs for tagging + +`tags` + +* is required + +* Type: `object` ([Details](detections-properties-tags.md)) + +* cannot be null + +* defined in: [Detection Schema](detections-properties-tags.md "#/properties/tags#/properties/tags") + +### tags Type + +`object` ([Details](detections-properties-tags.md)) + +### tags Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +**unique items**: all items in this array must be unique. Duplicates are not allowed. + +### tags Default Value + +The default value is: + +```json +{} +``` + +### tags Examples + +```yaml +analytic_story: credential_dumping +kill_chain_phases: Action on Objectives +mitre_attack_id: T1078.004 +cis20: CIS 13 +nist: DE.DP +security domain: network +asset_type: AWS Instance +risk_object: user +risk_object_type: network_artifacts +risk score: '60' +custom_key: custom_value + +``` + +## type + +type of detection + +`type` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Detection Schema](detections-properties-type.md "#/properties/type#/properties/type") + +### type Type + +`string` + +### type Examples + +```yaml +streaming + +``` + +## datamodel + +datamodel used in the search + +`datamodel` + +* is optional + +* Type: `string[]` + +* cannot be null + +* defined in: [Detection Schema](detections-properties-datamodel.md "#/properties/datamodel#/properties/datamodel") + +### datamodel Type + +`string[]` + +### datamodel Examples + +```yaml +Endpoint + +``` + +## version + +version of detection, e.g. 1 or 2 ... + +`version` + +* is required + +* Type: `integer` + +* cannot be null + +* defined in: [Detection Schema](detections-properties-version.md "#/properties/version#/properties/version") + +### version Type + +`integer` + +### version Examples + +```yaml +2 + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/lookups-oneof-0.md b/docs/spec/lookups-oneof-0.md new file mode 100644 index 0000000000..78403ade45 --- /dev/null +++ b/docs/spec/lookups-oneof-0.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/oneOf/0 +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## 0 Type + +unknown diff --git a/docs/spec/lookups-oneof-1.md b/docs/spec/lookups-oneof-1.md new file mode 100644 index 0000000000..4b493c4f17 --- /dev/null +++ b/docs/spec/lookups-oneof-1.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/oneOf/1 +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## 1 Type + +unknown diff --git a/docs/spec/lookups-properties-case_sensitive_match.md b/docs/spec/lookups-properties-case_sensitive_match.md new file mode 100644 index 0000000000..b067278e67 --- /dev/null +++ b/docs/spec/lookups-properties-case_sensitive_match.md @@ -0,0 +1,31 @@ +# Untitled string in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/properties/case_sensitive_match +``` + +What the macro is intended to filter + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## case_sensitive_match Type + +`string` + +## case_sensitive_match Constraints + +**enum**: the value of this property must be equal to one of the following values: + +| Value | Explanation | +| :-------- | :---------- | +| `"true"` | | +| `"false"` | | + +## case_sensitive_match Examples + +```yaml +'true' + +``` diff --git a/docs/spec/lookups-properties-collection.md b/docs/spec/lookups-properties-collection.md new file mode 100644 index 0000000000..62b7028b4d --- /dev/null +++ b/docs/spec/lookups-properties-collection.md @@ -0,0 +1,22 @@ +# Untitled string in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/properties/collection +``` + +Name of the collection to use for this lookup + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## collection Type + +`string` + +## collection Examples + +```yaml +prohibited_apps_launching_cmd + +``` diff --git a/docs/spec/lookups-properties-default_match.md b/docs/spec/lookups-properties-default_match.md new file mode 100644 index 0000000000..46369bbdda --- /dev/null +++ b/docs/spec/lookups-properties-default_match.md @@ -0,0 +1,22 @@ +# Untitled string in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/properties/default_match +``` + +The default value if no match is found + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## default_match Type + +`string` + +## default_match Examples + +```yaml +'true' + +``` diff --git a/docs/spec/lookups-properties-description.md b/docs/spec/lookups-properties-description.md new file mode 100644 index 0000000000..e9fc0cfe11 --- /dev/null +++ b/docs/spec/lookups-properties-description.md @@ -0,0 +1,22 @@ +# Untitled string in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/properties/description +``` + +The description of this lookup + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## description Type + +`string` + +## description Examples + +```yaml +This lookup contains file names that exist in the Windows\System32 directory + +``` diff --git a/docs/spec/lookups-properties-fields_list.md b/docs/spec/lookups-properties-fields_list.md new file mode 100644 index 0000000000..e0a699ee17 --- /dev/null +++ b/docs/spec/lookups-properties-fields_list.md @@ -0,0 +1,22 @@ +# Untitled string in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/properties/fields_list +``` + +A comma and space separated list of field names + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## fields_list Type + +`string` + +## fields_list Examples + +```yaml +_key, dest, process_name + +``` diff --git a/docs/spec/lookups-properties-filename.md b/docs/spec/lookups-properties-filename.md new file mode 100644 index 0000000000..80584c0215 --- /dev/null +++ b/docs/spec/lookups-properties-filename.md @@ -0,0 +1,22 @@ +# Untitled string in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/properties/filename +``` + +The name of the file to use for this lookup + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## filename Type + +`string` + +## filename Examples + +```yaml +prohibited_apps_launching_cmd.csv + +``` diff --git a/docs/spec/lookups-properties-filter.md b/docs/spec/lookups-properties-filter.md new file mode 100644 index 0000000000..ad1a68ff22 --- /dev/null +++ b/docs/spec/lookups-properties-filter.md @@ -0,0 +1,22 @@ +# Untitled string in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/properties/filter +``` + +Use this attribute to improve search performance when working with significantly large KV + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## filter Type + +`string` + +## filter Examples + +```yaml +dest="SPLK_*" + +``` diff --git a/docs/spec/lookups-properties-match_type.md b/docs/spec/lookups-properties-match_type.md new file mode 100644 index 0000000000..3ca467f6a8 --- /dev/null +++ b/docs/spec/lookups-properties-match_type.md @@ -0,0 +1,22 @@ +# Untitled string in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/properties/match_type +``` + +A comma and space-delimited list of \(\) specification to allow for non-exact matching + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## match_type Type + +`string` + +## match_type Examples + +```yaml +WILDCARD(process) + +``` diff --git a/docs/spec/lookups-properties-max_matches.md b/docs/spec/lookups-properties-max_matches.md new file mode 100644 index 0000000000..652395498b --- /dev/null +++ b/docs/spec/lookups-properties-max_matches.md @@ -0,0 +1,22 @@ +# Untitled integer in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/properties/max_matches +``` + +The maximum number of possible matches for each input lookup value + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## max_matches Type + +`integer` + +## max_matches Examples + +```yaml +'100' + +``` diff --git a/docs/spec/lookups-properties-min_matches.md b/docs/spec/lookups-properties-min_matches.md new file mode 100644 index 0000000000..e950b97fbd --- /dev/null +++ b/docs/spec/lookups-properties-min_matches.md @@ -0,0 +1,22 @@ +# Untitled integer in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/properties/min_matches +``` + +Minimum number of possible matches for each input lookup value + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## min_matches Type + +`integer` + +## min_matches Examples + +```yaml +'1' + +``` diff --git a/docs/spec/lookups-properties-name.md b/docs/spec/lookups-properties-name.md new file mode 100644 index 0000000000..ca27b5144c --- /dev/null +++ b/docs/spec/lookups-properties-name.md @@ -0,0 +1,22 @@ +# Untitled string in Lookup Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/lookups.json#/properties/name +``` + +The name of the lookup to be used in searches + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | + +## name Type + +`string` + +## name Examples + +```yaml +isWindowsSystemFile_lookup + +``` diff --git a/docs/spec/lookups.md b/docs/spec/lookups.md index b4008bda0f..79b6e284e3 100644 --- a/docs/spec/lookups.md +++ b/docs/spec/lookups.md @@ -1,30 +1,321 @@ -# Lookup Manifest +# Lookup Manifest Schema +```txt +https://api.splunkresearch.com/schemas/lookups.json +``` -*A object that defines a lookup file and its properties.* +A object that defines a lookup file and its properties. +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [lookups.spec.json](../../out/lookups.spec.json "open original schema") | -## Properties +## Lookup Manifest Type +`object` ([Lookup Manifest](lookups.md)) -- **`case_sensitive_match`** *(string)*: What the macro is intended to filter. Must be one of: `['true', 'false']`. +one (and only one) of -- **`collection`** *(string)*: Name of the collection to use for this lookup. +* [Untitled undefined type in Lookup Manifest](lookups-oneof-0.md "check type definition") -- **`default_match`** *(string)*: The default value if no match is found. +* [Untitled undefined type in Lookup Manifest](lookups-oneof-1.md "check type definition") -- **`description`** *(string)*: The description of this lookup. +# Lookup Manifest Properties -- **`fields_list`** *(string)*: A comma and space separated list of field names. +| Property | Type | Required | Nullable | Defined by | +| :-------------------------------------------- | :-------- | :------- | :------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------- | +| [case_sensitive_match](#case_sensitive_match) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-case_sensitive_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/case_sensitive_match") | +| [collection](#collection) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-collection.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/collection") | +| [default_match](#default_match) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-default_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/default_match") | +| [description](#description) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-description.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/description") | +| [fields_list](#fields_list) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-fields_list.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/fields_list") | +| [filename](#filename) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-filename.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filename") | +| [filter](#filter) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-filter.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filter") | +| [match_type](#match_type) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-match_type.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/match_type") | +| [max_matches](#max_matches) | `integer` | Optional | cannot be null | [Lookup Manifest](lookups-properties-max_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/max_matches") | +| [min_matches](#min_matches) | `integer` | Optional | cannot be null | [Lookup Manifest](lookups-properties-min_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/min_matches") | +| [name](#name) | `string` | Optional | cannot be null | [Lookup Manifest](lookups-properties-name.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/name") | -- **`filename`** *(string)*: The name of the file to use for this lookup. +## case_sensitive_match -- **`filter`** *(string)*: Use this attribute to improve search performance when working with significantly large KV. +What the macro is intended to filter -- **`match_type`** *(string)*: A comma and space-delimited list of () specification to allow for non-exact matching. +`case_sensitive_match` -- **`max_matches`** *(integer)*: The maximum number of possible matches for each input lookup value. +* is optional -- **`min_matches`** *(integer)*: Minimum number of possible matches for each input lookup value. +* Type: `string` -- **`name`** *(string)*: The name of the lookup to be used in searches. +* cannot be null + +* defined in: [Lookup Manifest](lookups-properties-case_sensitive_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/case_sensitive_match") + +### case_sensitive_match Type + +`string` + +### case_sensitive_match Constraints + +**enum**: the value of this property must be equal to one of the following values: + +| Value | Explanation | +| :-------- | :---------- | +| `"true"` | | +| `"false"` | | + +### case_sensitive_match Examples + +```yaml +'true' + +``` + +## collection + +Name of the collection to use for this lookup + +`collection` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Lookup Manifest](lookups-properties-collection.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/collection") + +### collection Type + +`string` + +### collection Examples + +```yaml +prohibited_apps_launching_cmd + +``` + +## default_match + +The default value if no match is found + +`default_match` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Lookup Manifest](lookups-properties-default_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/default_match") + +### default_match Type + +`string` + +### default_match Examples + +```yaml +'true' + +``` + +## description + +The description of this lookup + +`description` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Lookup Manifest](lookups-properties-description.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/description") + +### description Type + +`string` + +### description Examples + +```yaml +This lookup contains file names that exist in the Windows\System32 directory + +``` + +## fields_list + +A comma and space separated list of field names + +`fields_list` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Lookup Manifest](lookups-properties-fields_list.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/fields_list") + +### fields_list Type + +`string` + +### fields_list Examples + +```yaml +_key, dest, process_name + +``` + +## filename + +The name of the file to use for this lookup + +`filename` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Lookup Manifest](lookups-properties-filename.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filename") + +### filename Type + +`string` + +### filename Examples + +```yaml +prohibited_apps_launching_cmd.csv + +``` + +## filter + +Use this attribute to improve search performance when working with significantly large KV + +`filter` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Lookup Manifest](lookups-properties-filter.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filter") + +### filter Type + +`string` + +### filter Examples + +```yaml +dest="SPLK_*" + +``` + +## match_type + +A comma and space-delimited list of \(\) specification to allow for non-exact matching + +`match_type` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Lookup Manifest](lookups-properties-match_type.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/match_type") + +### match_type Type + +`string` + +### match_type Examples + +```yaml +WILDCARD(process) + +``` + +## max_matches + +The maximum number of possible matches for each input lookup value + +`max_matches` + +* is optional + +* Type: `integer` + +* cannot be null + +* defined in: [Lookup Manifest](lookups-properties-max_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/max_matches") + +### max_matches Type + +`integer` + +### max_matches Examples + +```yaml +'100' + +``` + +## min_matches + +Minimum number of possible matches for each input lookup value + +`min_matches` + +* is optional + +* Type: `integer` + +* cannot be null + +* defined in: [Lookup Manifest](lookups-properties-min_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/min_matches") + +### min_matches Type + +`integer` + +### min_matches Examples + +```yaml +'1' + +``` + +## name + +The name of the lookup to be used in searches + +`name` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Lookup Manifest](lookups-properties-name.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/name") + +### name Type + +`string` + +### name Examples + +```yaml +isWindowsSystemFile_lookup + +``` diff --git a/docs/spec/macros-properties-arguments-items.md b/docs/spec/macros-properties-arguments-items.md new file mode 100644 index 0000000000..34f73377e8 --- /dev/null +++ b/docs/spec/macros-properties-arguments-items.md @@ -0,0 +1,15 @@ +# Untitled string in Macro Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/macros.json#/properties/arguments/items +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | + +## items Type + +`string` diff --git a/docs/spec/macros-properties-arguments.md b/docs/spec/macros-properties-arguments.md new file mode 100644 index 0000000000..1334acd3c1 --- /dev/null +++ b/docs/spec/macros-properties-arguments.md @@ -0,0 +1,21 @@ +# Untitled array in Macro Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/macros.json#/properties/arguments +``` + +A list of the arguments being passed to this macro + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | + +## arguments Type + +`string[]` + +## arguments Constraints + +**minimum number of items**: the minimum number of items for this array is: `0` + +**unique items**: all items in this array must be unique. Duplicates are not allowed. diff --git a/docs/spec/macros-properties-definition.md b/docs/spec/macros-properties-definition.md new file mode 100644 index 0000000000..128ea17364 --- /dev/null +++ b/docs/spec/macros-properties-definition.md @@ -0,0 +1,22 @@ +# Untitled string in Macro Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/macros.json#/properties/definition +``` + +The macro definition + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | + +## definition Type + +`string` + +## definition Examples + +```yaml +(query=fls-na* AND query = www* AND query=images*) + +``` diff --git a/docs/spec/macros-properties-description.md b/docs/spec/macros-properties-description.md new file mode 100644 index 0000000000..e5f8aed632 --- /dev/null +++ b/docs/spec/macros-properties-description.md @@ -0,0 +1,22 @@ +# Untitled string in Macro Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/macros.json#/properties/description +``` + +What the macro is intended to filter + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | + +## description Type + +`string` + +## description Examples + +```yaml +Use this macro to filter out known good objects + +``` diff --git a/docs/spec/macros-properties-name.md b/docs/spec/macros-properties-name.md new file mode 100644 index 0000000000..484c1e4e3f --- /dev/null +++ b/docs/spec/macros-properties-name.md @@ -0,0 +1,22 @@ +# Untitled string in Macro Manifest Schema + +```txt +https://api.splunkresearch.com/schemas/macros.json#/properties/name +``` + +The name of the macro + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | + +## name Type + +`string` + +## name Examples + +```yaml +detection_search_output_filter + +``` diff --git a/docs/spec/macros.md b/docs/spec/macros.md index 8238f4e9bb..c3cb8dc57b 100644 --- a/docs/spec/macros.md +++ b/docs/spec/macros.md @@ -1,18 +1,123 @@ -# Macro Manifest +# Macro Manifest Schema +```txt +https://api.splunkresearch.com/schemas/macros.json +``` -*An object that defines the parameters for a Splunk Macro* +An object that defines the parameters for a Splunk Macro +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [macros.spec.json](../../out/macros.spec.json "open original schema") | -## Properties +## Macro Manifest Type +`object` ([Macro Manifest](macros.md)) -- **`arguments`** *(array)*: A list of the arguments being passed to this macro. +# Macro Manifest Properties - - **Items** *(string)* +| Property | Type | Required | Nullable | Defined by | +| :-------------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------ | +| [arguments](#arguments) | `array` | Optional | cannot be null | [Macro Manifest](macros-properties-arguments.md "https://api.splunkresearch.com/schemas/macros.json#/properties/arguments") | +| [definition](#definition) | `string` | Optional | cannot be null | [Macro Manifest](macros-properties-definition.md "https://api.splunkresearch.com/schemas/macros.json#/properties/definition") | +| [description](#description) | `string` | Required | cannot be null | [Macro Manifest](macros-properties-description.md "https://api.splunkresearch.com/schemas/macros.json#/properties/description") | +| [name](#name) | `string` | Required | cannot be null | [Macro Manifest](macros-properties-name.md "https://api.splunkresearch.com/schemas/macros.json#/properties/name") | -- **`definition`** *(string)*: The macro definition. +## arguments -- **`description`** *(string)*: What the macro is intended to filter. +A list of the arguments being passed to this macro -- **`name`** *(string)*: The name of the macro. +`arguments` + +* is optional + +* Type: `string[]` + +* cannot be null + +* defined in: [Macro Manifest](macros-properties-arguments.md "https://api.splunkresearch.com/schemas/macros.json#/properties/arguments") + +### arguments Type + +`string[]` + +### arguments Constraints + +**minimum number of items**: the minimum number of items for this array is: `0` + +**unique items**: all items in this array must be unique. Duplicates are not allowed. + +## definition + +The macro definition + +`definition` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Macro Manifest](macros-properties-definition.md "https://api.splunkresearch.com/schemas/macros.json#/properties/definition") + +### definition Type + +`string` + +### definition Examples + +```yaml +(query=fls-na* AND query = www* AND query=images*) + +``` + +## description + +What the macro is intended to filter + +`description` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Macro Manifest](macros-properties-description.md "https://api.splunkresearch.com/schemas/macros.json#/properties/description") + +### description Type + +`string` + +### description Examples + +```yaml +Use this macro to filter out known good objects + +``` + +## name + +The name of the macro + +`name` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Macro Manifest](macros-properties-name.md "https://api.splunkresearch.com/schemas/macros.json#/properties/name") + +### name Type + +`string` + +### name Examples + +```yaml +detection_search_output_filter + +``` diff --git a/docs/spec/response_tasks-default.md b/docs/spec/response_tasks-default.md new file mode 100644 index 0000000000..31296217d9 --- /dev/null +++ b/docs/spec/response_tasks-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Response Schema Schema + +```txt +https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response_tasks.spec.json#/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/response_tasks-properties-automation-default.md b/docs/spec/response_tasks-properties-automation-default.md new file mode 100644 index 0000000000..b2cdd45428 --- /dev/null +++ b/docs/spec/response_tasks-properties-automation-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Response Schema Schema + +```txt +#/properties/automation#/properties/automation/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/response_tasks-properties-automation.md b/docs/spec/response_tasks-properties-automation.md new file mode 100644 index 0000000000..bdd1eb66d4 --- /dev/null +++ b/docs/spec/response_tasks-properties-automation.md @@ -0,0 +1,62 @@ +# Untitled object in Response Schema Schema + +```txt +#/properties/automation#/properties/automation +``` + +An array of key value pairs for defining actions and playbooks + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | + +## automation Type + +`object` ([Details](response_tasks-properties-automation.md)) + +## automation Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +**unique items**: all items in this array must be unique. Duplicates are not allowed. + +## automation Default Value + +The default value is: + +```json +{ + "is_note_required": false, + "sla_type": "minutes", + "sla": "", + "role": "", + "action": [], + "playbooks": [] +} +``` + +## automation Examples + +```yaml +is_note_required: false +sla_type: minutes +sla: 30 +action: + - run_query +playbooks: + - scm: local + playbook: automate something + - scm: local + playbook: automate something else + +``` + +# automation Properties + +| Property | Type | Required | Nullable | Defined by | +| :-------------------- | :--- | :------- | :---------- | :--------- | +| Additional Properties | Any | Optional | can be null | | + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/response_tasks-properties-sla.md b/docs/spec/response_tasks-properties-sla.md new file mode 100644 index 0000000000..9de4e3e98f --- /dev/null +++ b/docs/spec/response_tasks-properties-sla.md @@ -0,0 +1,27 @@ +# Untitled integer in Response Schema Schema + +```txt +#/properties/sla#/properties/sla +``` + +Measured integer for Service Level Agreement for completion of the phase + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | + +## sla Type + +`integer` + +## sla Examples + +```yaml +5 + +``` + +```yaml +30 + +``` diff --git a/docs/spec/response_tasks-properties-sla_type.md b/docs/spec/response_tasks-properties-sla_type.md new file mode 100644 index 0000000000..28319602eb --- /dev/null +++ b/docs/spec/response_tasks-properties-sla_type.md @@ -0,0 +1,40 @@ +# Untitled string in Response Schema Schema + +```txt +#/properties/sla_type#/properties/sla_type +``` + +Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc) + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | + +## sla_type Type + +`string` + +## sla_type Default Value + +The default value is: + +```json +"minutes" +``` + +## sla_type Examples + +```yaml +minutes + +``` + +```yaml +hours + +``` + +```yaml +days + +``` diff --git a/docs/spec/response_tasks.md b/docs/spec/response_tasks.md index fc51ab06a6..6ee8b796ed 100644 --- a/docs/spec/response_tasks.md +++ b/docs/spec/response_tasks.md @@ -1,32 +1,395 @@ -# Response Schema +# Response Schema Schema +```txt +https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response_tasks.spec.json +``` -*schema for response task* +schema for response task +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------ | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [response_tasks.spec.json](../../out/response_tasks.spec.json "open original schema") | -## Properties +## Response Schema Type +`object` ([Response Schema](response_tasks.md)) -- **`author`** *(string)*: Author of the response task. Default: ``. +## Response Schema Default Value -- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. +The default value is: -- **`description`** *(string)*: Description of response task. Default: ``. +```json +{} +``` -- **`id`** *(string)*: UUID as unique identifier. Default: ``. +# Response Schema Properties -- **`name`** *(string)*: Name of response task. Default: ``. +| Property | Type | Required | Nullable | Defined by | +| :-------------------------- | :-------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------- | +| [author](#author) | `string` | Required | cannot be null | [Response Schema](response_tasks-properties-author.md "#/properties/author#/properties/author") | +| [date](#date) | `string` | Required | cannot be null | [Response Schema](response_tasks-properties-date.md "#/properties/date#/properties/date") | +| [description](#description) | `string` | Required | cannot be null | [Response Schema](response_tasks-properties-description.md "#/properties/description#/properties/description") | +| [id](#id) | `string` | Required | cannot be null | [Response Schema](response_tasks-properties-id.md "#/properties/id#/properties/id") | +| [name](#name) | `string` | Required | cannot be null | [Response Schema](response_tasks-properties-name.md "#/properties/name#/properties/name") | +| [sla](#sla) | `integer` | Optional | cannot be null | [Response Schema](response_tasks-properties-sla.md "#/properties/sla#/properties/sla") | +| [sla_type](#sla_type) | `string` | Optional | cannot be null | [Response Schema](response_tasks-properties-sla_type.md "#/properties/sla_type#/properties/sla_type") | +| [automation](#automation) | `object` | Optional | cannot be null | [Response Schema](response_tasks-properties-automation.md "#/properties/automation#/properties/automation") | +| [tags](#tags) | `object` | Required | cannot be null | [Response Schema](response_tasks-properties-tags.md "#/properties/tags#/properties/tags") | +| [version](#version) | `integer` | Required | cannot be null | [Response Schema](response_tasks-properties-version.md "#/properties/version#/properties/version") | +| [references](#references) | `array` | Optional | cannot be null | [Response Schema](response_tasks-properties-references.md "#/properties/references#/properties/references") | +| Additional Properties | Any | Optional | can be null | | -- **`sla`** *(integer)*: Measured integer for Service Level Agreement for completion of the phase. Default: `0`. +## author -- **`sla_type`** *(string)*: Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc). Default: `minutes`. +Author of the response task -- **`automation`** *(object)*: An array of key value pairs for defining actions and playbooks. Can contain additional properties. Default: `{'is_note_required': False, 'sla_type': 'minutes', 'sla': '', 'role': '', 'action': [], 'playbooks': []}`. +`author` -- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. +* is required -- **`version`** *(integer)*: version of detection, e.g. 1 or 2 ... Default: `0`. +* Type: `string` -- **`references`** *(array)*: A list of references for this response, phase or task (e.g. web or printed citation). Default: `[]`. +* cannot be null - - **Items** *(string)*: An explanation about the purpose of this instance. Default: ``. +* defined in: [Response Schema](response_tasks-properties-author.md "#/properties/author#/properties/author") + +### author Type + +`string` + +### author Examples + +```yaml +ButterCup, Splunk + +``` + +## date + +date of creation or modification, format yyyy-mm-dd + +`date` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](response_tasks-properties-date.md "#/properties/date#/properties/date") + +### date Type + +`string` + +### date Examples + +```yaml +'2019-12-06' + +``` + +## description + +Description of response task + +`description` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](response_tasks-properties-description.md "#/properties/description#/properties/description") + +### description Type + +`string` + +### description Examples + +```yaml +Response example. + +``` + +## id + +UUID as unique identifier + +`id` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](response_tasks-properties-id.md "#/properties/id#/properties/id") + +### id Type + +`string` + +### id Examples + +```yaml +fb4c31b0-13e8-4155-8aa5-24de4b8d6717 + +``` + +## name + +Name of response task + +`name` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](response_tasks-properties-name.md "#/properties/name#/properties/name") + +### name Type + +`string` + +### name Examples + +```yaml +Response Example + +``` + +## sla + +Measured integer for Service Level Agreement for completion of the phase + +`sla` + +* is optional + +* Type: `integer` + +* cannot be null + +* defined in: [Response Schema](response_tasks-properties-sla.md "#/properties/sla#/properties/sla") + +### sla Type + +`integer` + +### sla Examples + +```yaml +5 + +``` + +```yaml +30 + +``` + +## sla_type + +Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc) + +`sla_type` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](response_tasks-properties-sla_type.md "#/properties/sla_type#/properties/sla_type") + +### sla_type Type + +`string` + +### sla_type Default Value + +The default value is: + +```json +"minutes" +``` + +### sla_type Examples + +```yaml +minutes + +``` + +```yaml +hours + +``` + +```yaml +days + +``` + +## automation + +An array of key value pairs for defining actions and playbooks + +`automation` + +* is optional + +* Type: `object` ([Details](response_tasks-properties-automation.md)) + +* cannot be null + +* defined in: [Response Schema](response_tasks-properties-automation.md "#/properties/automation#/properties/automation") + +### automation Type + +`object` ([Details](response_tasks-properties-automation.md)) + +### automation Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +**unique items**: all items in this array must be unique. Duplicates are not allowed. + +### automation Default Value + +The default value is: + +```json +{ + "is_note_required": false, + "sla_type": "minutes", + "sla": "", + "role": "", + "action": [], + "playbooks": [] +} +``` + +### automation Examples + +```yaml +is_note_required: false +sla_type: minutes +sla: 30 +action: + - run_query +playbooks: + - scm: local + playbook: automate something + - scm: local + playbook: automate something else + +``` + +## tags + +An array of key value pairs for tagging + +`tags` + +* is required + +* Type: `object` ([Details](response_tasks-properties-tags.md)) + +* cannot be null + +* defined in: [Response Schema](response_tasks-properties-tags.md "#/properties/tags#/properties/tags") + +### tags Type + +`object` ([Details](response_tasks-properties-tags.md)) + +### tags Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +**unique items**: all items in this array must be unique. Duplicates are not allowed. + +### tags Default Value + +The default value is: + +```json +{} +``` + +### tags Examples + +```yaml +analytic_story: credential_dumping + +``` + +## version + +version of detection, e.g. 1 or 2 ... + +`version` + +* is required + +* Type: `integer` + +* cannot be null + +* defined in: [Response Schema](response_tasks-properties-version.md "#/properties/version#/properties/version") + +### version Type + +`integer` + +### version Examples + +```yaml +1 + +``` + +## references + +A list of references for this response, phase or task (e.g. web or printed citation) + +`references` + +* is optional + +* Type: `string[]` ([Blue Team Handbook by Don Murdoch - Amazon](response_tasks-properties-references-blue-team-handbook-by-don-murdoch---amazon.md)) + +* cannot be null + +* defined in: [Response Schema](response_tasks-properties-references.md "#/properties/references#/properties/references") + +### references Type + +`string[]` ([Blue Team Handbook by Don Murdoch - Amazon](response_tasks-properties-references-blue-team-handbook-by-don-murdoch---amazon.md)) + +### references Default Value + +The default value is: + +```json +[] +``` + +### references Examples + +```yaml +- Blue Team Handbook by Don Murdoch - Alarm Triage Overview pages 146-148 +- https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/responses-default.md b/docs/spec/responses-default.md new file mode 100644 index 0000000000..3478b338bd --- /dev/null +++ b/docs/spec/responses-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Response Schema Schema + +```txt +https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response.spec.json#/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/responses-properties-is_note_required.md b/docs/spec/responses-properties-is_note_required.md new file mode 100644 index 0000000000..26aed247e2 --- /dev/null +++ b/docs/spec/responses-properties-is_note_required.md @@ -0,0 +1,27 @@ +# Untitled boolean in Response Schema Schema + +```txt +#/properties/is_note_required#/properties/is_note_required +``` + +Global assignment for notes being required for tasks, can be individually set in the task + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | + +## is_note_required Type + +`boolean` + +## is_note_required Examples + +```yaml +true + +``` + +```yaml +false + +``` diff --git a/docs/spec/responses-properties-response_phase-default.md b/docs/spec/responses-properties-response_phase-default.md new file mode 100644 index 0000000000..e773e97379 --- /dev/null +++ b/docs/spec/responses-properties-response_phase-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Response Schema Schema + +```txt +#/properties/response_phases#/properties/response_phase/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/responses-properties-response_phase.md b/docs/spec/responses-properties-response_phase.md new file mode 100644 index 0000000000..bdd06286c0 --- /dev/null +++ b/docs/spec/responses-properties-response_phase.md @@ -0,0 +1,51 @@ +# Untitled array in Response Schema Schema + +```txt +#/properties/response_phases#/properties/response_phase +``` + +Response divided into phases. These will used to referenced known response_phase parameters + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | + +## response_phase Type + +`array` + +## response_phase Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +## response_phase Default Value + +The default value is: + +```json +{} +``` + +## response_phase Examples + +```yaml +preparation: + - id: 7c72d944-3995-4485-8e57-67b4c353989b + name: Preparation NIST +identification: + - id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892 + name: Detection and Analysis + - id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0 + name: Analysis + +``` + +# response_phase Properties + +| Property | Type | Required | Nullable | Defined by | +| :-------------------- | :--- | :------- | :---------- | :--------- | +| Additional Properties | Any | Optional | can be null | | + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/responses.md b/docs/spec/responses.md index 4e21a5f6fc..314ad93450 100644 --- a/docs/spec/responses.md +++ b/docs/spec/responses.md @@ -1,30 +1,340 @@ -# Response Schema +# Response Schema Schema +```txt +https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response.spec.json +``` -*schema for response* +schema for response +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [responses.spec.json](../../out/responses.spec.json "open original schema") | -## Properties +## Response Schema Type +`object` ([Response Schema](responses.md)) -- **`author`** *(string)*: Author of the response. Default: ``. +## Response Schema Default Value -- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. +The default value is: -- **`description`** *(string)*: Description of response. Default: ``. +```json +{} +``` -- **`id`** *(string)*: UUID as unique identifier. Default: ``. +# Response Schema Properties -- **`name`** *(string)*: Name of response. Default: ``. +| Property | Type | Required | Nullable | Defined by | +| :------------------------------------ | :-------- | :------- | :------------- | :----------------------------------------------------------------------------------------------------------------------- | +| [author](#author) | `string` | Required | cannot be null | [Response Schema](responses-properties-author.md "#/properties/author#/properties/author") | +| [date](#date) | `string` | Required | cannot be null | [Response Schema](responses-properties-date.md "#/properties/date#/properties/date") | +| [description](#description) | `string` | Required | cannot be null | [Response Schema](responses-properties-description.md "#/properties/description#/properties/description") | +| [id](#id) | `string` | Required | cannot be null | [Response Schema](responses-properties-id.md "#/properties/id#/properties/id") | +| [name](#name) | `string` | Required | cannot be null | [Response Schema](responses-properties-name.md "#/properties/name#/properties/name") | +| [response_phase](#response_phase) | `array` | Required | cannot be null | [Response Schema](responses-properties-response_phase.md "#/properties/response_phases#/properties/response_phase") | +| [tags](#tags) | `object` | Required | cannot be null | [Response Schema](responses-properties-tags.md "#/properties/tags#/properties/tags") | +| [version](#version) | `integer` | Required | cannot be null | [Response Schema](responses-properties-version.md "#/properties/version#/properties/version") | +| [is_note_required](#is_note_required) | `boolean` | Optional | cannot be null | [Response Schema](responses-properties-is_note_required.md "#/properties/is_note_required#/properties/is_note_required") | +| [references](#references) | `array` | Optional | cannot be null | [Response Schema](responses-properties-references.md "#/properties/references#/properties/references") | +| Additional Properties | Any | Optional | can be null | | -- **`response_phase`** *(array)*: Response divided into phases. These will used to referenced known response_phase parameters. Can contain additional properties. Default: `{}`. +## author -- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. +Author of the response -- **`version`** *(integer)*: version of detection, e.g. 1 or 2 ... Default: `0`. +`author` -- **`is_note_required`** *(boolean)*: Global assignment for notes being required for tasks, can be individually set in the task. Default: `False`. +* is required -- **`references`** *(array)*: A list of references for this response, phase or task (e.g. web or printed citation). Default: `[]`. +* Type: `string` - - **Items** *(string)*: An explanation about the purpose of this instance. Default: ``. +* cannot be null + +* defined in: [Response Schema](responses-properties-author.md "#/properties/author#/properties/author") + +### author Type + +`string` + +### author Examples + +```yaml +Rico Valdez, Patrick Bareiß, Splunk + +``` + +## date + +date of creation or modification, format yyyy-mm-dd + +`date` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](responses-properties-date.md "#/properties/date#/properties/date") + +### date Type + +`string` + +### date Examples + +```yaml +'2019-12-06' + +``` + +## description + +Description of response + +`description` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](responses-properties-description.md "#/properties/description#/properties/description") + +### description Type + +`string` + +### description Examples + +```yaml +Response example. + +``` + +## id + +UUID as unique identifier + +`id` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](responses-properties-id.md "#/properties/id#/properties/id") + +### id Type + +`string` + +### id Examples + +```yaml +fb4c31b0-13e8-4155-8aa5-24de4b8d6717 + +``` + +## name + +Name of response + +`name` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](responses-properties-name.md "#/properties/name#/properties/name") + +### name Type + +`string` + +### name Examples + +```yaml +Response Example + +``` + +## response_phase + +Response divided into phases. These will used to referenced known response_phase parameters + +`response_phase` + +* is required + +* Type: `array` + +* cannot be null + +* defined in: [Response Schema](responses-properties-response_phase.md "#/properties/response_phases#/properties/response_phase") + +### response_phase Type + +`array` + +### response_phase Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +### response_phase Default Value + +The default value is: + +```json +{} +``` + +### response_phase Examples + +```yaml +preparation: + - id: 7c72d944-3995-4485-8e57-67b4c353989b + name: Preparation NIST +identification: + - id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892 + name: Detection and Analysis + - id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0 + name: Analysis + +``` + +## tags + +An array of key value pairs for tagging + +`tags` + +* is required + +* Type: `object` ([Details](responses-properties-tags.md)) + +* cannot be null + +* defined in: [Response Schema](responses-properties-tags.md "#/properties/tags#/properties/tags") + +### tags Type + +`object` ([Details](responses-properties-tags.md)) + +### tags Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +**unique items**: all items in this array must be unique. Duplicates are not allowed. + +### tags Default Value + +The default value is: + +```json +{} +``` + +### tags Examples + +```yaml +analytic_story: credential_dumping + +``` + +## version + +version of detection, e.g. 1 or 2 ... + +`version` + +* is required + +* Type: `integer` + +* cannot be null + +* defined in: [Response Schema](responses-properties-version.md "#/properties/version#/properties/version") + +### version Type + +`integer` + +### version Examples + +```yaml +1 + +``` + +## is_note_required + +Global assignment for notes being required for tasks, can be individually set in the task + +`is_note_required` + +* is optional + +* Type: `boolean` + +* cannot be null + +* defined in: [Response Schema](responses-properties-is_note_required.md "#/properties/is_note_required#/properties/is_note_required") + +### is_note_required Type + +`boolean` + +### is_note_required Examples + +```yaml +true + +``` + +```yaml +false + +``` + +## references + +A list of references for this response, phase or task (e.g. web or printed citation) + +`references` + +* is optional + +* Type: `string[]` ([Blue Team Handbook by Don Murdoch - Amazon](responses-properties-references-blue-team-handbook-by-don-murdoch---amazon.md)) + +* cannot be null + +* defined in: [Response Schema](responses-properties-references.md "#/properties/references#/properties/references") + +### references Type + +`string[]` ([Blue Team Handbook by Don Murdoch - Amazon](responses-properties-references-blue-team-handbook-by-don-murdoch---amazon.md)) + +### references Default Value + +The default value is: + +```json +[] +``` + +### references Examples + +```yaml +- Blue Team Handbook by Don Murdoch - Alarm Triage Overview pages 146-148 +- https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/responses_phase-default.md b/docs/spec/responses_phase-default.md new file mode 100644 index 0000000000..c1576f1e4e --- /dev/null +++ b/docs/spec/responses_phase-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Response Schema Schema + +```txt +http://example.com/example.json#/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/responses_phase-properties-response_task-default.md b/docs/spec/responses_phase-properties-response_task-default.md new file mode 100644 index 0000000000..9ede2dfaf9 --- /dev/null +++ b/docs/spec/responses_phase-properties-response_task-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Response Schema Schema + +```txt +#/properties/response_task#/properties/response_task/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/responses_phase-properties-response_task.md b/docs/spec/responses_phase-properties-response_task.md new file mode 100644 index 0000000000..780e4b3d5b --- /dev/null +++ b/docs/spec/responses_phase-properties-response_task.md @@ -0,0 +1,57 @@ +# Untitled array in Response Schema Schema + +```txt +#/properties/response_task#/properties/response_task +``` + +Response phase is divided into task(s) to be completed. These will used to referenced known response_task parameters. Order is as positioned and with unique name. + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") | + +## response_task Type + +`array` + +## response_task Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +## response_task Default Value + +The default value is: + +```json +{} +``` + +## response_task Examples + +```yaml +id: 7c72d944-3995-4485-8e57-67b4c353989b +name: Prepare for Incident Handling + +``` + +```yaml +id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892 +name: Preventing Incidents + +``` + +```yaml +id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0 +name: Practice + +``` + +# response_task Properties + +| Property | Type | Required | Nullable | Defined by | +| :-------------------- | :--- | :------- | :---------- | :--------- | +| Additional Properties | Any | Optional | can be null | | + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/responses_phase.md b/docs/spec/responses_phase.md index c17e3e5e51..09d1b3be2a 100644 --- a/docs/spec/responses_phase.md +++ b/docs/spec/responses_phase.md @@ -1,32 +1,389 @@ -# Response Schema +# Response Schema Schema +```txt +http://example.com/example.json +``` -*schema for phase* +schema for phase +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [responses_phase.spec.json](../../out/responses_phase.spec.json "open original schema") | -## Properties +## Response Schema Type +`object` ([Response Schema](responses_phase.md)) -- **`author`** *(string)*: Author of the phase. Default: ``. +## Response Schema Default Value -- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. +The default value is: -- **`description`** *(string)*: Description of phase. Default: ``. +```json +{} +``` -- **`id`** *(string)*: UUID as unique identifier. Default: ``. +# Response Schema Properties -- **`name`** *(string)*: Name of phase. Default: ``. +| Property | Type | Required | Nullable | Defined by | +| :------------------------------ | :-------- | :------- | :------------- | :-------------------------------------------------------------------------------------------------------------------- | +| [author](#author) | `string` | Required | cannot be null | [Response Schema](responses_phase-properties-author.md "#/properties/author#/properties/author") | +| [date](#date) | `string` | Required | cannot be null | [Response Schema](responses_phase-properties-date.md "#/properties/date#/properties/date") | +| [description](#description) | `string` | Required | cannot be null | [Response Schema](responses_phase-properties-description.md "#/properties/description#/properties/description") | +| [id](#id) | `string` | Required | cannot be null | [Response Schema](responses_phase-properties-id.md "#/properties/id#/properties/id") | +| [name](#name) | `string` | Required | cannot be null | [Response Schema](responses_phase-properties-name.md "#/properties/name#/properties/name") | +| [response_task](#response_task) | `array` | Required | cannot be null | [Response Schema](responses_phase-properties-response_task.md "#/properties/response_task#/properties/response_task") | +| [tags](#tags) | `object` | Required | cannot be null | [Response Schema](responses_phase-properties-tags.md "#/properties/tags#/properties/tags") | +| [version](#version) | `integer` | Required | cannot be null | [Response Schema](responses_phase-properties-version.md "#/properties/version#/properties/version") | +| [sla](#sla) | `integer` | Optional | cannot be null | [Response Schema](responses_phase-properties-sla.md "#/properties/sla#/properties/sla") | +| [sla_type](#sla_type) | `string` | Optional | cannot be null | [Response Schema](responses_phase-properties-sla_type.md "#/properties/sla_type#/properties/sla_type") | +| [references](#references) | `array` | Optional | cannot be null | [Response Schema](responses_phase-properties-references.md "#/properties/references#/properties/references") | +| Additional Properties | Any | Optional | can be null | | -- **`response_task`** *(array)*: Response phase is divided into task(s) to be completed. These will used to referenced known response_task parameters. Order is as positioned and with unique name. Can contain additional properties. Default: `{}`. +## author -- **`tags`** *(object)*: An array of key value pairs for tagging. Can contain additional properties. Default: `{}`. +Author of the phase -- **`version`** *(integer)*: version of detection, e.g. 1 or 2 ... Default: `0`. +`author` -- **`sla`** *(integer)*: Measured integer for Service Level Agreement for completion of the phase. Default: `None`. +* is required -- **`sla_type`** *(string)*: Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc). Default: `minutes`. +* Type: `string` -- **`references`** *(array)*: A list of references for this response, phase or task (e.g. web or printed citation). Default: `[]`. +* cannot be null - - **Items** *(string)*: An explanation about the purpose of this instance. Default: ``. +* defined in: [Response Schema](responses_phase-properties-author.md "#/properties/author#/properties/author") + +### author Type + +`string` + +### author Examples + +```yaml +Rico Valdez, Patrick Bareiß, Splunk + +``` + +## date + +date of creation or modification, format yyyy-mm-dd + +`date` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](responses_phase-properties-date.md "#/properties/date#/properties/date") + +### date Type + +`string` + +### date Examples + +```yaml +'2019-12-06' + +``` + +## description + +Description of phase + +`description` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](responses_phase-properties-description.md "#/properties/description#/properties/description") + +### description Type + +`string` + +### description Examples + +```yaml +Response phase descripion. + +``` + +## id + +UUID as unique identifier + +`id` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](responses_phase-properties-id.md "#/properties/id#/properties/id") + +### id Type + +`string` + +### id Examples + +```yaml +fb4c31b0-13e8-4155-8aa5-24de4b8d6717 + +``` + +## name + +Name of phase + +`name` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](responses_phase-properties-name.md "#/properties/name#/properties/name") + +### name Type + +`string` + +### name Examples + +```yaml +Preparation + +``` + +## response_task + +Response phase is divided into task(s) to be completed. These will used to referenced known response_task parameters. Order is as positioned and with unique name. + +`response_task` + +* is required + +* Type: `array` + +* cannot be null + +* defined in: [Response Schema](responses_phase-properties-response_task.md "#/properties/response_task#/properties/response_task") + +### response_task Type + +`array` + +### response_task Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +### response_task Default Value + +The default value is: + +```json +{} +``` + +### response_task Examples + +```yaml +id: 7c72d944-3995-4485-8e57-67b4c353989b +name: Prepare for Incident Handling + +``` + +```yaml +id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892 +name: Preventing Incidents + +``` + +```yaml +id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0 +name: Practice + +``` + +## tags + +An array of key value pairs for tagging + +`tags` + +* is required + +* Type: `object` ([Details](responses_phase-properties-tags.md)) + +* cannot be null + +* defined in: [Response Schema](responses_phase-properties-tags.md "#/properties/tags#/properties/tags") + +### tags Type + +`object` ([Details](responses_phase-properties-tags.md)) + +### tags Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +**unique items**: all items in this array must be unique. Duplicates are not allowed. + +### tags Default Value + +The default value is: + +```json +{} +``` + +### tags Examples + +```yaml +analytic_story: credential_dumping + +``` + +## version + +version of detection, e.g. 1 or 2 ... + +`version` + +* is required + +* Type: `integer` + +* cannot be null + +* defined in: [Response Schema](responses_phase-properties-version.md "#/properties/version#/properties/version") + +### version Type + +`integer` + +### version Examples + +```yaml +1 + +``` + +## sla + +Measured integer for Service Level Agreement for completion of the phase + +`sla` + +* is optional + +* Type: `integer` + +* cannot be null + +* defined in: [Response Schema](responses_phase-properties-sla.md "#/properties/sla#/properties/sla") + +### sla Type + +`integer` + +### sla Examples + +```yaml +5 + +``` + +```yaml +30 + +``` + +## sla_type + +Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc) + +`sla_type` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Response Schema](responses_phase-properties-sla_type.md "#/properties/sla_type#/properties/sla_type") + +### sla_type Type + +`string` + +### sla_type Default Value + +The default value is: + +```json +"minutes" +``` + +### sla_type Examples + +```yaml +minutes + +``` + +```yaml +hours + +``` + +```yaml +days + +``` + +## references + +A list of references for this response, phase or task (e.g. web or printed citation) + +`references` + +* is optional + +* Type: `string[]` ([3.1 Preparation](responses_phase-properties-references-31-preparation.md)) + +* cannot be null + +* defined in: [Response Schema](responses_phase-properties-references.md "#/properties/references#/properties/references") + +### references Type + +`string[]` ([3.1 Preparation](responses_phase-properties-references-31-preparation.md)) + +### references Default Value + +The default value is: + +```json +[] +``` + +### references Examples + +```yaml +https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/stories-default.md b/docs/spec/stories-default.md new file mode 100644 index 0000000000..9abc854a40 --- /dev/null +++ b/docs/spec/stories-default.md @@ -0,0 +1,15 @@ +# Untitled undefined type in Analytics Story Schema Schema + +```txt +http://example.com/example.json#/default +``` + + + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [stories.spec.json*](../../out/stories.spec.json "open original schema") | + +## default Type + +unknown diff --git a/docs/spec/stories-properties-narrative.md b/docs/spec/stories-properties-narrative.md new file mode 100644 index 0000000000..cc4532dd7b --- /dev/null +++ b/docs/spec/stories-properties-narrative.md @@ -0,0 +1,26 @@ +# Untitled string in Analytics Story Schema Schema + +```txt +#/properties/narrative#/properties/narrative +``` + +narrative of the analytics story + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [stories.spec.json*](../../out/stories.spec.json "open original schema") | + +## narrative Type + +`string` + +## narrative Examples + +```yaml +>- + gathering credentials from a target system, often hashed or encrypted, is a + common attack technique. Even though the credentials may not be in plain text, + an attacker can still exfiltrate the data and set to cracking it offline, on + their own systems. + +``` diff --git a/docs/spec/stories.md b/docs/spec/stories.md index 27f32da5f3..50b565a2fe 100644 --- a/docs/spec/stories.md +++ b/docs/spec/stories.md @@ -1,26 +1,287 @@ -# Analytics Story Schema +# Analytics Story Schema Schema +```txt +http://example.com/example.json +``` -*schema analytics story* +schema analytics story +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [stories.spec.json](../../out/stories.spec.json "open original schema") | -## Properties +## Analytics Story Schema Type +`object` ([Analytics Story Schema](stories.md)) -- **`author`** *(string)*: Author of the analytics story. Default: ``. +## Analytics Story Schema Default Value -- **`date`** *(string)*: date of creation or modification, format yyyy-mm-dd. Default: ``. +The default value is: -- **`description`** *(string)*: description of the analytics story. Default: ``. +```json +{} +``` -- **`id`** *(string)*: UUID as unique identifier. Default: ``. +# Analytics Story Schema Properties -- **`name`** *(string)*: Name of the Analytics Story. Default: ``. +| Property | Type | Required | Nullable | Defined by | +| :-------------------------- | :-------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------- | +| [author](#author) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-author.md "#/properties/author#/properties/author") | +| [date](#date) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-date.md "#/properties/date#/properties/date") | +| [description](#description) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-description.md "#/properties/description#/properties/description") | +| [id](#id) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-id.md "#/properties/id#/properties/id") | +| [name](#name) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-name.md "#/properties/name#/properties/name") | +| [narrative](#narrative) | `string` | Required | cannot be null | [Analytics Story Schema](stories-properties-narrative.md "#/properties/narrative#/properties/narrative") | +| [search](#search) | `string` | Optional | cannot be null | [Analytics Story Schema](stories-properties-search.md "#/properties/search#/properties/search") | +| [tags](#tags) | `object` | Required | cannot be null | [Analytics Story Schema](stories-properties-tags.md "#/properties/tags#/properties/tags") | +| [version](#version) | `integer` | Required | cannot be null | [Analytics Story Schema](stories-properties-version.md "#/properties/version#/properties/version") | +| Additional Properties | Any | Optional | can be null | | -- **`narrative`** *(string)*: narrative of the analytics story. Default: ``. +## author -- **`search`** *(string)*: An additional Splunk search, which uses the result of the detections. Default: ``. +Author of the analytics story -- **`tags`** *(object)*: An explanation about the purpose of this instance. Can contain additional properties. Default: `{}`. +`author` -- **`version`** *(integer)*: version of analytics story, e.g. 1 or 2 ... Default: `0`. +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Analytics Story Schema](stories-properties-author.md "#/properties/author#/properties/author") + +### author Type + +`string` + +### author Examples + +```yaml +Rico Valdez, Patrick Bareiß, Splunk + +``` + +## date + +date of creation or modification, format yyyy-mm-dd + +`date` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Analytics Story Schema](stories-properties-date.md "#/properties/date#/properties/date") + +### date Type + +`string` + +### date Examples + +```yaml +'2019-12-06' + +``` + +## description + +description of the analytics story + +`description` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Analytics Story Schema](stories-properties-description.md "#/properties/description#/properties/description") + +### description Type + +`string` + +### description Examples + +```yaml +>- + Uncover activity consistent with credential dumping, a technique where + attackers compromise systems and attempt to obtain and exfiltrate passwords. + +``` + +## id + +UUID as unique identifier + +`id` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Analytics Story Schema](stories-properties-id.md "#/properties/id#/properties/id") + +### id Type + +`string` + +### id Examples + +```yaml +fb4c31b0-13e8-4155-8aa5-24de4b8d6717 + +``` + +## name + +Name of the Analytics Story + +`name` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Analytics Story Schema](stories-properties-name.md "#/properties/name#/properties/name") + +### name Type + +`string` + +### name Examples + +```yaml +Credential Dumping + +``` + +## narrative + +narrative of the analytics story + +`narrative` + +* is required + +* Type: `string` + +* cannot be null + +* defined in: [Analytics Story Schema](stories-properties-narrative.md "#/properties/narrative#/properties/narrative") + +### narrative Type + +`string` + +### narrative Examples + +```yaml +>- + gathering credentials from a target system, often hashed or encrypted, is a + common attack technique. Even though the credentials may not be in plain text, + an attacker can still exfiltrate the data and set to cracking it offline, on + their own systems. + +``` + +## search + +An additional Splunk search, which uses the result of the detections + +`search` + +* is optional + +* Type: `string` + +* cannot be null + +* defined in: [Analytics Story Schema](stories-properties-search.md "#/properties/search#/properties/search") + +### search Type + +`string` + +### search Examples + +```yaml +>- + index=asx mitre_id=t1003 | stats values(source) as detections values(process) + as processes values(user) as users values(_time) as time count by dest + +``` + +## tags + +An explanation about the purpose of this instance. + +`tags` + +* is required + +* Type: `object` ([Details](stories-properties-tags.md)) + +* cannot be null + +* defined in: [Analytics Story Schema](stories-properties-tags.md "#/properties/tags#/properties/tags") + +### tags Type + +`object` ([Details](stories-properties-tags.md)) + +### tags Constraints + +**minimum number of items**: the minimum number of items for this array is: `1` + +### tags Default Value + +The default value is: + +```json +{} +``` + +### tags Examples + +```yaml +analytic_story: credential_dumping + +``` + +## version + +version of analytics story, e.g. 1 or 2 ... + +`version` + +* is required + +* Type: `integer` + +* cannot be null + +* defined in: [Analytics Story Schema](stories-properties-version.md "#/properties/version#/properties/version") + +### version Type + +`integer` + +### version Examples + +```yaml +1 + +``` + +## Additional Properties + +Additional properties are allowed and do not have to follow a specific schema From 978e67817f4f299360c01183acb18d03033e311e Mon Sep 17 00:00:00 2001 From: divious1 Date: Thu, 18 Mar 2021 22:39:08 -0400 Subject: [PATCH 22/62] updating to remove type and also updated circleci --- .circleci/config.yml | 4 +- bin/doc_gen.py | 2 + docs/README.md | 21 +++---- docs/detections.md | 120 +++++++++++++++++++-------------------- docs/detections.wiki | 130 +++++++++++++++++++++---------------------- docs/stories.md | 2 +- docs/stories.wiki | 10 ++-- 7 files changed, 143 insertions(+), 146 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 711c24dc80..9fc864945f 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -82,11 +82,11 @@ jobs: source venv/bin/activate python bin/validate.py --path . --verbose - run: - name: run doc-gen + name: generate documentation command: | cd security-content source venv/bin/activate - python bin/doc-gen.py --path . --output docs -v + python bin/doc_gen.py --path . --output docs -v build-sources: executor: content-executor diff --git a/bin/doc_gen.py b/bin/doc_gen.py index ddad81d99f..8c951e9015 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -5,6 +5,7 @@ import sys import re from os import path, walk import json +import jsonschema2md from jinja2 import Environment, FileSystemLoader from attackcti import attack_client from pyattck import Attck @@ -246,6 +247,7 @@ if __name__ == "__main__": parser.add_argument("-p", "--path", required=True, help="path to security_content repo") parser.add_argument("-o", "--output", required=True, help="path to the output directory for the docs") parser.add_argument("-v", "--verbose", required=False, default=False, action='store_true', help="prints verbose output") + # parse them args = parser.parse_args() REPO_PATH = args.path diff --git a/docs/README.md b/docs/README.md index 7dbecc3e89..d9ebb8dba3 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,12 +1,14 @@ -# Splunk Security Content -![](static/logo.png) +# Splunk Security Content +![](static/logo.png) Welcome to the Splunk Security Content This project gives you access to our repository of Analytic Stories that are security guides which provide background on TTPs, mapped to the MITRE framework, the Lockheed Martin Kill Chain, and CIS controls. They include Splunk searches, machine-learning algorithms, and Splunk Phantom playbooks (where available)—all designed to work together to detect, investigate, and respond to threats. ## View Our Content -You can review our Analytic Stories by category [here](stories_categories.md), or in our [Splunk App](https://github.com/splunk/security_content/releases). + +* [Analytic Stories](docs/stories.md) +* [Detections](docs/detections.md) If you prefer working with the command line, check out our [API](https://docs.splunkresearch.com/?version=latest): @@ -17,15 +19,11 @@ curl -s https://content.splunkresearch.com | jq } ``` -## Getting Started - -Once you've installed our [app](https://github.com/splunk/security_content/releases), we recommend using our Analytic Story Execution App [(ASX)](https://github.com/splunk/analytics_story_execution) to execute and schedule all of the detections a story automatically. - ## Test Out The Detections -The [attack_range](https://github.com/splunk/attack_range) project allows you to spin up an enviroment and launch attacks against it to test the detections. +The [attack_range](https://github.com/splunk/attack_range) project allows you to spin up an enviroment and launch attacks against it to test the detections. ## Questions? -If you get stuck or need help with any of our tools, see our [support options](https://github.com/splunk/security_content#support). +If you get stuck or need help with any of our tools, see our [support options](https://github.com/splunk/security_content#support). ## Contribute Content If you want to help the rest of the security community by sharing your own detections, see our [contributor guide](https://github.com/splunk/security_content/blob/develop/docs/CONTRIBUTING.md). Digital defenders unite! @@ -39,7 +37,7 @@ If you want to help the rest of the security community by sharing your own detec * [baselines/](https://github.com/splunk/security_content/tree/develop/baselines): Splunk Phantom and Splunk Enterprise baseline searches needed to support detection searches in Analytic Stories #### Content Spec Files -* [stories](https://github.com/splunk/security_content/blob/develop/docs/spec/stories.spec.md) +* [stories](https://github.com/splunk/security_content/blob/develop/docs/spec/stories.spec.md) * [detections](https://github.com/splunk/security_content/blob/develop/docs/spec/detections.spec.md) * [deployments](https://github.com/splunk/security_content/blob/develop/docs/spec/deployments.spec.md) * [responses](https://github.com/splunk/security_content/blob/develop/docs/spec/responses.spec.md) @@ -47,6 +45,3 @@ If you want to help the rest of the security community by sharing your own detec * [baselines](https://github.com/splunk/security_content/blob/develop/docs/spec/baselines.spec.md) * [lookups](https://github.com/splunk/security_content/blob/develop/docs/spec/lookups.spec.md) * [macros](https://github.com/splunk/security_content/blob/develop/docs/spec/macros.spec.md) - - - diff --git a/docs/detections.md b/docs/detections.md index a74ca79e96..d7d3480a81 100644 --- a/docs/detections.md +++ b/docs/detections.md @@ -6222,6 +6222,66 @@ _version_: 3 ### Attempted Credential Dump From Registry via Reg exe Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. +- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +- **Datamodel**: Endpoint +- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/) +- **Last Updated**: 2019-12-02 + +
+ details + +#### Search +``` + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` +``` +#### Associated Analytic Story + +* Credential Dumping + + +#### How To Implement +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +#### Required field + + + +#### ATT&CK + +| ID | Technique | Tactic | +| ----------- | ----------- |--------------| +| T1003.002 | Security Account Manager | Credential Access | + + +#### Kill Chain Phase + +* Actions on Objectives + + +#### Known False Positives +None identified. + +#### Reference + + +#### Test Dataset + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + + +_version_: 4 +
+ +--- + +### Attempted Credential Dump From Registry via Reg exe +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + - **Product**: UEBA for Security Cloud - **Datamodel**: - **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/) @@ -6291,66 +6351,6 @@ _version_: 1 --- -### Attempted Credential Dump From Registry via Reg exe -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - -- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -- **Datamodel**: Endpoint -- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/) -- **Last Updated**: 2019-12-02 - -
- details - -#### Search -``` - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `attempted_credential_dump_from_registry_via_reg_exe_filter` -``` -#### Associated Analytic Story - -* Credential Dumping - - -#### How To Implement -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -#### Required field - - - -#### ATT&CK - -| ID | Technique | Tactic | -| ----------- | ----------- |--------------| -| T1003.002 | Security Account Manager | Credential Access | - - -#### Kill Chain Phase - -* Actions on Objectives - - -#### Known False Positives -None identified. - -#### Reference - - -#### Test Dataset - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - - -_version_: 4 -
- ---- - ### BCDEdit Failure Recovery Modification This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. diff --git a/docs/detections.wiki b/docs/detections.wiki index 83443c083e..b1cdfa73be 100644 --- a/docs/detections.wiki +++ b/docs/detections.wiki @@ -10236,6 +10236,71 @@ None identified. Attempts to disable security-related services should be identif ===Attempted credential dump from registry via reg exe=== Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. +* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud +* '''Datamodel''': Endpoint +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.002/ T1003.002] +* '''Last Updated''': 2019-12-02 + +
+
+ +====Search==== + +| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest +| `drop_dm_object_name(Processes)` +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `attempted_credential_dump_from_registry_via_reg_exe_filter` + +====Associated Analytic Story==== + +* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] + + +====How To Implement==== +You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. + +====Required field==== + + + +====ATT&CK==== +{| +! style="text-align:left;"| ID +! Technique +! Tactic +|- +| T1003.002 +| Security Account Manager +| Credential Access +|} + + +====Kill Chain Phase==== + +* Actions on Objectives + + +====Known False Positives==== +None identified. + +====Reference==== + + +====Test Dataset==== + +* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + + +''version'': 4 +
+
+ +---- + +===Attempted credential dump from registry via reg exe=== +Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. + * '''Product''': UEBA for Security Cloud * '''Datamodel''': * '''ATT&CK''': [https://attack.mitre.org/techniques/T1003/ T1003] @@ -10310,71 +10375,6 @@ None identified. ---- -===Attempted credential dump from registry via reg exe=== -Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. - -* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud -* '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.002/ T1003.002] -* '''Last Updated''': 2019-12-02 - -
-
- -====Search==== - -| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest -| `drop_dm_object_name(Processes)` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `attempted_credential_dump_from_registry_via_reg_exe_filter` - -====Associated Analytic Story==== - -* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]] - - -====How To Implement==== -You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. - -====Required field==== - - - -====ATT&CK==== -{| -! style="text-align:left;"| ID -! Technique -! Tactic -|- -| T1003.002 -| Security Account Manager -| Credential Access -|} - - -====Kill Chain Phase==== - -* Actions on Objectives - - -====Known False Positives==== -None identified. - -====Reference==== - - -====Test Dataset==== - -* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - - -''version'': 4 -
-
- ----- - ===Bcdedit failure recovery modification=== This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. diff --git a/docs/stories.md b/docs/stories.md index 0ad7855dfe..20e5d8db0e 100644 --- a/docs/stories.md +++ b/docs/stories.md @@ -590,8 +590,8 @@ Uncover activity consistent with credential dumping, a technique wherein attacke | ----------- | ----------- |--------------| | T1003.001 | LSASS Memory | Credential Access | | T1059.001 | PowerShell | Execution | -| T1003 | OS Credential Dumping | Credential Access | | T1003.002 | Security Account Manager | Credential Access | +| T1003 | OS Credential Dumping | Credential Access | | T1003.003 | NTDS | Credential Access | #### Kill Chain Phase diff --git a/docs/stories.wiki b/docs/stories.wiki index 3623763c32..0f2f16281c 100644 --- a/docs/stories.wiki +++ b/docs/stories.wiki @@ -664,7 +664,7 @@ Uncover activity consistent with credential dumping, a technique wherein attacke * '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud * '''Datamodel''': Endpoint -* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1003/ T1003], [https://attack.mitre.org/techniques/T1003.002/ T1003.002], [https://attack.mitre.org/techniques/T1003.003/ T1003.003] +* '''ATT&CK''': [https://attack.mitre.org/techniques/T1003.001/ T1003.001], [https://attack.mitre.org/techniques/T1059.001/ T1059.001], [https://attack.mitre.org/techniques/T1003.002/ T1003.002], [https://attack.mitre.org/techniques/T1003/ T1003], [https://attack.mitre.org/techniques/T1003.003/ T1003.003] * '''Last Updated''': 2020-02-04
@@ -722,14 +722,14 @@ Uncover activity consistent with credential dumping, a technique wherein attacke | PowerShell | Execution |- -| T1003 -| OS Credential Dumping -| Credential Access -|- | T1003.002 | Security Account Manager | Credential Access |- +| T1003 +| OS Credential Dumping +| Credential Access +|- | T1003.003 | NTDS | Credential Access From b63e3c81773bb0f696975778dd35d10c28374262 Mon Sep 17 00:00:00 2001 From: tcontreras Date: Fri, 19 Mar 2021 17:03:57 +0100 Subject: [PATCH 23/62] detections and test data --- .../endpoint/clop_common_exec_parameter.yml | 14 ++++-- .../clop_ransomware_known_service_name.yml | 36 +++++++++++++++ ...create_service_in_suspicious_file_path.yml | 36 +++++++++++++++ .../endpoint/high_file_deletion_frequency.yml | 39 ++++++++++++++++ .../high_process_termination_frequency_.yml | 36 +++++++++++++++ ...process_deleting_its_process_file_path.yml | 41 +++++++++++++++++ .../ransomware_notes_bulk_creation.yml | 40 ++++++++++++++++ .../endpoint/resize_shadowstorage_volume.yml | 46 +++++++++++++++++++ .../clop_common_exec_parameter.test.yml | 12 +++++ ...lop_ransomware_known_service_name.test.yml | 12 +++++ ...e_service_in_suspicious_file_path.test.yml | 12 +++++ .../high_file_deletion_frequency.test.yml | 12 +++++ ...gh_process_termination_frequency_.test.yml | 12 +++++ ...ss_deleting_its_process_file_path.test.yml | 12 +++++ .../ransomware_notes_bulk_creation.test.yml | 12 +++++ .../resize_shadowstorage_volume.test.yml | 12 +++++ 16 files changed, 379 insertions(+), 5 deletions(-) create mode 100644 detections/endpoint/clop_ransomware_known_service_name.yml create mode 100644 detections/endpoint/create_service_in_suspicious_file_path.yml create mode 100644 detections/endpoint/high_file_deletion_frequency.yml create mode 100644 detections/endpoint/high_process_termination_frequency_.yml create mode 100644 detections/endpoint/process_deleting_its_process_file_path.yml create mode 100644 detections/endpoint/ransomware_notes_bulk_creation.yml create mode 100644 detections/endpoint/resize_shadowstorage_volume.yml create mode 100644 tests/endpoint/clop_common_exec_parameter.test.yml create mode 100644 tests/endpoint/clop_ransomware_known_service_name.test.yml create mode 100644 tests/endpoint/create_service_in_suspicious_file_path.test.yml create mode 100644 tests/endpoint/high_file_deletion_frequency.test.yml create mode 100644 tests/endpoint/high_process_termination_frequency_.test.yml create mode 100644 tests/endpoint/process_deleting_its_process_file_path.test.yml create mode 100644 tests/endpoint/ransomware_notes_bulk_creation.test.yml create mode 100644 tests/endpoint/resize_shadowstorage_volume.test.yml diff --git a/detections/endpoint/clop_common_exec_parameter.yml b/detections/endpoint/clop_common_exec_parameter.yml index 6818ae67b4..cff0846254 100644 --- a/detections/endpoint/clop_common_exec_parameter.yml +++ b/detections/endpoint/clop_common_exec_parameter.yml @@ -12,10 +12,14 @@ description: The following analytics are designed to identifies some CLOP ransom it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly. -search: '`sysmon` EventCode=1 cmdline IN ("*runrun*", "*temp.dat*") | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, - parent_process_name, process_name, OriginalFileName, process_path, CommandLine | - rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `clop_common_exec_parameter_filter`' +search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline + values(Processes.parent_process_name) as parent_process + values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" + by Processes.parent_process_name Processes.process_name Processes.process Processes.dest Processes.user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `clop_common_exec_parameter_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the @@ -29,7 +33,7 @@ tags: - Clop Ransomware automated_detection_testing: tba dataset: - - tba + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log kill_chain_phases: - Obfuscation mitre_attack_id: diff --git a/detections/endpoint/clop_ransomware_known_service_name.yml b/detections/endpoint/clop_ransomware_known_service_name.yml new file mode 100644 index 0000000000..7e7a520fee --- /dev/null +++ b/detections/endpoint/clop_ransomware_known_service_name.yml @@ -0,0 +1,36 @@ +name: Clop Ransomware Known Service Name +id: 07e08a12-870c-11eb-b5f9-acde48001122 +version: 1 +date: '2021-03-17' +author: Teoderick Contreras +type: batch +datamodel: +- Endpoint +description: This detection is to identify the common service name created by the CLOP ransomware as part of its persistence + and high privilege code execution in the infected machine. +search: '`sysmon`EventCode=1 cmdline IN ("*runrun*","*temp.dat*") + | stats count min(_time) as firstTime max(_time) as lastTime count by Computer User EventCode parent_process_name process_name OriginalFileName process_path cmdline + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `clop_ransomware_known_service_name_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting + logs with the Service name, Service File Name Service Start type, and Service Type from your + endpoints. +known_false_positives: unknown +references: +- https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html +- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html +tags: + analytic_story: + - Clop Ransomware + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_service_simulated/windows-system.log + kill_chain_phases: + - Privilege Escalation + mitre_attack_id: + - T1543 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + \ No newline at end of file diff --git a/detections/endpoint/create_service_in_suspicious_file_path.yml b/detections/endpoint/create_service_in_suspicious_file_path.yml new file mode 100644 index 0000000000..462edd8eff --- /dev/null +++ b/detections/endpoint/create_service_in_suspicious_file_path.yml @@ -0,0 +1,36 @@ +name: Create Service In Suspicious File Path +id: 429141be-8311-11eb-adb6-acde48001122 +version: 1 +date: '2021-03-12' +author: Teoderick Contreras +type: batch +datamodel: +- Endpoint +description: This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows. +search: ' `wineventlog_system` EventCode=7045 Service_File_Name = "*\.exe" + NOT (Service_File_Name IN ("C:\\Windows\\*", "C:\\Program File*", "C:\\Programdata\\*", "%systemroot%\\*")) + Service_Type = "user mode service" + | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `create_service_in_suspicious_file_path_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting + logs with the Service name, Service File Name Service Start type, and Service Type from your + endpoints. +known_false_positives: unknown +references: +- https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html +- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html +tags: + analytic_story: + - Clop Ransomware + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_service_simulated/windows-system.log + kill_chain_phases: + - Privilege Escalation + mitre_attack_id: + - T1569.001, T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + \ No newline at end of file diff --git a/detections/endpoint/high_file_deletion_frequency.yml b/detections/endpoint/high_file_deletion_frequency.yml new file mode 100644 index 0000000000..a5a666bf90 --- /dev/null +++ b/detections/endpoint/high_file_deletion_frequency.yml @@ -0,0 +1,39 @@ +name: High File Deletion Frequency +id: 45b125c4-866f-11eb-a95a-acde48001122 +version: 1 +date: '2021-03-16' +author: Teoderick Contreras +type: batch +datamodel: +- Endpoint +description: This search looks for high frequency of file deletion relative to process name and process id. + These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions.. +search: '`sysmon`EventCode=23 + TargetFilename IN ("*\.cmd", "*\.ini","*\.gif", "*\.jpg", "*\.jpeg", "*\.db", "*\.ps1", "*\.doc*", "*\.xls*", "*\.ppt*", "*\.bmp","*\.zip", "*\.rar", "*\.7z", "*\.chm", "*\.png", "*\.log", "*\.vbs", "*\.js") + | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer user EventCode Image ProcessID + |where count >=100 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `high_file_deletion_frequency_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting + logs with the deleted target file name, process name and process id from your + endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the + Sysmon TA. +known_false_positives: user may delete bunch of pictures or files in a folder. +references: +- https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html +- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html +tags: + analytic_story: + - Clop Ransomware + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + kill_chain_phases: + - Exploitation + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + \ No newline at end of file diff --git a/detections/endpoint/high_process_termination_frequency_.yml b/detections/endpoint/high_process_termination_frequency_.yml new file mode 100644 index 0000000000..c83be6fe5c --- /dev/null +++ b/detections/endpoint/high_process_termination_frequency_.yml @@ -0,0 +1,36 @@ +name: High Process Termination Frequency +id: 17cd75b2-8666-11eb-9ab4-acde48001122 +version: 1 +date: '2021-03-16' +author: Teoderick Contreras +type: batch +datamodel: +- Endpoint +description: This analytics are designed to indentify a high frequency of process termination on a machine which is a common behavior of ransomware malware before encrypting files. + This technique is designed to avoid an exception error while accessing (docs, images, database and etc..) in the infected machine for encryption. +search: '`sysmon`EventCode=5 + |bin _time span=3s + |stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime count by Computer EventCode | where count >= 15 + | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `high_process_termination_frequency__filter`' +how_to_implement: To successfully implement this search, you need to be ingesting + logs with the Image (process full path of terminated process) from your + endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the + Sysmon TA. +known_false_positives: UPDATE_KNOWN_FALSE_POSITIVES +references: +- https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html +- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html +tags: + analytic_story: + - Clop Ransomware + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + kill_chain_phases: + - Exploitation + mitre_attack_id: + - T1486 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + \ No newline at end of file diff --git a/detections/endpoint/process_deleting_its_process_file_path.yml b/detections/endpoint/process_deleting_its_process_file_path.yml new file mode 100644 index 0000000000..8e98704835 --- /dev/null +++ b/detections/endpoint/process_deleting_its_process_file_path.yml @@ -0,0 +1,41 @@ +name: Process Deleting Its Process File Path +id: f7eda4bc-871c-11eb-b110-acde48001122 +version: 1 +date: '2021-03-17' +author: Teoderick Contreras +type: batch +datamodel: +- Endpoint +description: This detection is to identify a suspicious process that tries to delete the process file path related to its process. + This technique is known to be defense evasion once a certain condition of malware is satisfied or not. + Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect. +search: '`sysmon`EventCode=1 cmdline = "*/c del*" Image = "*\\cmd.exe" + |eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") + | stats min(_time) as firstTime max(_time) as lastTime count by Computer user ParentImage ParentCommandLine Image cmdline EventCode ProcessID result + | where result = "contained" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `process_deleting_its_process_file_path_filter`' +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +known_false_positives: unknown +references: +- https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html +- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html +tags: + analytic_story: + - Clop Ransomware + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + kill_chain_phases: + - Exploitation + mitre_attack_id: + - T1003.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + \ No newline at end of file diff --git a/detections/endpoint/ransomware_notes_bulk_creation.yml b/detections/endpoint/ransomware_notes_bulk_creation.yml new file mode 100644 index 0000000000..8b6e9508a9 --- /dev/null +++ b/detections/endpoint/ransomware_notes_bulk_creation.yml @@ -0,0 +1,40 @@ +name: Ransomware Notes bulk creation +id: eff7919a-8330-11eb-83f8-acde48001122 +version: 1 +date: '2021-03-12' +author: Teoderick Contreras +type: batch +datamodel: +- Endpoint +description: The following analytics identifies a big number of instance of ransomware notes (filetype e.g .txt, .html, .hta) + file creation to the infected machine. This behavior is a good sensor if the ransomware note filename is quite new for security industry + or the ransomware note filename is not in your lookup table list for monitoring. + +search: '`sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") +| stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path by Computer Image file_name +| where unique_readme_path_count >= 50 +| `security_content_ctime(firstTime)` +| `security_content_ctime(lastTime)` +| `ransomware_notes_bulk_creation_filter`' +how_to_implement: "You must be ingesting data that records the filesystem activity + from your hosts to populate the Endpoint file-system data model node. If you are + using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which + you want to collect data." +known_false_positives: unknown +references: +- https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html +- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html +tags: + analytic_story: + - Clop Ransomware + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + kill_chain_phases: + - Obfuscation + mitre_attack_id: + - T1486 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + \ No newline at end of file diff --git a/detections/endpoint/resize_shadowstorage_volume.yml b/detections/endpoint/resize_shadowstorage_volume.yml new file mode 100644 index 0000000000..38e1edcfc3 --- /dev/null +++ b/detections/endpoint/resize_shadowstorage_volume.yml @@ -0,0 +1,46 @@ +name: Resize ShadowStorage volume +id: bc760ca6-8336-11eb-bcbb-acde48001122 +version: 1 +date: '2021-03-12' +author: Teoderick Contreras +type: batch +datamodel: +- Endpoint +description: The following analytics identifies the resizing of shadowstorage by ransomware malware to avoid the shadow volumes being made again. + this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. + + one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum + size as much as possible +search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline + values(Processes.parent_process_name) as parent_process + values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell.exe" OR + Processes.parent_process_name = "powershell_ise.exe" OR + Processes.parent_process_name = "wmic.exe" + Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" + by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user + | `security_content_ctime(firstTime)` + |`security_content_ctime(lastTime)` + | `resize_shadowstorage_volume_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting + logs with the process name, parent process, and command-line executions from your + endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the + Sysmon TA. +known_false_positives: network admin can resize the shadowstorage for valid purposes. +references: +- https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html +- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html +tags: + analytic_story: + - Clop Ransomware + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + kill_chain_phases: + - Exploitation + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + \ No newline at end of file diff --git a/tests/endpoint/clop_common_exec_parameter.test.yml b/tests/endpoint/clop_common_exec_parameter.test.yml new file mode 100644 index 0000000000..7334f1f4be --- /dev/null +++ b/tests/endpoint/clop_common_exec_parameter.test.yml @@ -0,0 +1,12 @@ +name: Clop Common Exec Parameter Unit Test +tests: +- name: Clop Common Exec Parameter + file: detections/endpoint/clop_ransomware_common_exec_parameter.yml + pass_condition: '| stats count | where count > 0' + earliest_time: '-24h' + latest_time: 'now' + attack_data: + - file_name: windows-sysmon.log + data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: xmlwineventlog \ No newline at end of file diff --git a/tests/endpoint/clop_ransomware_known_service_name.test.yml b/tests/endpoint/clop_ransomware_known_service_name.test.yml new file mode 100644 index 0000000000..d43dd7dbde --- /dev/null +++ b/tests/endpoint/clop_ransomware_known_service_name.test.yml @@ -0,0 +1,12 @@ +name: Clop Ransomware Known Service Name Unit Test +tests: +- name: Clop Ransomware Known Service Name + file: detections/endpoint/clop_ransomware_known_service_name.yml + pass_condition: '| stats count | where count > 0' + earliest_time: '-24h' + latest_time: 'now' + attack_data: + - file_name: windows-system.log + data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_service_simulated/windows-system.log + source: WinEventLog:System + sourcetype: WinEventLog \ No newline at end of file diff --git a/tests/endpoint/create_service_in_suspicious_file_path.test.yml b/tests/endpoint/create_service_in_suspicious_file_path.test.yml new file mode 100644 index 0000000000..b5bbf55ef7 --- /dev/null +++ b/tests/endpoint/create_service_in_suspicious_file_path.test.yml @@ -0,0 +1,12 @@ +name: Create Service In Suspicious File Path Unit Test +tests: +- name: Create Service In Suspicious File Path + file: detections/endpoint/create_service_in_suspicious_file_path.yml + pass_condition: '| stats count | where count > 0' + earliest_time: '-24h' + latest_time: 'now' + attack_data: + - file_name: windows-system.log + data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_service_simulated/windows-system.log + source: WinEventLog:System + sourcetype: WinEventLog \ No newline at end of file diff --git a/tests/endpoint/high_file_deletion_frequency.test.yml b/tests/endpoint/high_file_deletion_frequency.test.yml new file mode 100644 index 0000000000..51a7b94a06 --- /dev/null +++ b/tests/endpoint/high_file_deletion_frequency.test.yml @@ -0,0 +1,12 @@ +name: High File Deletion Frequency Unit Test +tests: +- name: High File Deletion Frequency + file: detections/endpoint/high_file_deletion_frequency.yml + pass_condition: '| stats count | where count > 0' + earliest_time: '-24h' + latest_time: 'now' + attack_data: + - file_name: windows-sysmon.log + data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: xmlwineventlog \ No newline at end of file diff --git a/tests/endpoint/high_process_termination_frequency_.test.yml b/tests/endpoint/high_process_termination_frequency_.test.yml new file mode 100644 index 0000000000..6638357205 --- /dev/null +++ b/tests/endpoint/high_process_termination_frequency_.test.yml @@ -0,0 +1,12 @@ +name: High Process Termination Frequency Unit Test +tests: +- name: High Process Termination Frequency + file: detections/endpoint/high_process_termination_frequency_.yml + pass_condition: '| stats count | where count > 0' + earliest_time: '-24h' + latest_time: 'now' + attack_data: + - file_name: windows-sysmon.log + data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: xmlwineventlog \ No newline at end of file diff --git a/tests/endpoint/process_deleting_its_process_file_path.test.yml b/tests/endpoint/process_deleting_its_process_file_path.test.yml new file mode 100644 index 0000000000..1364504d5a --- /dev/null +++ b/tests/endpoint/process_deleting_its_process_file_path.test.yml @@ -0,0 +1,12 @@ +name: Process Deleting Its Process File Path Unit Test +tests: +- name: Process Deleting Its Process File Path + file: detections/endpoint/process_deleting_its_process_file_path.yml + pass_condition: '| stats count | where count > 0' + earliest_time: '-24h' + latest_time: 'now' + attack_data: + - file_name: windows-sysmon.log + data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: xmlwineventlog \ No newline at end of file diff --git a/tests/endpoint/ransomware_notes_bulk_creation.test.yml b/tests/endpoint/ransomware_notes_bulk_creation.test.yml new file mode 100644 index 0000000000..83e084f8d4 --- /dev/null +++ b/tests/endpoint/ransomware_notes_bulk_creation.test.yml @@ -0,0 +1,12 @@ +name: Ransomware Notes bulk creation Unit Test +tests: +- name: Ransomware Notes bulk creation + file: detections/endpoint/ransomware_notes_bulk_creation.yml + pass_condition: '| stats count | where count > 0' + earliest_time: '-24h' + latest_time: 'now' + attack_data: + - file_name: windows-sysmon.log + data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: xmlwineventlog \ No newline at end of file diff --git a/tests/endpoint/resize_shadowstorage_volume.test.yml b/tests/endpoint/resize_shadowstorage_volume.test.yml new file mode 100644 index 0000000000..d4c7a5571c --- /dev/null +++ b/tests/endpoint/resize_shadowstorage_volume.test.yml @@ -0,0 +1,12 @@ +name: Resize ShadowStorage volume Unit Test +tests: +- name: Resize ShadowStorage volume + file: detections/endpoint/resize_shadowstorage_volume.yml + pass_condition: '| stats count | where count > 0' + earliest_time: '-24h' + latest_time: 'now' + attack_data: + - file_name: windows-sysmon.log + data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: xmlwineventlog \ No newline at end of file From 63aad3ee438db44d585af29dd0f908870967e83c Mon Sep 17 00:00:00 2001 From: divious1 Date: Fri, 19 Mar 2021 13:58:48 -0400 Subject: [PATCH 24/62] deprecating detection --- .../ssa___rare_parent_process_relationship_lolbas.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename detections/{endpoint => deprecated}/ssa___rare_parent_process_relationship_lolbas.yml (98%) diff --git a/detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml b/detections/deprecated/ssa___rare_parent_process_relationship_lolbas.yml similarity index 98% rename from detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml rename to detections/deprecated/ssa___rare_parent_process_relationship_lolbas.yml index 016e7518ad..1fa1d784df 100644 --- a/detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml +++ b/detections/deprecated/ssa___rare_parent_process_relationship_lolbas.yml @@ -47,7 +47,7 @@ search: '| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe") | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, - dest_user_id), body = "TBD" | into write_ssa_detected_events();' + dest_user_id), body = "TBD"' how_to_implement: Collect endpoint data such as sysmon or 4688 events. known_false_positives: 'Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when From 066b81e6ef797f73970d20d19bc1fc6787f71ee3 Mon Sep 17 00:00:00 2001 From: divious1 Date: Fri, 19 Mar 2021 13:59:56 -0400 Subject: [PATCH 25/62] moved out of deprecated --- .../ssa___rare_parent_process_relationship_lolbas.yml | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename detections/{deprecated => endpoint}/ssa___rare_parent_process_relationship_lolbas.yml (100%) diff --git a/detections/deprecated/ssa___rare_parent_process_relationship_lolbas.yml b/detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml similarity index 100% rename from detections/deprecated/ssa___rare_parent_process_relationship_lolbas.yml rename to detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml From 6bf8b82877eb2bcbb3812f671dc6272d4ef8a355 Mon Sep 17 00:00:00 2001 From: divious1 Date: Fri, 19 Mar 2021 14:08:26 -0400 Subject: [PATCH 26/62] added write to null --- .../endpoint/ssa___rare_parent_process_relationship_lolbas.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml b/detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml index 1fa1d784df..2168b8e1ef 100644 --- a/detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml +++ b/detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml @@ -47,7 +47,7 @@ search: '| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe") | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, - dest_user_id), body = "TBD"' + dest_user_id), body = "TBD" | into write_null();' how_to_implement: Collect endpoint data such as sysmon or 4688 events. known_false_positives: 'Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when From 0ec2001838689af50d00ae0a82586d59cb02ca15 Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Fri, 19 Mar 2021 13:52:34 -0700 Subject: [PATCH 27/62] Renaming test .yaml -> .yml | No events should come out of the detection --- ...l => ssa___rare_parent_process_relationship_lolbas.test.yml} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename tests/endpoint/{ssa___rare_parent_process_relationship_lolbas.test.yaml => ssa___rare_parent_process_relationship_lolbas.test.yml} (93%) diff --git a/tests/endpoint/ssa___rare_parent_process_relationship_lolbas.test.yaml b/tests/endpoint/ssa___rare_parent_process_relationship_lolbas.test.yml similarity index 93% rename from tests/endpoint/ssa___rare_parent_process_relationship_lolbas.test.yaml rename to tests/endpoint/ssa___rare_parent_process_relationship_lolbas.test.yml index a44cdc41f0..7cc3da7342 100644 --- a/tests/endpoint/ssa___rare_parent_process_relationship_lolbas.test.yaml +++ b/tests/endpoint/ssa___rare_parent_process_relationship_lolbas.test.yml @@ -2,7 +2,7 @@ name: Rare Parent/Child Process Relationship - SSA Unit Test tests: - name: Access LSASS Memory for Dump Creation file: endpoint/ssa___rare_parent_process_relationship_lolbas.yml - pass_condition: '@count_gt(0)' + pass_condition: '@count_eq(0)' description: Test detection looking for LOLBAS processes spawned by other processes that are rarely seen together attack_data: - file_name: T1059.all.labeled.lolbas-test.json From 593ae74fdf3d654fe6513ff124c37aa571dcb4b1 Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Fri, 19 Mar 2021 14:51:53 -0700 Subject: [PATCH 28/62] Refactoring template replacement --- bin/ssa-end-to-end-testing/modules/utils.py | 24 +++++++++++---------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/bin/ssa-end-to-end-testing/modules/utils.py b/bin/ssa-end-to-end-testing/modules/utils.py index b6c5cc7ede..8548d0be47 100644 --- a/bin/ssa-end-to-end-testing/modules/utils.py +++ b/bin/ssa-end-to-end-testing/modules/utils.py @@ -76,13 +76,19 @@ def request_headers(header_token): def manipulate_spl(env, spl, results_index): - spl = replace_ssa_macros(env, spl) + # Obtain the SSA source + pulsar_source_connection_id, pulsar_source_topic = return_macros(env) + source = READ_SSA_ENRICHED_EVENTS_EXPANDED\ + .replace("__PULSAR_SOURCE_CONNECTION_ID__", pulsar_source_connection_id)\ + .replace("__PULSAR_SOURCE_TOPIC__", pulsar_source_topic) + # Obtain the test sink + sink = ";" if results_index is not None: - # When an index is defined for a test, it writes the output of this pipeline to this index. - # original_pipeline; => original_pipeline | into index("module", "index"); module = results_index["module"] index = results_index["name"] - spl = spl[:spl.rindex(";")] + f" | into index(\"{module}\", \"{index}\");" + sink = f"| into index(\"{module}\", \"{index}\");" + # Replace spl template with its `source` and `sink` + spl = replace_ssa_macros(source, sink, spl) LOGGER.info(f"spl: {spl}") return spl @@ -93,13 +99,9 @@ def read_spl(file_path, file_name): return spl -def replace_ssa_macros(env, spl): - pulsar_source_connection_id, pulsar_source_topic = return_macros(env) - macro_expanded = READ_SSA_ENRICHED_EVENTS_EXPANDED.replace("__PULSAR_SOURCE_CONNECTION_ID__", pulsar_source_connection_id) - macro_expanded = macro_expanded.replace("__PULSAR_SOURCE_TOPIC__", pulsar_source_topic) - spl = spl.replace(READ_SSA_ENRICHED_EVENTS, macro_expanded) - spl = spl.replace(WRITE_SSA_DETECTED_EVENTS, ";") - #spl = spl.replace("\n", " ") +def replace_ssa_macros(source, sink, spl): + spl = spl.replace(READ_SSA_ENRICHED_EVENTS, source) + spl = spl.replace(WRITE_SSA_DETECTED_EVENTS, sink) return spl From 548f768ff34b7484d757878dbb1e3ecce4cfcd1c Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Fri, 19 Mar 2021 15:10:06 -0700 Subject: [PATCH 29/62] refactoring base tests --- bin/ssa-end-to-end-testing/modules/spl/detection.spl | 3 ++- bin/ssa-end-to-end-testing/modules/spl/detection2.spl | 3 ++- bin/ssa-end-to-end-testing/modules/spl/firehose.spl | 2 +- bin/ssa-end-to-end-testing/modules/spl/troubleshoot.spl | 2 +- 4 files changed, 6 insertions(+), 4 deletions(-) diff --git a/bin/ssa-end-to-end-testing/modules/spl/detection.spl b/bin/ssa-end-to-end-testing/modules/spl/detection.spl index c4cf977814..1e2c938227 100644 --- a/bin/ssa-end-to-end-testing/modules/spl/detection.spl +++ b/bin/ssa-end-to-end-testing/modules/spl/detection.spl @@ -17,4 +17,5 @@ | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), -body = "TBD"; \ No newline at end of file +body = "TBD" +| into write_ssa_detected_events(); \ No newline at end of file diff --git a/bin/ssa-end-to-end-testing/modules/spl/detection2.spl b/bin/ssa-end-to-end-testing/modules/spl/detection2.spl index d035fc7f36..73cabaaafc 100644 --- a/bin/ssa-end-to-end-testing/modules/spl/detection2.spl +++ b/bin/ssa-end-to-end-testing/modules/spl/detection2.spl @@ -17,4 +17,5 @@ | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), -body = "TBD"; +body = "TBD" +| into write_ssa_detected_events(); diff --git a/bin/ssa-end-to-end-testing/modules/spl/firehose.spl b/bin/ssa-end-to-end-testing/modules/spl/firehose.spl index be0790a827..c8bdf3a602 100644 --- a/bin/ssa-end-to-end-testing/modules/spl/firehose.spl +++ b/bin/ssa-end-to-end-testing/modules/spl/firehose.spl @@ -1 +1 @@ -| from read_splunk_firehose(); \ No newline at end of file + | from read_splunk_firehose(); \ No newline at end of file diff --git a/bin/ssa-end-to-end-testing/modules/spl/troubleshoot.spl b/bin/ssa-end-to-end-testing/modules/spl/troubleshoot.spl index c093070090..d374be63ef 100644 --- a/bin/ssa-end-to-end-testing/modules/spl/troubleshoot.spl +++ b/bin/ssa-end-to-end-testing/modules/spl/troubleshoot.spl @@ -1 +1 @@ -| from read_ssa_enriched_events(); \ No newline at end of file +| from read_ssa_enriched_events() | into write_ssa_detected_events(); \ No newline at end of file From 1eb44354e82d00693d10943caa55eaa6d4c90110 Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Fri, 19 Mar 2021 15:54:47 -0700 Subject: [PATCH 30/62] renaming back test for ssa detection --- ...ml => ssa___rare_parent_process_relationship_lolbas.test.yaml} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/endpoint/{ssa___rare_parent_process_relationship_lolbas.test.yml => ssa___rare_parent_process_relationship_lolbas.test.yaml} (100%) diff --git a/tests/endpoint/ssa___rare_parent_process_relationship_lolbas.test.yml b/tests/endpoint/ssa___rare_parent_process_relationship_lolbas.test.yaml similarity index 100% rename from tests/endpoint/ssa___rare_parent_process_relationship_lolbas.test.yml rename to tests/endpoint/ssa___rare_parent_process_relationship_lolbas.test.yaml From d47e5c395e5f1177f91a53d05e6a8a410caa3576 Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Fri, 19 Mar 2021 16:45:05 -0700 Subject: [PATCH 31/62] Dealing with yaml and yml for testing --- bin/ssa-end-to-end-testing/modules/github_service.py | 7 ++++--- bin/ssa-end-to-end-testing/run_ssa_smoketest.py | 2 +- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/bin/ssa-end-to-end-testing/modules/github_service.py b/bin/ssa-end-to-end-testing/modules/github_service.py index de159e476c..82ed3b8c15 100644 --- a/bin/ssa-end-to-end-testing/modules/github_service.py +++ b/bin/ssa-end-to-end-testing/modules/github_service.py @@ -35,8 +35,6 @@ class GithubService: changed_ssa_test_files = [] - #tests = self.read_security_content_test_files() - for file_path in changed_files: # added or changed test files if file_path.startswith('tests'): @@ -47,7 +45,10 @@ class GithubService: # changed detections if file_path.startswith('detections'): if os.path.basename(file_path).startswith('ssa'): - file_path_new = os.path.splitext(file_path)[0].replace('detections', 'tests') + '.test.yml' + file_path_base = os.path.splitext(file_path)[0].replace('detections', 'tests') + '.test' + file_path_new = file_path_base + '.yml' + if not os.path.exists(file_path_new): + file_path_new = file_path_base + '.yaml' if file_path_new not in changed_ssa_test_files: changed_ssa_test_files.append(file_path_new) diff --git a/bin/ssa-end-to-end-testing/run_ssa_smoketest.py b/bin/ssa-end-to-end-testing/run_ssa_smoketest.py index aeb006f38b..6e69820fba 100644 --- a/bin/ssa-end-to-end-testing/run_ssa_smoketest.py +++ b/bin/ssa-end-to-end-testing/run_ssa_smoketest.py @@ -38,7 +38,7 @@ def main(args): for test_file in test_files_ssa: LOGGER.info(test_file) - if len(test_files_ssa)==0: + if len(test_files_ssa) == 0: LOGGER.info('Nothing to test for SSA smoke test.') sys.exit(0) From ba2835963d70b448d86f726257719a2edf63dd51 Mon Sep 17 00:00:00 2001 From: tcontreras Date: Mon, 22 Mar 2021 11:07:01 +0100 Subject: [PATCH 32/62] modify-detections --- .../endpoint/clop_common_exec_parameter.yml | 33 +++++++++++-------- .../clop_ransomware_known_service_name.yml | 22 +++++++++---- ...create_service_in_suspicious_file_path.yml | 21 +++++++----- .../endpoint/high_file_deletion_frequency.yml | 26 +++++++++------ ...=> high_process_termination_frequency.yml} | 19 ++++++----- ...process_deleting_its_process_file_path.yml | 30 +++++++++++------ .../ransomware_notes_bulk_creation.yml | 26 ++++++++++----- .../endpoint/resize_shadowstorage_volume.yml | 11 +++++-- 8 files changed, 120 insertions(+), 68 deletions(-) rename detections/endpoint/{high_process_termination_frequency_.yml => high_process_termination_frequency.yml} (63%) diff --git a/detections/endpoint/clop_common_exec_parameter.yml b/detections/endpoint/clop_common_exec_parameter.yml index cff0846254..96be25d960 100644 --- a/detections/endpoint/clop_common_exec_parameter.yml +++ b/detections/endpoint/clop_common_exec_parameter.yml @@ -7,19 +7,19 @@ type: batch datamodel: - Endpoint description: The following analytics are designed to identifies some CLOP ransomware variant that using arguments - to execute its main code or feature of its code. In this variant if the parameter is "runrun", - CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", - it will try to read from some stream pipe or file start encrypting files within the infected local machines. - This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is - waiting for some parameter to execute properly. + to execute its main code or feature of its code. In this variant if the parameter is "runrun", + CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", + it will try to read from some stream pipe or file start encrypting files within the infected local machines. + This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is + waiting for some parameter to execute properly. search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline - values(Processes.parent_process_name) as parent_process - values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" - by Processes.parent_process_name Processes.process_name Processes.process Processes.dest Processes.user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `clop_common_exec_parameter_filter`' + values(Processes.parent_process_name) as parent_process + values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" + by Processes.parent_process_name Processes.process_name Processes.process Processes.dest Processes.user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `clop_common_exec_parameter_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the @@ -32,8 +32,6 @@ tags: analytic_story: - Clop Ransomware automated_detection_testing: tba - dataset: - - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log kill_chain_phases: - Obfuscation mitre_attack_id: @@ -42,4 +40,11 @@ tags: - Splunk Enterprise - Splunk Enterprise Security - Splunk Cloud + required_fields: + - Processes.process + - Processes.parent_process_name + - _time + - Processes.process_name + - Processes.dest + - Processes.user security_domain: endpoint diff --git a/detections/endpoint/clop_ransomware_known_service_name.yml b/detections/endpoint/clop_ransomware_known_service_name.yml index 7e7a520fee..6f70f48c4a 100644 --- a/detections/endpoint/clop_ransomware_known_service_name.yml +++ b/detections/endpoint/clop_ransomware_known_service_name.yml @@ -7,12 +7,13 @@ type: batch datamodel: - Endpoint description: This detection is to identify the common service name created by the CLOP ransomware as part of its persistence - and high privilege code execution in the infected machine. + and high privilege code execution in the infected machine. search: '`sysmon`EventCode=1 cmdline IN ("*runrun*","*temp.dat*") - | stats count min(_time) as firstTime max(_time) as lastTime count by Computer User EventCode parent_process_name process_name OriginalFileName process_path cmdline - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `clop_ransomware_known_service_name_filter`' + | stats count min(_time) as firstTime max(_time) as lastTime count + by Computer User EventCode parent_process_name process_name OriginalFileName process_path cmdline + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `clop_ransomware_known_service_name_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. @@ -23,8 +24,6 @@ references: tags: analytic_story: - Clop Ransomware - dataset: - - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_service_simulated/windows-system.log kill_chain_phases: - Privilege Escalation mitre_attack_id: @@ -33,4 +32,13 @@ tags: - Splunk Enterprise - Splunk Enterprise Security - Splunk Cloud + required_fields: + - EventCode + - cmdline + - _time + - parent_process_name + - process_name + - OriginalFileName + - process_path + security_domain: endpoint \ No newline at end of file diff --git a/detections/endpoint/create_service_in_suspicious_file_path.yml b/detections/endpoint/create_service_in_suspicious_file_path.yml index 462edd8eff..5acda780b0 100644 --- a/detections/endpoint/create_service_in_suspicious_file_path.yml +++ b/detections/endpoint/create_service_in_suspicious_file_path.yml @@ -8,11 +8,11 @@ datamodel: - Endpoint description: This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows. search: ' `wineventlog_system` EventCode=7045 Service_File_Name = "*\.exe" - NOT (Service_File_Name IN ("C:\\Windows\\*", "C:\\Program File*", "C:\\Programdata\\*", "%systemroot%\\*")) - Service_Type = "user mode service" - | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `create_service_in_suspicious_file_path_filter`' + NOT (Service_File_Name IN ("C:\\Windows\\*", "C:\\Program File*", "C:\\Programdata\\*", "%systemroot%\\*")) + Service_Type = "user mode service" + | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `create_service_in_suspicious_file_path_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. @@ -23,8 +23,6 @@ references: tags: analytic_story: - Clop Ransomware - dataset: - - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_service_simulated/windows-system.log kill_chain_phases: - Privilege Escalation mitre_attack_id: @@ -33,4 +31,11 @@ tags: - Splunk Enterprise - Splunk Enterprise Security - Splunk Cloud - \ No newline at end of file + required_fields: + - EventCode + - Service_File_Name + - Service_Type + - _time + - Service_Name + - Service_Start_Type + security_domain: endpoint \ No newline at end of file diff --git a/detections/endpoint/high_file_deletion_frequency.yml b/detections/endpoint/high_file_deletion_frequency.yml index a5a666bf90..dc99cc2d2b 100644 --- a/detections/endpoint/high_file_deletion_frequency.yml +++ b/detections/endpoint/high_file_deletion_frequency.yml @@ -7,14 +7,14 @@ type: batch datamodel: - Endpoint description: This search looks for high frequency of file deletion relative to process name and process id. - These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions.. + These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions.. search: '`sysmon`EventCode=23 - TargetFilename IN ("*\.cmd", "*\.ini","*\.gif", "*\.jpg", "*\.jpeg", "*\.db", "*\.ps1", "*\.doc*", "*\.xls*", "*\.ppt*", "*\.bmp","*\.zip", "*\.rar", "*\.7z", "*\.chm", "*\.png", "*\.log", "*\.vbs", "*\.js") - | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer user EventCode Image ProcessID - |where count >=100 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `high_file_deletion_frequency_filter`' + TargetFilename IN ("*\.cmd", "*\.ini","*\.gif", "*\.jpg", "*\.jpeg", "*\.db", "*\.ps1", "*\.doc*", "*\.xls*", "*\.ppt*", "*\.bmp","*\.zip", "*\.rar", "*\.7z", "*\.chm", "*\.png", "*\.log", "*\.vbs", "*\.js") + | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer user EventCode Image ProcessID + |where count >=100 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `high_file_deletion_frequency_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the deleted target file name, process name and process id from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the @@ -26,8 +26,6 @@ references: tags: analytic_story: - Clop Ransomware - dataset: - - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log kill_chain_phases: - Exploitation mitre_attack_id: @@ -36,4 +34,12 @@ tags: - Splunk Enterprise - Splunk Enterprise Security - Splunk Cloud - \ No newline at end of file + required_fields: + - EventCode + - TargetFilename + - Computer + - user + - Image + - ProcessID + - _time + security_domain: endpoint \ No newline at end of file diff --git a/detections/endpoint/high_process_termination_frequency_.yml b/detections/endpoint/high_process_termination_frequency.yml similarity index 63% rename from detections/endpoint/high_process_termination_frequency_.yml rename to detections/endpoint/high_process_termination_frequency.yml index c83be6fe5c..35b2cf647c 100644 --- a/detections/endpoint/high_process_termination_frequency_.yml +++ b/detections/endpoint/high_process_termination_frequency.yml @@ -1,4 +1,4 @@ -name: High Process Termination Frequency +name: High Process Termination Frequency id: 17cd75b2-8666-11eb-9ab4-acde48001122 version: 1 date: '2021-03-16' @@ -7,11 +7,11 @@ type: batch datamodel: - Endpoint description: This analytics are designed to indentify a high frequency of process termination on a machine which is a common behavior of ransomware malware before encrypting files. - This technique is designed to avoid an exception error while accessing (docs, images, database and etc..) in the infected machine for encryption. + This technique is designed to avoid an exception error while accessing (docs, images, database and etc..) in the infected machine for encryption. search: '`sysmon`EventCode=5 - |bin _time span=3s - |stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime count by Computer EventCode | where count >= 15 - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `high_process_termination_frequency__filter`' + |bin _time span=3s + |stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime count by Computer EventCode | where count >= 15 + | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `high_process_termination_frequency_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the Image (process full path of terminated process) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the @@ -23,8 +23,6 @@ references: tags: analytic_story: - Clop Ransomware - dataset: - - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log kill_chain_phases: - Exploitation mitre_attack_id: @@ -33,4 +31,9 @@ tags: - Splunk Enterprise - Splunk Enterprise Security - Splunk Cloud - \ No newline at end of file + required_fields: + - EventCode + - Image + - Computer + - _time + security_domain: endpoint \ No newline at end of file diff --git a/detections/endpoint/process_deleting_its_process_file_path.yml b/detections/endpoint/process_deleting_its_process_file_path.yml index 8e98704835..a6a3ba3ad6 100644 --- a/detections/endpoint/process_deleting_its_process_file_path.yml +++ b/detections/endpoint/process_deleting_its_process_file_path.yml @@ -7,15 +7,15 @@ type: batch datamodel: - Endpoint description: This detection is to identify a suspicious process that tries to delete the process file path related to its process. - This technique is known to be defense evasion once a certain condition of malware is satisfied or not. - Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect. + This technique is known to be defense evasion once a certain condition of malware is satisfied or not. + Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect. search: '`sysmon`EventCode=1 cmdline = "*/c del*" Image = "*\\cmd.exe" - |eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") - | stats min(_time) as firstTime max(_time) as lastTime count by Computer user ParentImage ParentCommandLine Image cmdline EventCode ProcessID result - | where result = "contained" - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `process_deleting_its_process_file_path_filter`' + |eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") + | stats min(_time) as firstTime max(_time) as lastTime count by Computer user ParentImage ParentCommandLine Image cmdline EventCode ProcessID result + | where result = "contained" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `process_deleting_its_process_file_path_filter`' how_to_implement: You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. @@ -28,8 +28,6 @@ references: tags: analytic_story: - Clop Ransomware - dataset: - - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log kill_chain_phases: - Exploitation mitre_attack_id: @@ -38,4 +36,16 @@ tags: - Splunk Enterprise - Splunk Enterprise Security - Splunk Cloud + required_fields: + - EventCode + - Computer + - user + - ParentImage + - ParentCommandLine + - Image + - cmdline + - ProcessID + - result + - _time + security_domain: endpoint \ No newline at end of file diff --git a/detections/endpoint/ransomware_notes_bulk_creation.yml b/detections/endpoint/ransomware_notes_bulk_creation.yml index 8b6e9508a9..7bf3e1e3a0 100644 --- a/detections/endpoint/ransomware_notes_bulk_creation.yml +++ b/detections/endpoint/ransomware_notes_bulk_creation.yml @@ -7,15 +7,16 @@ type: batch datamodel: - Endpoint description: The following analytics identifies a big number of instance of ransomware notes (filetype e.g .txt, .html, .hta) - file creation to the infected machine. This behavior is a good sensor if the ransomware note filename is quite new for security industry - or the ransomware note filename is not in your lookup table list for monitoring. + file creation to the infected machine. This behavior is a good sensor if the ransomware note filename is quite new for security industry + or the ransomware note filename is not in your lookup table list for monitoring. search: '`sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") -| stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path by Computer Image file_name -| where unique_readme_path_count >= 50 -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `ransomware_notes_bulk_creation_filter`' + | stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path + by Computer Image file_name + | where unique_readme_path_count >= 50 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ransomware_notes_bulk_creation_filter`' how_to_implement: "You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which @@ -27,8 +28,6 @@ references: tags: analytic_story: - Clop Ransomware - dataset: - - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log kill_chain_phases: - Obfuscation mitre_attack_id: @@ -37,4 +36,13 @@ tags: - Splunk Enterprise - Splunk Enterprise Security - Splunk Cloud + required_fields: + - EventCode + - file_name + - _time + - TargetFilename + - Computer + - Image + - user + security_domain: endpoint \ No newline at end of file diff --git a/detections/endpoint/resize_shadowstorage_volume.yml b/detections/endpoint/resize_shadowstorage_volume.yml index 38e1edcfc3..05d43956f2 100644 --- a/detections/endpoint/resize_shadowstorage_volume.yml +++ b/detections/endpoint/resize_shadowstorage_volume.yml @@ -33,8 +33,6 @@ references: tags: analytic_story: - Clop Ransomware - dataset: - - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log kill_chain_phases: - Exploitation mitre_attack_id: @@ -43,4 +41,13 @@ tags: - Splunk Enterprise - Splunk Enterprise Security - Splunk Cloud + required_fields: + - Processes.process + - Process.parent_process_name + - _time + - Processes.process_name + - Processes.parent_process + - Processes.dest + - Processes.user + security_domain: endpoint \ No newline at end of file From b42ef50322a1faf2a76956c78bab5e7781d5caf5 Mon Sep 17 00:00:00 2001 From: tcontreras Date: Mon, 22 Mar 2021 11:45:21 +0100 Subject: [PATCH 33/62] clop_test_file --- tests/endpoint/clop_common_exec_parameter.test.yml | 2 +- tests/endpoint/clop_ransomware_known_service_name.test.yml | 4 ++-- .../endpoint/create_service_in_suspicious_file_path.test.yml | 4 ++-- tests/endpoint/high_file_deletion_frequency.test.yml | 2 +- tests/endpoint/high_process_termination_frequency_.test.yml | 2 +- .../endpoint/process_deleting_its_process_file_path.test.yml | 2 +- tests/endpoint/ransomware_notes_bulk_creation.test.yml | 2 +- tests/endpoint/resize_shadowstorage_volume.test.yml | 2 +- 8 files changed, 10 insertions(+), 10 deletions(-) diff --git a/tests/endpoint/clop_common_exec_parameter.test.yml b/tests/endpoint/clop_common_exec_parameter.test.yml index 7334f1f4be..07ed9772e4 100644 --- a/tests/endpoint/clop_common_exec_parameter.test.yml +++ b/tests/endpoint/clop_common_exec_parameter.test.yml @@ -1,7 +1,7 @@ name: Clop Common Exec Parameter Unit Test tests: - name: Clop Common Exec Parameter - file: detections/endpoint/clop_ransomware_common_exec_parameter.yml + file: endpoint/clop_ransomware_common_exec_parameter.yml pass_condition: '| stats count | where count > 0' earliest_time: '-24h' latest_time: 'now' diff --git a/tests/endpoint/clop_ransomware_known_service_name.test.yml b/tests/endpoint/clop_ransomware_known_service_name.test.yml index d43dd7dbde..31e11ab66b 100644 --- a/tests/endpoint/clop_ransomware_known_service_name.test.yml +++ b/tests/endpoint/clop_ransomware_known_service_name.test.yml @@ -1,12 +1,12 @@ name: Clop Ransomware Known Service Name Unit Test tests: - name: Clop Ransomware Known Service Name - file: detections/endpoint/clop_ransomware_known_service_name.yml + file: endpoint/clop_ransomware_known_service_name.yml pass_condition: '| stats count | where count > 0' earliest_time: '-24h' latest_time: 'now' attack_data: - file_name: windows-system.log - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_service_simulated/windows-system.log + data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-system.log source: WinEventLog:System sourcetype: WinEventLog \ No newline at end of file diff --git a/tests/endpoint/create_service_in_suspicious_file_path.test.yml b/tests/endpoint/create_service_in_suspicious_file_path.test.yml index b5bbf55ef7..eba6df52b3 100644 --- a/tests/endpoint/create_service_in_suspicious_file_path.test.yml +++ b/tests/endpoint/create_service_in_suspicious_file_path.test.yml @@ -1,12 +1,12 @@ name: Create Service In Suspicious File Path Unit Test tests: - name: Create Service In Suspicious File Path - file: detections/endpoint/create_service_in_suspicious_file_path.yml + file: endpoint/create_service_in_suspicious_file_path.yml pass_condition: '| stats count | where count > 0' earliest_time: '-24h' latest_time: 'now' attack_data: - file_name: windows-system.log - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_service_simulated/windows-system.log + data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-system.log source: WinEventLog:System sourcetype: WinEventLog \ No newline at end of file diff --git a/tests/endpoint/high_file_deletion_frequency.test.yml b/tests/endpoint/high_file_deletion_frequency.test.yml index 51a7b94a06..0e569e5830 100644 --- a/tests/endpoint/high_file_deletion_frequency.test.yml +++ b/tests/endpoint/high_file_deletion_frequency.test.yml @@ -1,7 +1,7 @@ name: High File Deletion Frequency Unit Test tests: - name: High File Deletion Frequency - file: detections/endpoint/high_file_deletion_frequency.yml + file: endpoint/high_file_deletion_frequency.yml pass_condition: '| stats count | where count > 0' earliest_time: '-24h' latest_time: 'now' diff --git a/tests/endpoint/high_process_termination_frequency_.test.yml b/tests/endpoint/high_process_termination_frequency_.test.yml index 6638357205..0337abc110 100644 --- a/tests/endpoint/high_process_termination_frequency_.test.yml +++ b/tests/endpoint/high_process_termination_frequency_.test.yml @@ -1,7 +1,7 @@ name: High Process Termination Frequency Unit Test tests: - name: High Process Termination Frequency - file: detections/endpoint/high_process_termination_frequency_.yml + file: endpoint/high_process_termination_frequency_.yml pass_condition: '| stats count | where count > 0' earliest_time: '-24h' latest_time: 'now' diff --git a/tests/endpoint/process_deleting_its_process_file_path.test.yml b/tests/endpoint/process_deleting_its_process_file_path.test.yml index 1364504d5a..52c2694147 100644 --- a/tests/endpoint/process_deleting_its_process_file_path.test.yml +++ b/tests/endpoint/process_deleting_its_process_file_path.test.yml @@ -1,7 +1,7 @@ name: Process Deleting Its Process File Path Unit Test tests: - name: Process Deleting Its Process File Path - file: detections/endpoint/process_deleting_its_process_file_path.yml + file: endpoint/process_deleting_its_process_file_path.yml pass_condition: '| stats count | where count > 0' earliest_time: '-24h' latest_time: 'now' diff --git a/tests/endpoint/ransomware_notes_bulk_creation.test.yml b/tests/endpoint/ransomware_notes_bulk_creation.test.yml index 83e084f8d4..6be797d3fd 100644 --- a/tests/endpoint/ransomware_notes_bulk_creation.test.yml +++ b/tests/endpoint/ransomware_notes_bulk_creation.test.yml @@ -1,7 +1,7 @@ name: Ransomware Notes bulk creation Unit Test tests: - name: Ransomware Notes bulk creation - file: detections/endpoint/ransomware_notes_bulk_creation.yml + file: endpoint/ransomware_notes_bulk_creation.yml pass_condition: '| stats count | where count > 0' earliest_time: '-24h' latest_time: 'now' diff --git a/tests/endpoint/resize_shadowstorage_volume.test.yml b/tests/endpoint/resize_shadowstorage_volume.test.yml index d4c7a5571c..d2460b16a0 100644 --- a/tests/endpoint/resize_shadowstorage_volume.test.yml +++ b/tests/endpoint/resize_shadowstorage_volume.test.yml @@ -1,7 +1,7 @@ name: Resize ShadowStorage volume Unit Test tests: - name: Resize ShadowStorage volume - file: detections/endpoint/resize_shadowstorage_volume.yml + file: endpoint/resize_shadowstorage_volume.yml pass_condition: '| stats count | where count > 0' earliest_time: '-24h' latest_time: 'now' From ba334521392d2143187751772ed8b449b53c97fc Mon Sep 17 00:00:00 2001 From: tcontreras Date: Mon, 22 Mar 2021 11:53:18 +0100 Subject: [PATCH 34/62] clop_test_file --- tests/endpoint/clop_common_exec_parameter.test.yml | 2 +- ...cy_.test.yml => high_process_termination_frequency.test.yml} | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) rename tests/endpoint/{high_process_termination_frequency_.test.yml => high_process_termination_frequency.test.yml} (90%) diff --git a/tests/endpoint/clop_common_exec_parameter.test.yml b/tests/endpoint/clop_common_exec_parameter.test.yml index 07ed9772e4..23bc2d1ec6 100644 --- a/tests/endpoint/clop_common_exec_parameter.test.yml +++ b/tests/endpoint/clop_common_exec_parameter.test.yml @@ -1,7 +1,7 @@ name: Clop Common Exec Parameter Unit Test tests: - name: Clop Common Exec Parameter - file: endpoint/clop_ransomware_common_exec_parameter.yml + file: endpoint/clop_common_exec_parameter.yml pass_condition: '| stats count | where count > 0' earliest_time: '-24h' latest_time: 'now' diff --git a/tests/endpoint/high_process_termination_frequency_.test.yml b/tests/endpoint/high_process_termination_frequency.test.yml similarity index 90% rename from tests/endpoint/high_process_termination_frequency_.test.yml rename to tests/endpoint/high_process_termination_frequency.test.yml index 0337abc110..d7e4022398 100644 --- a/tests/endpoint/high_process_termination_frequency_.test.yml +++ b/tests/endpoint/high_process_termination_frequency.test.yml @@ -1,4 +1,4 @@ -name: High Process Termination Frequency Unit Test +name: High Process Termination Frequency Unit Test tests: - name: High Process Termination Frequency file: endpoint/high_process_termination_frequency_.yml From 08a0b64eb6f195f6b6cbf0468dbc2b8993a61e4d Mon Sep 17 00:00:00 2001 From: tcontreras Date: Mon, 22 Mar 2021 11:57:42 +0100 Subject: [PATCH 35/62] clop_test_file --- tests/endpoint/high_process_termination_frequency.test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/endpoint/high_process_termination_frequency.test.yml b/tests/endpoint/high_process_termination_frequency.test.yml index d7e4022398..8e9e14a92a 100644 --- a/tests/endpoint/high_process_termination_frequency.test.yml +++ b/tests/endpoint/high_process_termination_frequency.test.yml @@ -1,7 +1,7 @@ name: High Process Termination Frequency Unit Test tests: - name: High Process Termination Frequency - file: endpoint/high_process_termination_frequency_.yml + file: endpoint/high_process_termination_frequency.yml pass_condition: '| stats count | where count > 0' earliest_time: '-24h' latest_time: 'now' From 6fa9ea2c0b4ae85215dde4ffb5980667b2921812 Mon Sep 17 00:00:00 2001 From: tcontreras Date: Mon, 22 Mar 2021 12:05:41 +0100 Subject: [PATCH 36/62] clop_detections --- .../endpoint/resize_shadowstorage_volume.yml | 27 +++++++++---------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/detections/endpoint/resize_shadowstorage_volume.yml b/detections/endpoint/resize_shadowstorage_volume.yml index 05d43956f2..fe701d08df 100644 --- a/detections/endpoint/resize_shadowstorage_volume.yml +++ b/detections/endpoint/resize_shadowstorage_volume.yml @@ -7,21 +7,20 @@ type: batch datamodel: - Endpoint description: The following analytics identifies the resizing of shadowstorage by ransomware malware to avoid the shadow volumes being made again. - this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. - - one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum - size as much as possible + this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. + one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum + size as much as possible search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline - values(Processes.parent_process_name) as parent_process - values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell.exe" OR - Processes.parent_process_name = "powershell_ise.exe" OR - Processes.parent_process_name = "wmic.exe" - Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" - by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user - | `security_content_ctime(firstTime)` - |`security_content_ctime(lastTime)` - | `resize_shadowstorage_volume_filter`' + values(Processes.parent_process_name) as parent_process + values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell.exe" OR + Processes.parent_process_name = "powershell_ise.exe" OR + Processes.parent_process_name = "wmic.exe" + Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" + by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user + | `security_content_ctime(firstTime)` + |`security_content_ctime(lastTime)` + | `resize_shadowstorage_volume_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the From 7412098442a2f869132d772208fbc726ff12c418 Mon Sep 17 00:00:00 2001 From: root Date: Mon, 22 Mar 2021 12:36:11 +0000 Subject: [PATCH 37/62] Added detection testing service results inClop Common Exec Parameter --- .../endpoint/clop_common_exec_parameter.yml | 31 ++++++++++--------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/detections/endpoint/clop_common_exec_parameter.yml b/detections/endpoint/clop_common_exec_parameter.yml index 96be25d960..71c8ae2eff 100644 --- a/detections/endpoint/clop_common_exec_parameter.yml +++ b/detections/endpoint/clop_common_exec_parameter.yml @@ -6,20 +6,19 @@ author: Teoderick Contreras, Splunk type: batch datamodel: - Endpoint -description: The following analytics are designed to identifies some CLOP ransomware variant that using arguments - to execute its main code or feature of its code. In this variant if the parameter is "runrun", - CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", - it will try to read from some stream pipe or file start encrypting files within the infected local machines. - This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is - waiting for some parameter to execute properly. +description: The following analytics are designed to identifies some CLOP ransomware + variant that using arguments to execute its main code or feature of its code. In + this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files + in network shares and if it is "temp.dat", it will try to read from some stream + pipe or file start encrypting files within the infected local machines. This technique + can be also identified as an anti-sandbox technique to make its code non-responsive + since it is waiting for some parameter to execute properly. search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline - values(Processes.parent_process_name) as parent_process - values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" - by Processes.parent_process_name Processes.process_name Processes.process Processes.dest Processes.user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `clop_common_exec_parameter_filter`' + values(Processes.parent_process_name) as parent_process values(Processes.process_name) + count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" by Processes.parent_process_name + Processes.process_name Processes.process Processes.dest Processes.user | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `clop_common_exec_parameter_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the @@ -31,7 +30,7 @@ references: tags: analytic_story: - Clop Ransomware - automated_detection_testing: tba + automated_detection_testing: passed kill_chain_phases: - Obfuscation mitre_attack_id: @@ -46,5 +45,7 @@ tags: - _time - Processes.process_name - Processes.dest - - Processes.user + - Processes.user security_domain: endpoint + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log From 7c09935e1e611bcf0de173538c4ec83baf668383 Mon Sep 17 00:00:00 2001 From: tcontreras Date: Mon, 22 Mar 2021 14:22:08 +0100 Subject: [PATCH 38/62] clop_detections --- detections/endpoint/clop_ransomware_known_service_name.yml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/detections/endpoint/clop_ransomware_known_service_name.yml b/detections/endpoint/clop_ransomware_known_service_name.yml index 6f70f48c4a..86fc45bfee 100644 --- a/detections/endpoint/clop_ransomware_known_service_name.yml +++ b/detections/endpoint/clop_ransomware_known_service_name.yml @@ -8,9 +8,8 @@ datamodel: - Endpoint description: This detection is to identify the common service name created by the CLOP ransomware as part of its persistence and high privilege code execution in the infected machine. -search: '`sysmon`EventCode=1 cmdline IN ("*runrun*","*temp.dat*") - | stats count min(_time) as firstTime max(_time) as lastTime count - by Computer User EventCode parent_process_name process_name OriginalFileName process_path cmdline +search: '`wineventlog_system` EventCode=7045 Service_Name IN ("SecurityCenterIBM", "WinCheckDRVs") + | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_ransomware_known_service_name_filter`' From 61fa09ff420d31fca407c31bcc819f8e38137b4f Mon Sep 17 00:00:00 2001 From: root Date: Mon, 22 Mar 2021 15:01:00 +0000 Subject: [PATCH 39/62] Added detection testing service results inRansomware Notes bulk creation --- .../ransomware_notes_bulk_creation.yml | 30 +++++++++---------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/detections/endpoint/ransomware_notes_bulk_creation.yml b/detections/endpoint/ransomware_notes_bulk_creation.yml index 7bf3e1e3a0..f426f36ccc 100644 --- a/detections/endpoint/ransomware_notes_bulk_creation.yml +++ b/detections/endpoint/ransomware_notes_bulk_creation.yml @@ -6,28 +6,26 @@ author: Teoderick Contreras type: batch datamodel: - Endpoint -description: The following analytics identifies a big number of instance of ransomware notes (filetype e.g .txt, .html, .hta) - file creation to the infected machine. This behavior is a good sensor if the ransomware note filename is quite new for security industry - or the ransomware note filename is not in your lookup table list for monitoring. - -search: '`sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") - | stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path - by Computer Image file_name - | where unique_readme_path_count >= 50 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` +description: The following analytics identifies a big number of instance of ransomware + notes (filetype e.g .txt, .html, .hta) file creation to the infected machine. This + behavior is a good sensor if the ransomware note filename is quite new for security + industry or the ransomware note filename is not in your lookup table list for monitoring. +search: '`sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") | stats + min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count + values(TargetFilename) as list_of_readme_path by Computer Image file_name | where + unique_readme_path_count >= 50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ransomware_notes_bulk_creation_filter`' -how_to_implement: "You must be ingesting data that records the filesystem activity +how_to_implement: You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which - you want to collect data." + you want to collect data. known_false_positives: unknown references: - https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html tags: analytic_story: - - Clop Ransomware + - Clop Ransomware kill_chain_phases: - Obfuscation mitre_attack_id: @@ -44,5 +42,7 @@ tags: - Computer - Image - user - security_domain: endpoint - \ No newline at end of file + security_domain: endpoint + automated_detection_testing: passed + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log From f2284c0090ee06facb4d24b0a1b6584f891af8f3 Mon Sep 17 00:00:00 2001 From: tcontreras Date: Mon, 22 Mar 2021 16:26:31 +0100 Subject: [PATCH 40/62] clop_detection_svc --- detections/endpoint/clop_ransomware_known_service_name.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detections/endpoint/clop_ransomware_known_service_name.yml b/detections/endpoint/clop_ransomware_known_service_name.yml index 86fc45bfee..d5b63cd205 100644 --- a/detections/endpoint/clop_ransomware_known_service_name.yml +++ b/detections/endpoint/clop_ransomware_known_service_name.yml @@ -7,7 +7,7 @@ type: batch datamodel: - Endpoint description: This detection is to identify the common service name created by the CLOP ransomware as part of its persistence - and high privilege code execution in the infected machine. + and high privilege code execution in the infected machine. Ussually CLOP ransomware use StartServiceCtrlDispatcherW API in creating this service entry. search: '`wineventlog_system` EventCode=7045 Service_Name IN ("SecurityCenterIBM", "WinCheckDRVs") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `security_content_ctime(firstTime)` From 52e5b1c6c2354fe2b410023f16fceeb30bb32240 Mon Sep 17 00:00:00 2001 From: root Date: Mon, 22 Mar 2021 17:08:42 +0000 Subject: [PATCH 41/62] Added detection testing service results inResize ShadowStorage volume --- .../endpoint/resize_shadowstorage_volume.yml | 37 ++++++++++--------- 1 file changed, 20 insertions(+), 17 deletions(-) diff --git a/detections/endpoint/resize_shadowstorage_volume.yml b/detections/endpoint/resize_shadowstorage_volume.yml index fe701d08df..6a868489c2 100644 --- a/detections/endpoint/resize_shadowstorage_volume.yml +++ b/detections/endpoint/resize_shadowstorage_volume.yml @@ -6,20 +6,21 @@ author: Teoderick Contreras type: batch datamodel: - Endpoint -description: The following analytics identifies the resizing of shadowstorage by ransomware malware to avoid the shadow volumes being made again. - this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. - one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum - size as much as possible +description: The following analytics identifies the resizing of shadowstorage by ransomware + malware to avoid the shadow volumes being made again. this technique is an alternative + by ransomware attacker than deleting the shadowstorage which is known alert in defensive + team. one example of ransomware that use this technique is CLOP ransomware where + it drops a .bat file that will resize the shadowstorage to minimum size as much + as possible search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline - values(Processes.parent_process_name) as parent_process - values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell.exe" OR - Processes.parent_process_name = "powershell_ise.exe" OR - Processes.parent_process_name = "wmic.exe" - Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" - by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user - | `security_content_ctime(firstTime)` - |`security_content_ctime(lastTime)` + values(Processes.parent_process_name) as parent_process values(Processes.process_name) + as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name + = "powershell.exe" OR Processes.parent_process_name = "powershell_ise.exe" OR Processes.parent_process_name + = "wmic.exe" Processes.process_name = "vssadmin.exe" Processes.process="*resize*" + Processes.process="*shadowstorage*" Processes.process="*/maxsize*" by Processes.parent_process_name + Processes.parent_process Processes.process_name Processes.process Processes.dest + Processes.user | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `resize_shadowstorage_volume_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your @@ -31,7 +32,7 @@ references: - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html tags: analytic_story: - - Clop Ransomware + - Clop Ransomware kill_chain_phases: - Exploitation mitre_attack_id: @@ -47,6 +48,8 @@ tags: - Processes.process_name - Processes.parent_process - Processes.dest - - Processes.user - security_domain: endpoint - \ No newline at end of file + - Processes.user + security_domain: endpoint + automated_detection_testing: passed + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log From 089e2c09d1fc0b95c6fc71c20acbf76dc9532baa Mon Sep 17 00:00:00 2001 From: root Date: Mon, 22 Mar 2021 17:43:09 +0000 Subject: [PATCH 42/62] Added detection testing service results inCreate Service In Suspicious File Path --- ...create_service_in_suspicious_file_path.yml | 23 +++++++++++-------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/detections/endpoint/create_service_in_suspicious_file_path.yml b/detections/endpoint/create_service_in_suspicious_file_path.yml index 5acda780b0..6787d6592d 100644 --- a/detections/endpoint/create_service_in_suspicious_file_path.yml +++ b/detections/endpoint/create_service_in_suspicious_file_path.yml @@ -6,16 +6,16 @@ author: Teoderick Contreras type: batch datamodel: - Endpoint -description: This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows. -search: ' `wineventlog_system` EventCode=7045 Service_File_Name = "*\.exe" - NOT (Service_File_Name IN ("C:\\Windows\\*", "C:\\Program File*", "C:\\Programdata\\*", "%systemroot%\\*")) - Service_Type = "user mode service" - | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `create_service_in_suspicious_file_path_filter`' +description: This detection is to identify a creation of "user mode service" where + the service file path is located in non-common service folder in windows. +search: ' `wineventlog_system` EventCode=7045 Service_File_Name = "*\.exe" NOT (Service_File_Name + IN ("C:\\Windows\\*", "C:\\Program File*", "C:\\Programdata\\*", "%systemroot%\\*")) + Service_Type = "user mode service" | stats count min(_time) as firstTime max(_time) + as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `create_service_in_suspicious_file_path_filter`' how_to_implement: To successfully implement this search, you need to be ingesting - logs with the Service name, Service File Name Service Start type, and Service Type from your - endpoints. + logs with the Service name, Service File Name Service Start type, and Service Type + from your endpoints. known_false_positives: unknown references: - https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html @@ -38,4 +38,7 @@ tags: - _time - Service_Name - Service_Start_Type - security_domain: endpoint \ No newline at end of file + security_domain: endpoint + automated_detection_testing: passed + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-system.log From 13ab17f2d18f42f55eca800ad209e15994f60c3a Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Mon, 22 Mar 2021 11:30:53 -0700 Subject: [PATCH 43/62] refactor test workflow with assertions and catch. Teardown as well --- .../modules/github_service.py | 3 - .../modules/test_ssa_detections.py | 116 +++++++++--------- bin/ssa-end-to-end-testing/modules/utils.py | 6 + 3 files changed, 64 insertions(+), 61 deletions(-) diff --git a/bin/ssa-end-to-end-testing/modules/github_service.py b/bin/ssa-end-to-end-testing/modules/github_service.py index 82ed3b8c15..09e13de335 100644 --- a/bin/ssa-end-to-end-testing/modules/github_service.py +++ b/bin/ssa-end-to-end-testing/modules/github_service.py @@ -2,8 +2,6 @@ import git import os import logging -from os import path -import sys # Logger @@ -25,7 +23,6 @@ class GithubService: repo_obj = git.Repo.clone_from(url, project, branch=branch) return repo_obj - def get_changed_test_files_ssa(self): branch1 = self.security_content_branch branch2 = 'develop' diff --git a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py index 126045831a..3b506247bb 100644 --- a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py +++ b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py @@ -1,4 +1,3 @@ - import logging import os import time @@ -6,8 +5,7 @@ import sys from http import HTTPStatus from modules.streams_service_api_helper import DSPApi -from modules.utils import manipulate_spl, read_spl, read_data - +from modules.utils import check_source_sink, manipulate_spl, read_spl, read_data # Logger logging.basicConfig(level=os.environ.get("LOGLEVEL", "INFO")) @@ -22,6 +20,7 @@ MAX_EXECUTION_TIME_LIMIT = 600 # per detection test TEST_DATASET = 'windows-security_small.txt' + class SSADetectionTesting: def __init__(self, env, tenant, header_token): @@ -45,7 +44,7 @@ class SSADetectionTesting: ] test_results = [] - for i in range(0,len(test_spls)): + for i in range(0, len(test_spls)): self.max_execution_time = MAX_EXECUTION_TIME_LIMIT test_result = self.ssa_detection_test(read_spl(file_path_spl, test_spls[i]), file_path_data, test_names[i]) test_results.append(test_result.copy()) @@ -62,17 +61,16 @@ class SSADetectionTesting: return passed - def test_ssa_detections(self, test_obj): LOGGER.info('Test SSA Detection: ' + test_obj["detection_obj"]["name"]) self.max_execution_time = MAX_EXECUTION_TIME_LIMIT file_path_attack_data = os.path.join(os.path.dirname(__file__), "../", test_obj["attack_data_file_path"]) - test_results = self.ssa_detection_test(test_obj["detection_obj"]["search"], file_path_attack_data, "SSA Smoke Test " + test_obj["test_obj"]["name"]) + test_results = self.ssa_detection_test(test_obj["detection_obj"]["search"], file_path_attack_data, + "SSA Smoke Test " + test_obj["test_obj"]["name"]) return test_results - ## Helper Functions ## def update_execution_time(self, time_frame): @@ -86,30 +84,12 @@ class SSADetectionTesting: time.sleep(time_in_s) return self.update_execution_time(time_in_s) - def check_result(self, condition, error_message): - try: - assert condition - except: - self.execution_passed = False - LOGGER.error(error_message) - - def write_test_results(self, test_name): - if not self.execution_passed: - msg = f"Detection test failed for {test_name}" - LOGGER.error(msg) - self.test_results["msg"] = msg - self.test_results["result"] = False - else: - msg = f"Detection test successful for {test_name}" - LOGGER.info(msg) - self.test_results["msg"] = msg - - def ssa_detection_test_init(self): self.test_results["result"] = True self.test_results["msg"] = "" - self.results_index = self.api.create_temp_index("mc") - + self.result_indexes = [self.api.create_temp_index("mc")] + self.created_pipelines = [] + self.activated_pipelines = [] def ssa_detection_test_main(self, spl, source, test_name): self.execution_passed = True @@ -117,27 +97,31 @@ class SSADetectionTesting: self.wait_time(SLEEP_TIME_CREATE_INDEX) spl = manipulate_spl(self.api.env, spl, self.results_index) - self.check_result(spl is not None, "fail to manipulate spl file") + assert spl is not None, "fail to manipulate spl file" pipeline_id = self.api.create_pipeline_from_spl(spl) - self.check_result(pipeline_id is not None, "failed to create a pipeline") + assert pipeline_id is not None, "failed to create a pipeline" _pipeline_status = self.api.pipeline_status(pipeline_id) - self.check_result(_pipeline_status=="CREATED", f"Current status of pipeline {pipeline_id} should be CREATED") + assert _pipeline_status == "CREATED", f"Current status of pipeline {pipeline_id} should be CREATED" + self.created_pipelines.append(pipeline_id) response_body = self.api.activate_pipeline(pipeline_id) - self.check_result(response_body.get("activated")==pipeline_id, f"pipeline {pipeline_id} should be successfully activate.") + assert response_body.get("activated") == pipeline_id, f"pipeline {pipeline_id} should be successfully activate." + self.activated_pipelines.append(pipeline_id) self.wait_time(SLEEP_TIME_ACTIVATE_PIPELINE) + if not check_source_sink(spl): + msg = f"Detection test successful for {test_name}" + LOGGER.warning(f"Test not completed. Detection seems deprecated, and will not send messages to SSA") + self.test_results["msg"] = msg + return self.test_results + data = read_data(source) LOGGER.info("Sending (%d) events" % (len(data))) - if len(data) == 0: - LOGGER.warning("No events to send, skip to next test.") - self.execution_passed = False - self.write_test_results(test_name) - return self.test_results + assert len(data) > 0, "No events to send, skip to next test." for d in data: response_body = self.api.ingest_data(d) @@ -151,8 +135,8 @@ class SSADetectionTesting: max_execution_time_reached = self.wait_time(WAIT_CYCLE) query = f"from indexes('{self.results_index['name']}') | search source!=\"Search Catalog\" " sid = self.api.submit_search_job(self.results_index['module'], query) - self.check_result(sid is not None, f"Failed to create a Search Job") - + assert sid is not None, f"Failed to create a Search Job" + job_finished = False while not job_finished: self.wait_time(WAIT_CYCLE) @@ -162,33 +146,49 @@ class SSADetectionTesting: results = self.api.get_search_job_results(sid) search_results = (len(results) > 0) if not search_results: - LOGGER.info(f"Search didn't return any results. Retrying in {WAIT_CYCLE}s, max execution time left {self.max_execution_time}s") - - self.check_result(len(results) > 0, "Search job didn't return any results") + LOGGER.info( + f"Search didn't return any results. Retrying in {WAIT_CYCLE}s, max execution time left {self.max_execution_time}s") - response, response_body = self.api.deactivate_pipeline(pipeline_id) - self.check_result(response.status_code == HTTPStatus.OK, f"The pipeline {pipeline_id} fails to deactivated.") + assert len(results) > 0, "Search job didn't return any results" - response = self.api.delete_pipeline(pipeline_id) - self.check_result(response.status_code == HTTPStatus.NO_CONTENT, f"Fail to delete pipeline {pipeline_id}.") - - self.write_test_results(test_name) + msg = f"Detection test successful for {test_name}" + LOGGER.info(msg) + self.test_results["msg"] = msg return self.test_results - def ssa_detection_test_teardown(self): - pass - self.api.delete_temp_index(self.results_index["id"]) - + """ + Deactivate and deletes pipelines, deletes results indexes, + and when it fails it shows pipelines and result indexes that were not removed. + :return: + None + """ + deactivate_pipeline = lambda p: self.api.deactivate_pipeline(p).status_code == HTTPStatus.OK + delete_pipeline = lambda p: self.api.delete_pipeline(p).status_code == HTTPStatus.NO_CONTENT + delete_index = lambda p: self.api.delete_temp_index(self.results_index["id"]) == HTTPStatus.NO_CONTENT + self.activated_pipelines = [p for p in self.activated_pipelines if not deactivate_pipeline(p)] + self.created_pipelines = [p for p in self.created_pipelines if not delete_pipeline(p)] + self.result_indexes = [p for p in self.result_indexes if not delete_index] + if len(self.activated_pipelines) > 0 or len(self.created_pipelines) > 0 or len(self.result_indexes) > 0: + LOGGER.warning("Not all SCS resources fred up") + LOGGER.info(f"Created Pipelines: {','.join(self.created_pipelines)}") + LOGGER.info(f"Active Pipelines: {','.join(self.activated_pipelines)}") + LOGGER.info(f"Result Indexes: {','.join(self.result_indexes)}") + else: + LOGGER.info("Testing successfully cleaned up") def ssa_detection_test(self, spl, source, test_name): self.ssa_detection_test_init() - test_result = self.ssa_detection_test_main(spl, source, test_name) - self.ssa_detection_test_teardown() - return test_result - - + try: + test_result = self.ssa_detection_test_main(spl, source, test_name) + self.ssa_detection_test_teardown() + return test_result + except AssertionError as e: + self.ssa_detection_test_teardown() + LOGGER.error(e.args[0]) + return {"result": False, + "msg": f"Detection test failure for {test_name}"} # only for troubleshooting # def ssa_detection_in_dsp_with_preview_session(self, spl, source, test_name): @@ -213,4 +213,4 @@ class SSADetectionTesting: # response = self.api.stop_preview_session(preview_id) - # self.write_test_results(test_name) \ No newline at end of file + # self.write_test_results(test_name) diff --git a/bin/ssa-end-to-end-testing/modules/utils.py b/bin/ssa-end-to-end-testing/modules/utils.py index 8548d0be47..7830901892 100644 --- a/bin/ssa-end-to-end-testing/modules/utils.py +++ b/bin/ssa-end-to-end-testing/modules/utils.py @@ -75,6 +75,12 @@ def request_headers(header_token): return headers +def check_source_sink(spl): + match_source = re.match(r"^\s*\|\s+from\s+read_ssa_enriched_events\(\s*\)", spl) + match_sink = re.match(r".*into\s+write_ssa_detected_events\(\s*\)\s*;", spl) + return match_source and match_sink + + def manipulate_spl(env, spl, results_index): # Obtain the SSA source pulsar_source_connection_id, pulsar_source_topic = return_macros(env) From d6d02eb6fcfdbfab1988fff8e1bc88683c621d08 Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Mon, 22 Mar 2021 11:56:08 -0700 Subject: [PATCH 44/62] removing index fixed --- .../modules/test_ssa_detections.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py index 3b506247bb..fa6270c46f 100644 --- a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py +++ b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py @@ -87,7 +87,7 @@ class SSADetectionTesting: def ssa_detection_test_init(self): self.test_results["result"] = True self.test_results["msg"] = "" - self.result_indexes = [self.api.create_temp_index("mc")] + self.results_index = self.api.create_temp_index("mc") self.created_pipelines = [] self.activated_pipelines = [] @@ -166,15 +166,14 @@ class SSADetectionTesting: """ deactivate_pipeline = lambda p: self.api.deactivate_pipeline(p).status_code == HTTPStatus.OK delete_pipeline = lambda p: self.api.delete_pipeline(p).status_code == HTTPStatus.NO_CONTENT - delete_index = lambda p: self.api.delete_temp_index(self.results_index["id"]) == HTTPStatus.NO_CONTENT + delete_index = lambda p: self.api.delete_temp_index(p["id"]) == HTTPStatus.NO_CONTENT self.activated_pipelines = [p for p in self.activated_pipelines if not deactivate_pipeline(p)] self.created_pipelines = [p for p in self.created_pipelines if not delete_pipeline(p)] - self.result_indexes = [p for p in self.result_indexes if not delete_index] - if len(self.activated_pipelines) > 0 or len(self.created_pipelines) > 0 or len(self.result_indexes) > 0: + if len(self.activated_pipelines) > 0 or len(self.created_pipelines) > 0 or not delete_index(self.results_index): LOGGER.warning("Not all SCS resources fred up") LOGGER.info(f"Created Pipelines: {','.join(self.created_pipelines)}") LOGGER.info(f"Active Pipelines: {','.join(self.activated_pipelines)}") - LOGGER.info(f"Result Indexes: {','.join(self.result_indexes)}") + LOGGER.info(f"Result Indexes: {self.results_index}") else: LOGGER.info("Testing successfully cleaned up") From 61d2dbb0e84d63b6f643982372bfa92401189571 Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Mon, 22 Mar 2021 12:44:49 -0700 Subject: [PATCH 45/62] api queries return tuples --- bin/ssa-end-to-end-testing/modules/test_ssa_detections.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py index fa6270c46f..61cfdce563 100644 --- a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py +++ b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py @@ -164,9 +164,9 @@ class SSADetectionTesting: :return: None """ - deactivate_pipeline = lambda p: self.api.deactivate_pipeline(p).status_code == HTTPStatus.OK - delete_pipeline = lambda p: self.api.delete_pipeline(p).status_code == HTTPStatus.NO_CONTENT - delete_index = lambda p: self.api.delete_temp_index(p["id"]) == HTTPStatus.NO_CONTENT + deactivate_pipeline = lambda p: self.api.deactivate_pipeline(p)[0].status_code == HTTPStatus.OK + delete_pipeline = lambda p: self.api.delete_pipeline(p)[0].status_code == HTTPStatus.NO_CONTENT + delete_index = lambda p: self.api.delete_temp_index(p["id"])[0] == HTTPStatus.NO_CONTENT self.activated_pipelines = [p for p in self.activated_pipelines if not deactivate_pipeline(p)] self.created_pipelines = [p for p in self.created_pipelines if not delete_pipeline(p)] if len(self.activated_pipelines) > 0 or len(self.created_pipelines) > 0 or not delete_index(self.results_index): From 641fa24fb33d7e570844d4f17fbf2854d5d0c01c Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Mon, 22 Mar 2021 12:56:35 -0700 Subject: [PATCH 46/62] matching api outputs --- bin/ssa-end-to-end-testing/modules/test_ssa_detections.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py index 61cfdce563..84bc48b59c 100644 --- a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py +++ b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py @@ -165,8 +165,8 @@ class SSADetectionTesting: None """ deactivate_pipeline = lambda p: self.api.deactivate_pipeline(p)[0].status_code == HTTPStatus.OK - delete_pipeline = lambda p: self.api.delete_pipeline(p)[0].status_code == HTTPStatus.NO_CONTENT - delete_index = lambda p: self.api.delete_temp_index(p["id"])[0] == HTTPStatus.NO_CONTENT + delete_pipeline = lambda p: self.api.delete_pipeline(p).status_code == HTTPStatus.NO_CONTENT + delete_index = lambda p: self.api.delete_temp_index(p["id"]) == HTTPStatus.NO_CONTENT self.activated_pipelines = [p for p in self.activated_pipelines if not deactivate_pipeline(p)] self.created_pipelines = [p for p in self.created_pipelines if not delete_pipeline(p)] if len(self.activated_pipelines) > 0 or len(self.created_pipelines) > 0 or not delete_index(self.results_index): From ec2ba3450ab29230d35a99bbffd51f1587163d9a Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Mon, 22 Mar 2021 14:16:21 -0700 Subject: [PATCH 47/62] fix check source/sink --- bin/ssa-end-to-end-testing/modules/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bin/ssa-end-to-end-testing/modules/utils.py b/bin/ssa-end-to-end-testing/modules/utils.py index 7830901892..13424416f6 100644 --- a/bin/ssa-end-to-end-testing/modules/utils.py +++ b/bin/ssa-end-to-end-testing/modules/utils.py @@ -77,7 +77,7 @@ def request_headers(header_token): def check_source_sink(spl): match_source = re.match(r"^\s*\|\s+from\s+read_ssa_enriched_events\(\s*\)", spl) - match_sink = re.match(r".*into\s+write_ssa_detected_events\(\s*\)\s*;", spl) + match_sink = re.search(r"\|\s*into\s+write_ssa_detected_events\(\s*\)\s*;", spl) return match_source and match_sink From 1b76734dd381121104ecd0bbda1c5ac9ec5aa1d6 Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Mon, 22 Mar 2021 14:42:16 -0700 Subject: [PATCH 48/62] check before modifications --- bin/ssa-end-to-end-testing/modules/test_ssa_detections.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py index 84bc48b59c..0747c8856b 100644 --- a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py +++ b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py @@ -96,6 +96,7 @@ class SSADetectionTesting: self.wait_time(SLEEP_TIME_CREATE_INDEX) + check_ssa_spl = check_source_sink(spl) spl = manipulate_spl(self.api.env, spl, self.results_index) assert spl is not None, "fail to manipulate spl file" @@ -112,7 +113,7 @@ class SSADetectionTesting: self.wait_time(SLEEP_TIME_ACTIVATE_PIPELINE) - if not check_source_sink(spl): + if not check_ssa_spl: msg = f"Detection test successful for {test_name}" LOGGER.warning(f"Test not completed. Detection seems deprecated, and will not send messages to SSA") self.test_results["msg"] = msg From f224e3865ee4401cc32d2b7b956a6dc205f6724e Mon Sep 17 00:00:00 2001 From: divious1 Date: Mon, 22 Mar 2021 18:02:35 -0400 Subject: [PATCH 49/62] removing unecessary import --- bin/doc_gen.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/bin/doc_gen.py b/bin/doc_gen.py index 8c951e9015..e19570eba1 100644 --- a/bin/doc_gen.py +++ b/bin/doc_gen.py @@ -5,9 +5,7 @@ import sys import re from os import path, walk import json -import jsonschema2md from jinja2 import Environment, FileSystemLoader -from attackcti import attack_client from pyattck import Attck From 2a5ad289a609c4cb73d478aab4650ba20021bd69 Mon Sep 17 00:00:00 2001 From: divious1 Date: Mon, 22 Mar 2021 19:02:47 -0400 Subject: [PATCH 50/62] added npm jsonchema2md --- .circleci/config.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index 9fc864945f..873f29d0a7 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -87,6 +87,10 @@ jobs: cd security-content source venv/bin/activate python bin/doc_gen.py --path . --output docs -v + # now generate spec docs + sudo apt-get install -y npm -qq + sudo npm install -g @adobe/jsonschema2md + jsonschema2md -d spec -o docs/spec -f yaml -e spec.json build-sources: executor: content-executor From adc4aa9908dda5695c8774ac86b45dc318188846 Mon Sep 17 00:00:00 2001 From: Ignacio Bermudez Corrales Date: Mon, 22 Mar 2021 16:09:20 -0700 Subject: [PATCH 51/62] tear down on any sort of error (not just assertions) --- bin/ssa-end-to-end-testing/modules/test_ssa_detections.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py index 0747c8856b..d9c323c189 100644 --- a/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py +++ b/bin/ssa-end-to-end-testing/modules/test_ssa_detections.py @@ -189,6 +189,11 @@ class SSADetectionTesting: LOGGER.error(e.args[0]) return {"result": False, "msg": f"Detection test failure for {test_name}"} + except Exception as e: + self.ssa_detection_test_teardown() + LOGGER.error(e) + return {"result": False, + "msg": f"Detection test failure for {test_name} (perhaps SCS problems)"} # only for troubleshooting # def ssa_detection_in_dsp_with_preview_session(self, spl, source, test_name): From cce49543b0985ed6ff80b2ad22c123ba42cfb685 Mon Sep 17 00:00:00 2001 From: divious1 Date: Mon, 22 Mar 2021 19:12:45 -0400 Subject: [PATCH 52/62] added logic to generate docs --- .circleci/config.yml | 4 +- docs/spec/baselines-properties-author.md | 22 --- .../baselines-properties-datamodel-items.md | 36 ----- docs/spec/baselines-properties-datamodel.md | 22 --- docs/spec/baselines-properties-date.md | 22 --- docs/spec/baselines-properties-description.md | 26 --- .../baselines-properties-how_to_implement.md | 24 --- docs/spec/baselines-properties-id.md | 22 --- .../baselines-properties-name-of-baseline.md | 22 --- docs/spec/baselines-properties-search.md | 24 --- .../spec/baselines-properties-tags-default.md | 15 -- docs/spec/baselines-properties-tags.md | 47 ------ docs/spec/baselines-properties-version.md | 22 --- docs/spec/baselines.md | 6 +- docs/spec/deployments-default.md | 15 -- ...oyments-properties-alert_action-default.md | 15 -- ...s-alert_action-properties-email-default.md | 15 -- ...ion-properties-email-properties-message.md | 22 --- ...ion-properties-email-properties-subject.md | 22 --- ...t_action-properties-email-properties-to.md | 22 --- ...roperties-alert_action-properties-email.md | 120 -------------- ...s-alert_action-properties-index-default.md | 15 -- ...action-properties-index-properties-name.md | 22 --- ...roperties-alert_action-properties-index.md | 66 -------- ...alert_action-properties-notable-default.md | 15 -- ...ies-notable-properties-rule_description.md | 22 --- ...roperties-notable-properties-rule_title.md | 22 --- ...perties-alert_action-properties-notable.md | 93 ----------- .../deployments-properties-alert_action.md | 153 ------------------ ...ployments-properties-scheduling-default.md | 15 -- ...ies-scheduling-properties-cron_schedule.md | 22 --- ...ies-scheduling-properties-earliest_time.md | 22 --- ...rties-scheduling-properties-latest_time.md | 22 --- ...s-scheduling-properties-schedule_window.md | 22 --- .../spec/deployments-properties-scheduling.md | 147 ----------------- docs/spec/deployments.md | 6 +- ...ctions-properties-known_false_positives.md | 24 --- ...-properties-references-the-items-schema.md | 23 --- docs/spec/detections-properties-references.md | 31 ---- docs/spec/detections-properties-type-items.md | 24 --- docs/spec/detections-properties-type.md | 22 --- docs/spec/detections.md | 6 +- docs/spec/lookups-oneof-0.md | 15 -- docs/spec/lookups-oneof-1.md | 15 -- ...lookups-properties-case_sensitive_match.md | 31 ---- docs/spec/lookups-properties-collection.md | 22 --- docs/spec/lookups-properties-default_match.md | 22 --- docs/spec/lookups-properties-description.md | 22 --- docs/spec/lookups-properties-fields_list.md | 22 --- docs/spec/lookups-properties-filename.md | 22 --- docs/spec/lookups-properties-filter.md | 22 --- docs/spec/lookups-properties-match_type.md | 22 --- docs/spec/lookups-properties-max_matches.md | 22 --- docs/spec/lookups-properties-min_matches.md | 22 --- docs/spec/lookups-properties-name.md | 22 --- docs/spec/lookups.md | 6 +- .../spec/macros-properties-arguments-items.md | 15 -- docs/spec/macros-properties-arguments.md | 21 --- docs/spec/macros-properties-definition.md | 22 --- docs/spec/macros-properties-description.md | 22 --- docs/spec/macros-properties-name.md | 22 --- docs/spec/macros.md | 6 +- docs/spec/response_tasks-default.md | 15 -- ...nse_tasks-properties-automation-default.md | 15 -- .../response_tasks-properties-automation.md | 62 ------- docs/spec/response_tasks-properties-sla.md | 27 ---- .../response_tasks-properties-sla_type.md | 40 ----- docs/spec/response_tasks.md | 6 +- docs/spec/responses-default.md | 15 -- .../responses-properties-is_note_required.md | 27 ---- ...onses-properties-response_phase-default.md | 15 -- .../responses-properties-response_phase.md | 51 ------ docs/spec/responses.md | 6 +- docs/spec/responses_phase-default.md | 15 -- ..._phase-properties-response_task-default.md | 15 -- ...esponses_phase-properties-response_task.md | 57 ------- docs/spec/responses_phase.md | 6 +- docs/spec/stories-default.md | 15 -- docs/spec/stories-properties-narrative.md | 26 --- docs/spec/stories.md | 6 +- 80 files changed, 30 insertions(+), 2123 deletions(-) delete mode 100644 docs/spec/baselines-properties-author.md delete mode 100644 docs/spec/baselines-properties-datamodel-items.md delete mode 100644 docs/spec/baselines-properties-datamodel.md delete mode 100644 docs/spec/baselines-properties-date.md delete mode 100644 docs/spec/baselines-properties-description.md delete mode 100644 docs/spec/baselines-properties-how_to_implement.md delete mode 100644 docs/spec/baselines-properties-id.md delete mode 100644 docs/spec/baselines-properties-name-of-baseline.md delete mode 100644 docs/spec/baselines-properties-search.md delete mode 100644 docs/spec/baselines-properties-tags-default.md delete mode 100644 docs/spec/baselines-properties-tags.md delete mode 100644 docs/spec/baselines-properties-version.md delete mode 100644 docs/spec/deployments-default.md delete mode 100644 docs/spec/deployments-properties-alert_action-default.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-email-default.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-email-properties-message.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-email-properties-to.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-email.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-index-default.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-index-properties-name.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-index.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-notable-default.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md delete mode 100644 docs/spec/deployments-properties-alert_action-properties-notable.md delete mode 100644 docs/spec/deployments-properties-alert_action.md delete mode 100644 docs/spec/deployments-properties-scheduling-default.md delete mode 100644 docs/spec/deployments-properties-scheduling-properties-cron_schedule.md delete mode 100644 docs/spec/deployments-properties-scheduling-properties-earliest_time.md delete mode 100644 docs/spec/deployments-properties-scheduling-properties-latest_time.md delete mode 100644 docs/spec/deployments-properties-scheduling-properties-schedule_window.md delete mode 100644 docs/spec/deployments-properties-scheduling.md delete mode 100644 docs/spec/detections-properties-known_false_positives.md delete mode 100644 docs/spec/detections-properties-references-the-items-schema.md delete mode 100644 docs/spec/detections-properties-references.md delete mode 100644 docs/spec/detections-properties-type-items.md delete mode 100644 docs/spec/detections-properties-type.md delete mode 100644 docs/spec/lookups-oneof-0.md delete mode 100644 docs/spec/lookups-oneof-1.md delete mode 100644 docs/spec/lookups-properties-case_sensitive_match.md delete mode 100644 docs/spec/lookups-properties-collection.md delete mode 100644 docs/spec/lookups-properties-default_match.md delete mode 100644 docs/spec/lookups-properties-description.md delete mode 100644 docs/spec/lookups-properties-fields_list.md delete mode 100644 docs/spec/lookups-properties-filename.md delete mode 100644 docs/spec/lookups-properties-filter.md delete mode 100644 docs/spec/lookups-properties-match_type.md delete mode 100644 docs/spec/lookups-properties-max_matches.md delete mode 100644 docs/spec/lookups-properties-min_matches.md delete mode 100644 docs/spec/lookups-properties-name.md delete mode 100644 docs/spec/macros-properties-arguments-items.md delete mode 100644 docs/spec/macros-properties-arguments.md delete mode 100644 docs/spec/macros-properties-definition.md delete mode 100644 docs/spec/macros-properties-description.md delete mode 100644 docs/spec/macros-properties-name.md delete mode 100644 docs/spec/response_tasks-default.md delete mode 100644 docs/spec/response_tasks-properties-automation-default.md delete mode 100644 docs/spec/response_tasks-properties-automation.md delete mode 100644 docs/spec/response_tasks-properties-sla.md delete mode 100644 docs/spec/response_tasks-properties-sla_type.md delete mode 100644 docs/spec/responses-default.md delete mode 100644 docs/spec/responses-properties-is_note_required.md delete mode 100644 docs/spec/responses-properties-response_phase-default.md delete mode 100644 docs/spec/responses-properties-response_phase.md delete mode 100644 docs/spec/responses_phase-default.md delete mode 100644 docs/spec/responses_phase-properties-response_task-default.md delete mode 100644 docs/spec/responses_phase-properties-response_task.md delete mode 100644 docs/spec/stories-default.md delete mode 100644 docs/spec/stories-properties-narrative.md diff --git a/.circleci/config.yml b/.circleci/config.yml index 873f29d0a7..53e77a82f3 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -90,7 +90,9 @@ jobs: # now generate spec docs sudo apt-get install -y npm -qq sudo npm install -g @adobe/jsonschema2md - jsonschema2md -d spec -o docs/spec -f yaml -e spec.json + jsonschema2md -d spec -o docs/spec -f yaml -e spec.json -x - + # clean up extra properties on docs + rm -rf docs/spec/*-*.md build-sources: executor: content-executor diff --git a/docs/spec/baselines-properties-author.md b/docs/spec/baselines-properties-author.md deleted file mode 100644 index 25b1c05002..0000000000 --- a/docs/spec/baselines-properties-author.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/author#/properties/author -``` - -Author of the baseline - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## author Type - -`string` - -## author Examples - -```yaml -Bahvin Patel, Splunk - -``` diff --git a/docs/spec/baselines-properties-datamodel-items.md b/docs/spec/baselines-properties-datamodel-items.md deleted file mode 100644 index c8e5070bfe..0000000000 --- a/docs/spec/baselines-properties-datamodel-items.md +++ /dev/null @@ -1,36 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/datamodel#/properties/datamodel/items -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## items Type - -`string` - -## items Constraints - -**enum**: the value of this property must be equal to one of the following values: - -| Value | Explanation | -| :--------------------- | :---------- | -| `"Endpoint"` | | -| `"Network_Traffic"` | | -| `"Authentication"` | | -| `"Change"` | | -| `"Change_Analysis"` | | -| `"Email"` | | -| `"Endpoint"` | | -| `"Network_Resolution"` | | -| `"Network_Sessions"` | | -| `"Network_Traffic"` | | -| `"UEBA"` | | -| `"Updates"` | | -| `"Vulnerabilities"` | | -| `"Web"` | | diff --git a/docs/spec/baselines-properties-datamodel.md b/docs/spec/baselines-properties-datamodel.md deleted file mode 100644 index 8ee4de9690..0000000000 --- a/docs/spec/baselines-properties-datamodel.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled array in Baseline Schema Schema - -```txt -#/properties/datamodel#/properties/datamodel -``` - -datamodel used in the search - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## datamodel Type - -`string[]` - -## datamodel Examples - -```yaml -Endpoint - -``` diff --git a/docs/spec/baselines-properties-date.md b/docs/spec/baselines-properties-date.md deleted file mode 100644 index f91effacdf..0000000000 --- a/docs/spec/baselines-properties-date.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/date#/properties/date -``` - -date of creation or modification, format yyyy-mm-dd - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## date Type - -`string` - -## date Examples - -```yaml -'2019-12-06' - -``` diff --git a/docs/spec/baselines-properties-description.md b/docs/spec/baselines-properties-description.md deleted file mode 100644 index e61031299b..0000000000 --- a/docs/spec/baselines-properties-description.md +++ /dev/null @@ -1,26 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/description#/properties/description -``` - -A detailed description of the baseline - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## description Type - -`string` - -## description Examples - -```yaml ->- - This search looks for CloudTrail events where an AWS instance is started and - creates a baseline of most recent time (latest) and the first time (earliest) - we've seen this region in our dataset grouped by the value awsRegion for the - last 30 days - -``` diff --git a/docs/spec/baselines-properties-how_to_implement.md b/docs/spec/baselines-properties-how_to_implement.md deleted file mode 100644 index 0bac7d7e11..0000000000 --- a/docs/spec/baselines-properties-how_to_implement.md +++ /dev/null @@ -1,24 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/how_to_implement#/properties/how_to_implement -``` - -information about how to implement. Only needed for non standard implementations. - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## how_to_implement Type - -`string` - -## how_to_implement Examples - -```yaml ->- - This search requires Sysmon Logs and a Sysmon configuration, which includes - EventCode 10 for lsass.exe. - -``` diff --git a/docs/spec/baselines-properties-id.md b/docs/spec/baselines-properties-id.md deleted file mode 100644 index 0ac191b7fb..0000000000 --- a/docs/spec/baselines-properties-id.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/id#/properties/id -``` - -UUID as unique identifier - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## id Type - -`string` - -## id Examples - -```yaml -fc0edc95-ff2b-48b0-9f6f-63da3789fd63 - -``` diff --git a/docs/spec/baselines-properties-name-of-baseline.md b/docs/spec/baselines-properties-name-of-baseline.md deleted file mode 100644 index 46e7b91cba..0000000000 --- a/docs/spec/baselines-properties-name-of-baseline.md +++ /dev/null @@ -1,22 +0,0 @@ -# Name of baseline Schema - -```txt -#/properties/name#/properties/name -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## name Type - -`string` ([Name of baseline](baselines-properties-name-of-baseline.md)) - -## name Examples - -```yaml -Previously Seen AWS Regions - -``` diff --git a/docs/spec/baselines-properties-search.md b/docs/spec/baselines-properties-search.md deleted file mode 100644 index dae22b4ca4..0000000000 --- a/docs/spec/baselines-properties-search.md +++ /dev/null @@ -1,24 +0,0 @@ -# Untitled string in Baseline Schema Schema - -```txt -#/properties/search#/properties/search -``` - -The Splunk search for the baseline - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## search Type - -`string` - -## search Examples - -```yaml ->- - cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as - latest by awsRegion | outputlookup previously_seen_aws_regions.csv - -``` diff --git a/docs/spec/baselines-properties-tags-default.md b/docs/spec/baselines-properties-tags-default.md deleted file mode 100644 index 6338be86cf..0000000000 --- a/docs/spec/baselines-properties-tags-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Baseline Schema Schema - -```txt -#/properties/tags#/properties/tags/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/baselines-properties-tags.md b/docs/spec/baselines-properties-tags.md deleted file mode 100644 index 622015ce22..0000000000 --- a/docs/spec/baselines-properties-tags.md +++ /dev/null @@ -1,47 +0,0 @@ -# Untitled object in Baseline Schema Schema - -```txt -#/properties/tags#/properties/tags -``` - -An array of key value pairs for tagging - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## tags Type - -`object` ([Details](baselines-properties-tags.md)) - -## tags Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -## tags Default Value - -The default value is: - -```json -{} -``` - -## tags Examples - -```yaml -analytic_story: suspicious_aws_ec2_activities -custom_key: custom_value - -``` - -# tags Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :--- | :------- | :---------- | :--------- | -| Additional Properties | Any | Optional | can be null | | - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/baselines-properties-version.md b/docs/spec/baselines-properties-version.md deleted file mode 100644 index 2d241c5920..0000000000 --- a/docs/spec/baselines-properties-version.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled integer in Baseline Schema Schema - -```txt -#/properties/version#/properties/version -``` - -version of baseline, e.g. 1 or 2 ... - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") | - -## version Type - -`integer` - -## version Examples - -```yaml -1 - -``` diff --git a/docs/spec/baselines.md b/docs/spec/baselines.md index 133ef8a080..ca84a410ca 100644 --- a/docs/spec/baselines.md +++ b/docs/spec/baselines.md @@ -6,9 +6,9 @@ http://example.com/example.json schema for baselines -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [baselines.spec.json](../../out/baselines.spec.json "open original schema") | +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [baselines.spec.json](../../spec/baselines.spec.json "open original schema") | ## Baseline Schema Type diff --git a/docs/spec/deployments-default.md b/docs/spec/deployments-default.md deleted file mode 100644 index 1a7b518209..0000000000 --- a/docs/spec/deployments-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -http://example.com/example.json#/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-alert_action-default.md b/docs/spec/deployments-properties-alert_action-default.md deleted file mode 100644 index 55e2308c13..0000000000 --- a/docs/spec/deployments-properties-alert_action-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -#/properties/alert_action#/properties/alert_action/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-email-default.md b/docs/spec/deployments-properties-alert_action-properties-email-default.md deleted file mode 100644 index c8b32006bb..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-email-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/email#/properties/alert_action/properties/email/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md deleted file mode 100644 index e01fa30a39..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message -``` - -message of email - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## message Type - -`string` - -## message Examples - -```yaml -Splunk Alert $name$ triggered %fields% - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md deleted file mode 100644 index e72fd29af3..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject -``` - -Subject of email - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## subject Type - -`string` - -## subject Examples - -```yaml -Splunk Alert $name$ - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md deleted file mode 100644 index 0bef21838f..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to -``` - -Recipient of email - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## to Type - -`string` - -## to Examples - -```yaml -test@test.com - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-email.md b/docs/spec/deployments-properties-alert_action-properties-email.md deleted file mode 100644 index 070e95ee64..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-email.md +++ /dev/null @@ -1,120 +0,0 @@ -# Untitled object in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/email#/properties/alert_action/properties/email -``` - -By enabling it, an email is sent with the results - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## email Type - -`object` ([Details](deployments-properties-alert_action-properties-email.md)) - -## email Default Value - -The default value is: - -```json -{} -``` - -## email Examples - -```yaml -message: Splunk Alert $name$ triggered %fields% -subject: Splunk Alert $name$ -to: test@test.com - -``` - -# email Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :------- | :------- | :------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| [message](#message) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-message.md "#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message") | -| [subject](#subject) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-subject.md "#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject") | -| [to](#to) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-to.md "#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to") | -| Additional Properties | Any | Optional | can be null | | - -## message - -message of email - -`message` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-message.md "#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message") - -### message Type - -`string` - -### message Examples - -```yaml -Splunk Alert $name$ triggered %fields% - -``` - -## subject - -Subject of email - -`subject` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-subject.md "#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject") - -### subject Type - -`string` - -### subject Examples - -```yaml -Splunk Alert $name$ - -``` - -## to - -Recipient of email - -`to` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-to.md "#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to") - -### to Type - -`string` - -### to Examples - -```yaml -test@test.com - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-alert_action-properties-index-default.md b/docs/spec/deployments-properties-alert_action-properties-index-default.md deleted file mode 100644 index 0acb5762ae..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-index-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/index#/properties/alert_action/properties/index/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md b/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md deleted file mode 100644 index be9a681a97..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name -``` - -Name of the index - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## name Type - -`string` - -## name Examples - -```yaml -asx - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-index.md b/docs/spec/deployments-properties-alert_action-properties-index.md deleted file mode 100644 index edc5070f0e..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-index.md +++ /dev/null @@ -1,66 +0,0 @@ -# Untitled object in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/index#/properties/alert_action/properties/index -``` - -By enabling it, the results are stored in another index - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## index Type - -`object` ([Details](deployments-properties-alert_action-properties-index.md)) - -## index Default Value - -The default value is: - -```json -{} -``` - -## index Examples - -```yaml -name: asx - -``` - -# index Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| [name](#name) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-index-properties-name.md "#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name") | -| Additional Properties | Any | Optional | can be null | | - -## name - -Name of the index - -`name` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-index-properties-name.md "#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name") - -### name Type - -`string` - -### name Examples - -```yaml -asx - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-default.md b/docs/spec/deployments-properties-alert_action-properties-notable-default.md deleted file mode 100644 index 84226f6fc7..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-notable-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md deleted file mode 100644 index 23882cd515..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description -``` - -Rule description of the notable event - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## rule_description Type - -`string` - -## rule_description Examples - -```yaml -'%description%' - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md deleted file mode 100644 index 9d5eb151c2..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title -``` - -Rule title of the notable event - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## rule_title Type - -`string` - -## rule_title Examples - -```yaml -'%name%' - -``` diff --git a/docs/spec/deployments-properties-alert_action-properties-notable.md b/docs/spec/deployments-properties-alert_action-properties-notable.md deleted file mode 100644 index d117f24ef9..0000000000 --- a/docs/spec/deployments-properties-alert_action-properties-notable.md +++ /dev/null @@ -1,93 +0,0 @@ -# Untitled object in Deployment Schema Schema - -```txt -#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable -``` - -By enabling it, a notable is generated - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## notable Type - -`object` ([Details](deployments-properties-alert_action-properties-notable.md)) - -## notable Default Value - -The default value is: - -```json -{} -``` - -## notable Examples - -```yaml -rule_description: '%description%' -rule_title: '%name%' - -``` - -# notable Properties - -| Property | Type | Required | Nullable | Defined by | -| :------------------------------------ | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| [rule_description](#rule_description) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_description.md "#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description") | -| [rule_title](#rule_title) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_title.md "#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title") | -| Additional Properties | Any | Optional | can be null | | - -## rule_description - -Rule description of the notable event - -`rule_description` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_description.md "#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description") - -### rule_description Type - -`string` - -### rule_description Examples - -```yaml -'%description%' - -``` - -## rule_title - -Rule title of the notable event - -`rule_title` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_title.md "#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title") - -### rule_title Type - -`string` - -### rule_title Examples - -```yaml -'%name%' - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-alert_action.md b/docs/spec/deployments-properties-alert_action.md deleted file mode 100644 index f5fa09670a..0000000000 --- a/docs/spec/deployments-properties-alert_action.md +++ /dev/null @@ -1,153 +0,0 @@ -# Untitled object in Deployment Schema Schema - -```txt -#/properties/alert_action#/properties/alert_action -``` - -Set alert action parameter for search - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## alert_action Type - -`object` ([Details](deployments-properties-alert_action.md)) - -## alert_action Default Value - -The default value is: - -```json -{} -``` - -## alert_action Examples - -```yaml -email: - message: Splunk Alert $name$ triggered %fields% - subject: Splunk Alert $name$ - to: test@test.com -index: - name: asx -notable: - rule_description: '%description%' - rule_title: '%name%' - -``` - -# alert_action Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| [email](#email) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email.md "#/properties/alert_action/properties/email#/properties/alert_action/properties/email") | -| [index](#index) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-index.md "#/properties/alert_action/properties/index#/properties/alert_action/properties/index") | -| [notable](#notable) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable.md "#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable") | -| Additional Properties | Any | Optional | can be null | | - -## email - -By enabling it, an email is sent with the results - -`email` - -* is optional - -* Type: `object` ([Details](deployments-properties-alert_action-properties-email.md)) - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-email.md "#/properties/alert_action/properties/email#/properties/alert_action/properties/email") - -### email Type - -`object` ([Details](deployments-properties-alert_action-properties-email.md)) - -### email Default Value - -The default value is: - -```json -{} -``` - -### email Examples - -```yaml -message: Splunk Alert $name$ triggered %fields% -subject: Splunk Alert $name$ -to: test@test.com - -``` - -## index - -By enabling it, the results are stored in another index - -`index` - -* is optional - -* Type: `object` ([Details](deployments-properties-alert_action-properties-index.md)) - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-index.md "#/properties/alert_action/properties/index#/properties/alert_action/properties/index") - -### index Type - -`object` ([Details](deployments-properties-alert_action-properties-index.md)) - -### index Default Value - -The default value is: - -```json -{} -``` - -### index Examples - -```yaml -name: asx - -``` - -## notable - -By enabling it, a notable is generated - -`notable` - -* is optional - -* Type: `object` ([Details](deployments-properties-alert_action-properties-notable.md)) - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable.md "#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable") - -### notable Type - -`object` ([Details](deployments-properties-alert_action-properties-notable.md)) - -### notable Default Value - -The default value is: - -```json -{} -``` - -### notable Examples - -```yaml -rule_description: '%description%' -rule_title: '%name%' - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments-properties-scheduling-default.md b/docs/spec/deployments-properties-scheduling-default.md deleted file mode 100644 index f39294b0a3..0000000000 --- a/docs/spec/deployments-properties-scheduling-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Deployment Schema Schema - -```txt -#/properties/scheduling#/properties/scheduling/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md b/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md deleted file mode 100644 index 52cbbfae51..0000000000 --- a/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule -``` - -Cron schedule to schedule the Splunk searches. - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## cron_schedule Type - -`string` - -## cron_schedule Examples - -```yaml -'*/10 * * * *' - -``` diff --git a/docs/spec/deployments-properties-scheduling-properties-earliest_time.md b/docs/spec/deployments-properties-scheduling-properties-earliest_time.md deleted file mode 100644 index 5808ddd97f..0000000000 --- a/docs/spec/deployments-properties-scheduling-properties-earliest_time.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time -``` - -earliest time of search - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## earliest_time Type - -`string` - -## earliest_time Examples - -```yaml -'-10m' - -``` diff --git a/docs/spec/deployments-properties-scheduling-properties-latest_time.md b/docs/spec/deployments-properties-scheduling-properties-latest_time.md deleted file mode 100644 index 566141825e..0000000000 --- a/docs/spec/deployments-properties-scheduling-properties-latest_time.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time -``` - -latest time of search - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## latest_time Type - -`string` - -## latest_time Examples - -```yaml -now - -``` diff --git a/docs/spec/deployments-properties-scheduling-properties-schedule_window.md b/docs/spec/deployments-properties-scheduling-properties-schedule_window.md deleted file mode 100644 index 41aad873d4..0000000000 --- a/docs/spec/deployments-properties-scheduling-properties-schedule_window.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Deployment Schema Schema - -```txt -#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window -``` - -schedule window for search - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## schedule_window Type - -`string` - -## schedule_window Examples - -```yaml -auto - -``` diff --git a/docs/spec/deployments-properties-scheduling.md b/docs/spec/deployments-properties-scheduling.md deleted file mode 100644 index cf64fb3727..0000000000 --- a/docs/spec/deployments-properties-scheduling.md +++ /dev/null @@ -1,147 +0,0 @@ -# Untitled object in Deployment Schema Schema - -```txt -#/properties/scheduling#/properties/scheduling -``` - -allows to set scheduling parameter - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") | - -## scheduling Type - -`object` ([Details](deployments-properties-scheduling.md)) - -## scheduling Default Value - -The default value is: - -```json -{} -``` - -## scheduling Examples - -```yaml -cron_schedule: '*/10 * * * *' -earliest_time: '-10m' -latest_time: now -schedule_window: auto - -``` - -# scheduling Properties - -| Property | Type | Required | Nullable | Defined by | -| :---------------------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| [cron_schedule](#cron_schedule) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-cron_schedule.md "#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule") | -| [earliest_time](#earliest_time) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-earliest_time.md "#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time") | -| [latest_time](#latest_time) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-latest_time.md "#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time") | -| [schedule_window](#schedule_window) | `string` | Optional | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-schedule_window.md "#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window") | -| Additional Properties | Any | Optional | can be null | | - -## cron_schedule - -Cron schedule to schedule the Splunk searches. - -`cron_schedule` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-scheduling-properties-cron_schedule.md "#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule") - -### cron_schedule Type - -`string` - -### cron_schedule Examples - -```yaml -'*/10 * * * *' - -``` - -## earliest_time - -earliest time of search - -`earliest_time` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-scheduling-properties-earliest_time.md "#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time") - -### earliest_time Type - -`string` - -### earliest_time Examples - -```yaml -'-10m' - -``` - -## latest_time - -latest time of search - -`latest_time` - -* is required - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-scheduling-properties-latest_time.md "#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time") - -### latest_time Type - -`string` - -### latest_time Examples - -```yaml -now - -``` - -## schedule_window - -schedule window for search - -`schedule_window` - -* is optional - -* Type: `string` - -* cannot be null - -* defined in: [Deployment Schema](deployments-properties-scheduling-properties-schedule_window.md "#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window") - -### schedule_window Type - -`string` - -### schedule_window Examples - -```yaml -auto - -``` - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/deployments.md b/docs/spec/deployments.md index 4d1597e99c..d77cb1e62f 100644 --- a/docs/spec/deployments.md +++ b/docs/spec/deployments.md @@ -6,9 +6,9 @@ http://example.com/example.json schema for deployment -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------ | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json](../../out/deployments.spec.json "open original schema") | +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [deployments.spec.json](../../spec/deployments.spec.json "open original schema") | ## Deployment Schema Type diff --git a/docs/spec/detections-properties-known_false_positives.md b/docs/spec/detections-properties-known_false_positives.md deleted file mode 100644 index ae7c1bafbd..0000000000 --- a/docs/spec/detections-properties-known_false_positives.md +++ /dev/null @@ -1,24 +0,0 @@ -# Untitled string in Detection Schema Schema - -```txt -#/properties/knwon_false_positives#/properties/known_false_positives -``` - -known false postives - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | - -## known_false_positives Type - -`string` - -## known_false_positives Examples - -```yaml ->- - Administrators can create memory dumps for debugging purposes, but memory - dumps of the LSASS process would be unusual. - -``` diff --git a/docs/spec/detections-properties-references-the-items-schema.md b/docs/spec/detections-properties-references-the-items-schema.md deleted file mode 100644 index c184428f27..0000000000 --- a/docs/spec/detections-properties-references-the-items-schema.md +++ /dev/null @@ -1,23 +0,0 @@ -# The Items Schema Schema - -```txt -#/properties/references/items#/properties/references/items -``` - -An explanation about the purpose of this instance. - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | - -## items Type - -`string` ([The Items Schema](detections-properties-references-the-items-schema.md)) - -## items Examples - -```yaml ->- - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -``` diff --git a/docs/spec/detections-properties-references.md b/docs/spec/detections-properties-references.md deleted file mode 100644 index 07618fc3f0..0000000000 --- a/docs/spec/detections-properties-references.md +++ /dev/null @@ -1,31 +0,0 @@ -# Untitled array in Detection Schema Schema - -```txt -#/properties/references#/properties/references -``` - -A list of references for this detection - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | - -## references Type - -`string[]` ([The Items Schema](detections-properties-references-the-items-schema.md)) - -## references Default Value - -The default value is: - -```json -[] -``` - -## references Examples - -```yaml -- >- - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf - -``` diff --git a/docs/spec/detections-properties-type-items.md b/docs/spec/detections-properties-type-items.md deleted file mode 100644 index 226980c0e6..0000000000 --- a/docs/spec/detections-properties-type-items.md +++ /dev/null @@ -1,24 +0,0 @@ -# Untitled string in Detection Schema Schema - -```txt -#/properties/type#/properties/type/items -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | - -## items Type - -`string` - -## items Constraints - -**enum**: the value of this property must be equal to one of the following values: - -| Value | Explanation | -| :------------ | :---------- | -| `"batch"` | | -| `"streaming"` | | diff --git a/docs/spec/detections-properties-type.md b/docs/spec/detections-properties-type.md deleted file mode 100644 index 98c36f20ac..0000000000 --- a/docs/spec/detections-properties-type.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Detection Schema Schema - -```txt -#/properties/type#/properties/type -``` - -type of detection - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [detections.spec.json*](../../out/detections.spec.json "open original schema") | - -## type Type - -`string` - -## type Examples - -```yaml -streaming - -``` diff --git a/docs/spec/detections.md b/docs/spec/detections.md index e2d8a0a501..e37aa83875 100644 --- a/docs/spec/detections.md +++ b/docs/spec/detections.md @@ -6,9 +6,9 @@ http://example.com/example.json schema for detections -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [detections.spec.json](../../out/detections.spec.json "open original schema") | +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [detections.spec.json](../../spec/detections.spec.json "open original schema") | ## Detection Schema Type diff --git a/docs/spec/lookups-oneof-0.md b/docs/spec/lookups-oneof-0.md deleted file mode 100644 index 78403ade45..0000000000 --- a/docs/spec/lookups-oneof-0.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/oneOf/0 -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## 0 Type - -unknown diff --git a/docs/spec/lookups-oneof-1.md b/docs/spec/lookups-oneof-1.md deleted file mode 100644 index 4b493c4f17..0000000000 --- a/docs/spec/lookups-oneof-1.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/oneOf/1 -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## 1 Type - -unknown diff --git a/docs/spec/lookups-properties-case_sensitive_match.md b/docs/spec/lookups-properties-case_sensitive_match.md deleted file mode 100644 index b067278e67..0000000000 --- a/docs/spec/lookups-properties-case_sensitive_match.md +++ /dev/null @@ -1,31 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/case_sensitive_match -``` - -What the macro is intended to filter - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## case_sensitive_match Type - -`string` - -## case_sensitive_match Constraints - -**enum**: the value of this property must be equal to one of the following values: - -| Value | Explanation | -| :-------- | :---------- | -| `"true"` | | -| `"false"` | | - -## case_sensitive_match Examples - -```yaml -'true' - -``` diff --git a/docs/spec/lookups-properties-collection.md b/docs/spec/lookups-properties-collection.md deleted file mode 100644 index 62b7028b4d..0000000000 --- a/docs/spec/lookups-properties-collection.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/collection -``` - -Name of the collection to use for this lookup - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## collection Type - -`string` - -## collection Examples - -```yaml -prohibited_apps_launching_cmd - -``` diff --git a/docs/spec/lookups-properties-default_match.md b/docs/spec/lookups-properties-default_match.md deleted file mode 100644 index 46369bbdda..0000000000 --- a/docs/spec/lookups-properties-default_match.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/default_match -``` - -The default value if no match is found - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## default_match Type - -`string` - -## default_match Examples - -```yaml -'true' - -``` diff --git a/docs/spec/lookups-properties-description.md b/docs/spec/lookups-properties-description.md deleted file mode 100644 index e9fc0cfe11..0000000000 --- a/docs/spec/lookups-properties-description.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/description -``` - -The description of this lookup - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## description Type - -`string` - -## description Examples - -```yaml -This lookup contains file names that exist in the Windows\System32 directory - -``` diff --git a/docs/spec/lookups-properties-fields_list.md b/docs/spec/lookups-properties-fields_list.md deleted file mode 100644 index e0a699ee17..0000000000 --- a/docs/spec/lookups-properties-fields_list.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/fields_list -``` - -A comma and space separated list of field names - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## fields_list Type - -`string` - -## fields_list Examples - -```yaml -_key, dest, process_name - -``` diff --git a/docs/spec/lookups-properties-filename.md b/docs/spec/lookups-properties-filename.md deleted file mode 100644 index 80584c0215..0000000000 --- a/docs/spec/lookups-properties-filename.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/filename -``` - -The name of the file to use for this lookup - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## filename Type - -`string` - -## filename Examples - -```yaml -prohibited_apps_launching_cmd.csv - -``` diff --git a/docs/spec/lookups-properties-filter.md b/docs/spec/lookups-properties-filter.md deleted file mode 100644 index ad1a68ff22..0000000000 --- a/docs/spec/lookups-properties-filter.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/filter -``` - -Use this attribute to improve search performance when working with significantly large KV - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## filter Type - -`string` - -## filter Examples - -```yaml -dest="SPLK_*" - -``` diff --git a/docs/spec/lookups-properties-match_type.md b/docs/spec/lookups-properties-match_type.md deleted file mode 100644 index 3ca467f6a8..0000000000 --- a/docs/spec/lookups-properties-match_type.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/match_type -``` - -A comma and space-delimited list of \(\) specification to allow for non-exact matching - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## match_type Type - -`string` - -## match_type Examples - -```yaml -WILDCARD(process) - -``` diff --git a/docs/spec/lookups-properties-max_matches.md b/docs/spec/lookups-properties-max_matches.md deleted file mode 100644 index 652395498b..0000000000 --- a/docs/spec/lookups-properties-max_matches.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled integer in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/max_matches -``` - -The maximum number of possible matches for each input lookup value - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## max_matches Type - -`integer` - -## max_matches Examples - -```yaml -'100' - -``` diff --git a/docs/spec/lookups-properties-min_matches.md b/docs/spec/lookups-properties-min_matches.md deleted file mode 100644 index e950b97fbd..0000000000 --- a/docs/spec/lookups-properties-min_matches.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled integer in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/min_matches -``` - -Minimum number of possible matches for each input lookup value - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## min_matches Type - -`integer` - -## min_matches Examples - -```yaml -'1' - -``` diff --git a/docs/spec/lookups-properties-name.md b/docs/spec/lookups-properties-name.md deleted file mode 100644 index ca27b5144c..0000000000 --- a/docs/spec/lookups-properties-name.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Lookup Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/lookups.json#/properties/name -``` - -The name of the lookup to be used in searches - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") | - -## name Type - -`string` - -## name Examples - -```yaml -isWindowsSystemFile_lookup - -``` diff --git a/docs/spec/lookups.md b/docs/spec/lookups.md index 79b6e284e3..d6a8ec5141 100644 --- a/docs/spec/lookups.md +++ b/docs/spec/lookups.md @@ -6,9 +6,9 @@ https://api.splunkresearch.com/schemas/lookups.json A object that defines a lookup file and its properties. -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [lookups.spec.json](../../out/lookups.spec.json "open original schema") | +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [lookups.spec.json](../../spec/lookups.spec.json "open original schema") | ## Lookup Manifest Type diff --git a/docs/spec/macros-properties-arguments-items.md b/docs/spec/macros-properties-arguments-items.md deleted file mode 100644 index 34f73377e8..0000000000 --- a/docs/spec/macros-properties-arguments-items.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled string in Macro Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/macros.json#/properties/arguments/items -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | - -## items Type - -`string` diff --git a/docs/spec/macros-properties-arguments.md b/docs/spec/macros-properties-arguments.md deleted file mode 100644 index 1334acd3c1..0000000000 --- a/docs/spec/macros-properties-arguments.md +++ /dev/null @@ -1,21 +0,0 @@ -# Untitled array in Macro Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/macros.json#/properties/arguments -``` - -A list of the arguments being passed to this macro - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | - -## arguments Type - -`string[]` - -## arguments Constraints - -**minimum number of items**: the minimum number of items for this array is: `0` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. diff --git a/docs/spec/macros-properties-definition.md b/docs/spec/macros-properties-definition.md deleted file mode 100644 index 128ea17364..0000000000 --- a/docs/spec/macros-properties-definition.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Macro Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/macros.json#/properties/definition -``` - -The macro definition - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | - -## definition Type - -`string` - -## definition Examples - -```yaml -(query=fls-na* AND query = www* AND query=images*) - -``` diff --git a/docs/spec/macros-properties-description.md b/docs/spec/macros-properties-description.md deleted file mode 100644 index e5f8aed632..0000000000 --- a/docs/spec/macros-properties-description.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Macro Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/macros.json#/properties/description -``` - -What the macro is intended to filter - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | - -## description Type - -`string` - -## description Examples - -```yaml -Use this macro to filter out known good objects - -``` diff --git a/docs/spec/macros-properties-name.md b/docs/spec/macros-properties-name.md deleted file mode 100644 index 484c1e4e3f..0000000000 --- a/docs/spec/macros-properties-name.md +++ /dev/null @@ -1,22 +0,0 @@ -# Untitled string in Macro Manifest Schema - -```txt -https://api.splunkresearch.com/schemas/macros.json#/properties/name -``` - -The name of the macro - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [macros.spec.json*](../../out/macros.spec.json "open original schema") | - -## name Type - -`string` - -## name Examples - -```yaml -detection_search_output_filter - -``` diff --git a/docs/spec/macros.md b/docs/spec/macros.md index c3cb8dc57b..3047f816f2 100644 --- a/docs/spec/macros.md +++ b/docs/spec/macros.md @@ -6,9 +6,9 @@ https://api.splunkresearch.com/schemas/macros.json An object that defines the parameters for a Splunk Macro -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [macros.spec.json](../../out/macros.spec.json "open original schema") | +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [macros.spec.json](../../spec/macros.spec.json "open original schema") | ## Macro Manifest Type diff --git a/docs/spec/response_tasks-default.md b/docs/spec/response_tasks-default.md deleted file mode 100644 index 31296217d9..0000000000 --- a/docs/spec/response_tasks-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response_tasks.spec.json#/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/response_tasks-properties-automation-default.md b/docs/spec/response_tasks-properties-automation-default.md deleted file mode 100644 index b2cdd45428..0000000000 --- a/docs/spec/response_tasks-properties-automation-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -#/properties/automation#/properties/automation/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/response_tasks-properties-automation.md b/docs/spec/response_tasks-properties-automation.md deleted file mode 100644 index bdd1eb66d4..0000000000 --- a/docs/spec/response_tasks-properties-automation.md +++ /dev/null @@ -1,62 +0,0 @@ -# Untitled object in Response Schema Schema - -```txt -#/properties/automation#/properties/automation -``` - -An array of key value pairs for defining actions and playbooks - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | - -## automation Type - -`object` ([Details](response_tasks-properties-automation.md)) - -## automation Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -**unique items**: all items in this array must be unique. Duplicates are not allowed. - -## automation Default Value - -The default value is: - -```json -{ - "is_note_required": false, - "sla_type": "minutes", - "sla": "", - "role": "", - "action": [], - "playbooks": [] -} -``` - -## automation Examples - -```yaml -is_note_required: false -sla_type: minutes -sla: 30 -action: - - run_query -playbooks: - - scm: local - playbook: automate something - - scm: local - playbook: automate something else - -``` - -# automation Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :--- | :------- | :---------- | :--------- | -| Additional Properties | Any | Optional | can be null | | - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/response_tasks-properties-sla.md b/docs/spec/response_tasks-properties-sla.md deleted file mode 100644 index 9de4e3e98f..0000000000 --- a/docs/spec/response_tasks-properties-sla.md +++ /dev/null @@ -1,27 +0,0 @@ -# Untitled integer in Response Schema Schema - -```txt -#/properties/sla#/properties/sla -``` - -Measured integer for Service Level Agreement for completion of the phase - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | - -## sla Type - -`integer` - -## sla Examples - -```yaml -5 - -``` - -```yaml -30 - -``` diff --git a/docs/spec/response_tasks-properties-sla_type.md b/docs/spec/response_tasks-properties-sla_type.md deleted file mode 100644 index 28319602eb..0000000000 --- a/docs/spec/response_tasks-properties-sla_type.md +++ /dev/null @@ -1,40 +0,0 @@ -# Untitled string in Response Schema Schema - -```txt -#/properties/sla_type#/properties/sla_type -``` - -Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc) - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") | - -## sla_type Type - -`string` - -## sla_type Default Value - -The default value is: - -```json -"minutes" -``` - -## sla_type Examples - -```yaml -minutes - -``` - -```yaml -hours - -``` - -```yaml -days - -``` diff --git a/docs/spec/response_tasks.md b/docs/spec/response_tasks.md index 6ee8b796ed..de3298f7e4 100644 --- a/docs/spec/response_tasks.md +++ b/docs/spec/response_tasks.md @@ -6,9 +6,9 @@ https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/resp schema for response task -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------ | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [response_tasks.spec.json](../../out/response_tasks.spec.json "open original schema") | +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [response_tasks.spec.json](../../spec/response_tasks.spec.json "open original schema") | ## Response Schema Type diff --git a/docs/spec/responses-default.md b/docs/spec/responses-default.md deleted file mode 100644 index 3478b338bd..0000000000 --- a/docs/spec/responses-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response.spec.json#/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/responses-properties-is_note_required.md b/docs/spec/responses-properties-is_note_required.md deleted file mode 100644 index 26aed247e2..0000000000 --- a/docs/spec/responses-properties-is_note_required.md +++ /dev/null @@ -1,27 +0,0 @@ -# Untitled boolean in Response Schema Schema - -```txt -#/properties/is_note_required#/properties/is_note_required -``` - -Global assignment for notes being required for tasks, can be individually set in the task - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | - -## is_note_required Type - -`boolean` - -## is_note_required Examples - -```yaml -true - -``` - -```yaml -false - -``` diff --git a/docs/spec/responses-properties-response_phase-default.md b/docs/spec/responses-properties-response_phase-default.md deleted file mode 100644 index e773e97379..0000000000 --- a/docs/spec/responses-properties-response_phase-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -#/properties/response_phases#/properties/response_phase/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/responses-properties-response_phase.md b/docs/spec/responses-properties-response_phase.md deleted file mode 100644 index bdd06286c0..0000000000 --- a/docs/spec/responses-properties-response_phase.md +++ /dev/null @@ -1,51 +0,0 @@ -# Untitled array in Response Schema Schema - -```txt -#/properties/response_phases#/properties/response_phase -``` - -Response divided into phases. These will used to referenced known response_phase parameters - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses.spec.json*](../../out/responses.spec.json "open original schema") | - -## response_phase Type - -`array` - -## response_phase Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -## response_phase Default Value - -The default value is: - -```json -{} -``` - -## response_phase Examples - -```yaml -preparation: - - id: 7c72d944-3995-4485-8e57-67b4c353989b - name: Preparation NIST -identification: - - id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892 - name: Detection and Analysis - - id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0 - name: Analysis - -``` - -# response_phase Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :--- | :------- | :---------- | :--------- | -| Additional Properties | Any | Optional | can be null | | - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/responses.md b/docs/spec/responses.md index 314ad93450..59068b91a7 100644 --- a/docs/spec/responses.md +++ b/docs/spec/responses.md @@ -6,9 +6,9 @@ https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/resp schema for response -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [responses.spec.json](../../out/responses.spec.json "open original schema") | +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [responses.spec.json](../../spec/responses.spec.json "open original schema") | ## Response Schema Type diff --git a/docs/spec/responses_phase-default.md b/docs/spec/responses_phase-default.md deleted file mode 100644 index c1576f1e4e..0000000000 --- a/docs/spec/responses_phase-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -http://example.com/example.json#/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/responses_phase-properties-response_task-default.md b/docs/spec/responses_phase-properties-response_task-default.md deleted file mode 100644 index 9ede2dfaf9..0000000000 --- a/docs/spec/responses_phase-properties-response_task-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Response Schema Schema - -```txt -#/properties/response_task#/properties/response_task/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/responses_phase-properties-response_task.md b/docs/spec/responses_phase-properties-response_task.md deleted file mode 100644 index 780e4b3d5b..0000000000 --- a/docs/spec/responses_phase-properties-response_task.md +++ /dev/null @@ -1,57 +0,0 @@ -# Untitled array in Response Schema Schema - -```txt -#/properties/response_task#/properties/response_task -``` - -Response phase is divided into task(s) to be completed. These will used to referenced known response_task parameters. Order is as positioned and with unique name. - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") | - -## response_task Type - -`array` - -## response_task Constraints - -**minimum number of items**: the minimum number of items for this array is: `1` - -## response_task Default Value - -The default value is: - -```json -{} -``` - -## response_task Examples - -```yaml -id: 7c72d944-3995-4485-8e57-67b4c353989b -name: Prepare for Incident Handling - -``` - -```yaml -id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892 -name: Preventing Incidents - -``` - -```yaml -id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0 -name: Practice - -``` - -# response_task Properties - -| Property | Type | Required | Nullable | Defined by | -| :-------------------- | :--- | :------- | :---------- | :--------- | -| Additional Properties | Any | Optional | can be null | | - -## Additional Properties - -Additional properties are allowed and do not have to follow a specific schema diff --git a/docs/spec/responses_phase.md b/docs/spec/responses_phase.md index 09d1b3be2a..0d98bfc963 100644 --- a/docs/spec/responses_phase.md +++ b/docs/spec/responses_phase.md @@ -6,9 +6,9 @@ http://example.com/example.json schema for phase -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [responses_phase.spec.json](../../out/responses_phase.spec.json "open original schema") | +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [responses_phase.spec.json](../../spec/responses_phase.spec.json "open original schema") | ## Response Schema Type diff --git a/docs/spec/stories-default.md b/docs/spec/stories-default.md deleted file mode 100644 index 9abc854a40..0000000000 --- a/docs/spec/stories-default.md +++ /dev/null @@ -1,15 +0,0 @@ -# Untitled undefined type in Analytics Story Schema Schema - -```txt -http://example.com/example.json#/default -``` - - - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [stories.spec.json*](../../out/stories.spec.json "open original schema") | - -## default Type - -unknown diff --git a/docs/spec/stories-properties-narrative.md b/docs/spec/stories-properties-narrative.md deleted file mode 100644 index cc4532dd7b..0000000000 --- a/docs/spec/stories-properties-narrative.md +++ /dev/null @@ -1,26 +0,0 @@ -# Untitled string in Analytics Story Schema Schema - -```txt -#/properties/narrative#/properties/narrative -``` - -narrative of the analytics story - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | Unknown identifiability | Forbidden | Allowed | none | [stories.spec.json*](../../out/stories.spec.json "open original schema") | - -## narrative Type - -`string` - -## narrative Examples - -```yaml ->- - gathering credentials from a target system, often hashed or encrypted, is a - common attack technique. Even though the credentials may not be in plain text, - an attacker can still exfiltrate the data and set to cracking it offline, on - their own systems. - -``` diff --git a/docs/spec/stories.md b/docs/spec/stories.md index 50b565a2fe..9e37724776 100644 --- a/docs/spec/stories.md +++ b/docs/spec/stories.md @@ -6,9 +6,9 @@ http://example.com/example.json schema analytics story -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | -| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------- | -| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [stories.spec.json](../../out/stories.spec.json "open original schema") | +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In | +| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- | +| Can be instantiated | No | Unknown status | No | Forbidden | Allowed | none | [stories.spec.json](../../spec/stories.spec.json "open original schema") | ## Analytics Story Schema Type From 3a9b4b7b4b6ab4b329d2753d34edda6ae58b6e61 Mon Sep 17 00:00:00 2001 From: divious1 Date: Mon, 22 Mar 2021 19:18:30 -0400 Subject: [PATCH 53/62] fixing conflicts --- requirements.txt | 31 ++++++++++++++++++------------- 1 file changed, 18 insertions(+), 13 deletions(-) diff --git a/requirements.txt b/requirements.txt index 81079a35de..28b1e6d5d9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -antlr4-python3-runtime==4.8.0 +antlr4-python3-runtime==4.9.2 appdirs==1.4.4 aspy.yaml==1.3.0 attackcti==0.3.4.3 @@ -9,7 +9,7 @@ cfgv==3.2.0 chardet==4.0.0 colorama==0.4.3 coloredlogs==14.0 -configparser==5.0.1 +configparser==5.0.2 contextlib2==0.6.0.post1 distlib==0.3.1 distro==1.4.0 @@ -18,27 +18,30 @@ fire==0.3.1 gitdb==4.0.5 html5lib==1.0.1 humanfriendly==9.1 -identify==1.5.13 +identify==2.1.3 idna==2.10 -importlib-metadata==3.4.0 -importlib-resources==5.1.0 +importlib-metadata==3.7.3 +importlib-resources==5.1.2 ipaddr==2.2.0 Jinja2==2.11.3 jsonschema==3.2.0 lockfile==0.12.2 MarkupSafe==1.1.1 -more-itertools==8.6.0 +more-itertools==8.7.0 msgpack==0.6.2 nodeenv==1.5.0 packaging==20.3 pathlib2==2.3.5 pendulum==1.2.5 pep517==0.8.2 -Pillow==8.1.1 -pre-commit==2.9.3 +Pillow==8.1.2 +pre-commit==2.11.1 progress==1.5 +prompt-toolkit==1.0.14 pyattck==2.1.3 pyfiglet==0.8.post1 +Pygments==2.8.1 +PyInquirer==1.0.3 pyparsing==2.4.6 pyrsistent==0.17.3 python-dateutil==2.8.1 @@ -46,6 +49,7 @@ pytoml==0.1.21 pytz==2021.1 pytzdata==2020.1 PyYAML==5.4.1 +regex==2021.3.17 requests==2.25.1 retrying==1.3.3 scandir==1.10.0 @@ -55,13 +59,14 @@ six==1.15.0 sly==0.4 smmap==3.0.5 stix2==2.1.0 -stix2-patterns==1.3.2 -taxii2-client==2.2.2 +stix2-patterns==1.2.1 +taxii2-client==2.3.0 termcolor==1.1.0 toml==0.10.2 typing==3.7.4.3 tzlocal==2.1 -urllib3==1.26.3 -virtualenv==20.4.2 +urllib3==1.26.4 +virtualenv==20.4.3 +wcwidth==0.2.5 webencodings==0.5.1 -zipp==3.4.0 +zipp==3.4.1 From f5bfe97b81420ef4203472eeb68ffc0031fa1122 Mon Sep 17 00:00:00 2001 From: tcontreras Date: Tue, 23 Mar 2021 11:33:10 +0100 Subject: [PATCH 54/62] clop_mod_detections_add_processid_comments --- detections/endpoint/clop_common_exec_parameter.yml | 3 ++- detections/endpoint/clop_ransomware_known_service_name.yml | 2 +- detections/endpoint/high_file_deletion_frequency.yml | 3 ++- 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/detections/endpoint/clop_common_exec_parameter.yml b/detections/endpoint/clop_common_exec_parameter.yml index 71c8ae2eff..d24f1f3797 100644 --- a/detections/endpoint/clop_common_exec_parameter.yml +++ b/detections/endpoint/clop_common_exec_parameter.yml @@ -17,7 +17,7 @@ search: '| tstats `security_content_summariesonly` values(Processes.process) as values(Processes.parent_process_name) as parent_process values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" by Processes.parent_process_name - Processes.process_name Processes.process Processes.dest Processes.user | `security_content_ctime(firstTime)` + Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_common_exec_parameter_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your @@ -46,6 +46,7 @@ tags: - Processes.process_name - Processes.dest - Processes.user + - Processes.process_id security_domain: endpoint dataset: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log diff --git a/detections/endpoint/clop_ransomware_known_service_name.yml b/detections/endpoint/clop_ransomware_known_service_name.yml index d5b63cd205..8c99a1aa20 100644 --- a/detections/endpoint/clop_ransomware_known_service_name.yml +++ b/detections/endpoint/clop_ransomware_known_service_name.yml @@ -7,7 +7,7 @@ type: batch datamodel: - Endpoint description: This detection is to identify the common service name created by the CLOP ransomware as part of its persistence - and high privilege code execution in the infected machine. Ussually CLOP ransomware use StartServiceCtrlDispatcherW API in creating this service entry. + and high privilege code execution in the infected machine. Ussually CLOP ransomware use StartServiceCtrlDispatcherW API in creating this service entry. search: '`wineventlog_system` EventCode=7045 Service_Name IN ("SecurityCenterIBM", "WinCheckDRVs") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `security_content_ctime(firstTime)` diff --git a/detections/endpoint/high_file_deletion_frequency.yml b/detections/endpoint/high_file_deletion_frequency.yml index dc99cc2d2b..dde285f710 100644 --- a/detections/endpoint/high_file_deletion_frequency.yml +++ b/detections/endpoint/high_file_deletion_frequency.yml @@ -7,7 +7,8 @@ type: batch datamodel: - Endpoint description: This search looks for high frequency of file deletion relative to process name and process id. - These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions.. + These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions and sysmon treat the + original files to be deleted as soon it was replace as encrypted data. search: '`sysmon`EventCode=23 TargetFilename IN ("*\.cmd", "*\.ini","*\.gif", "*\.jpg", "*\.jpeg", "*\.db", "*\.ps1", "*\.doc*", "*\.xls*", "*\.ppt*", "*\.bmp","*\.zip", "*\.rar", "*\.7z", "*\.chm", "*\.png", "*\.log", "*\.vbs", "*\.js") | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer user EventCode Image ProcessID From 6da776bb4a33c4c2defa768738ea367220b4892e Mon Sep 17 00:00:00 2001 From: root Date: Tue, 23 Mar 2021 11:04:30 +0000 Subject: [PATCH 55/62] Added detection testing service results inClop Ransomware Known Service Name --- .../clop_ransomware_known_service_name.yml | 23 +++++++++++-------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/detections/endpoint/clop_ransomware_known_service_name.yml b/detections/endpoint/clop_ransomware_known_service_name.yml index 8c99a1aa20..dcd20febdc 100644 --- a/detections/endpoint/clop_ransomware_known_service_name.yml +++ b/detections/endpoint/clop_ransomware_known_service_name.yml @@ -6,16 +6,17 @@ author: Teoderick Contreras type: batch datamodel: - Endpoint -description: This detection is to identify the common service name created by the CLOP ransomware as part of its persistence - and high privilege code execution in the infected machine. Ussually CLOP ransomware use StartServiceCtrlDispatcherW API in creating this service entry. -search: '`wineventlog_system` EventCode=7045 Service_Name IN ("SecurityCenterIBM", "WinCheckDRVs") - | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `clop_ransomware_known_service_name_filter`' +description: This detection is to identify the common service name created by the + CLOP ransomware as part of its persistence and high privilege code execution in + the infected machine. Ussually CLOP ransomware use StartServiceCtrlDispatcherW API + in creating this service entry. +search: '`wineventlog_system` EventCode=7045 Service_Name IN ("SecurityCenterIBM", + "WinCheckDRVs") | stats count min(_time) as firstTime max(_time) as lastTime by + EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `clop_ransomware_known_service_name_filter`' how_to_implement: To successfully implement this search, you need to be ingesting - logs with the Service name, Service File Name Service Start type, and Service Type from your - endpoints. + logs with the Service name, Service File Name Service Start type, and Service Type + from your endpoints. known_false_positives: unknown references: - https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html @@ -40,4 +41,6 @@ tags: - OriginalFileName - process_path security_domain: endpoint - \ No newline at end of file + automated_detection_testing: passed + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-system.log From 1696edba4129e02d38d8d2c6bf4a47066afc58cf Mon Sep 17 00:00:00 2001 From: tcontreras Date: Tue, 23 Mar 2021 12:28:54 +0100 Subject: [PATCH 56/62] clop_detection_processid_mod --- detections/endpoint/high_file_deletion_frequency.yml | 2 +- detections/endpoint/high_process_termination_frequency.yml | 5 +++-- .../endpoint/process_deleting_its_process_file_path.yml | 4 ++-- 3 files changed, 6 insertions(+), 5 deletions(-) diff --git a/detections/endpoint/high_file_deletion_frequency.yml b/detections/endpoint/high_file_deletion_frequency.yml index dde285f710..5b156371eb 100644 --- a/detections/endpoint/high_file_deletion_frequency.yml +++ b/detections/endpoint/high_file_deletion_frequency.yml @@ -9,7 +9,7 @@ datamodel: description: This search looks for high frequency of file deletion relative to process name and process id. These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions and sysmon treat the original files to be deleted as soon it was replace as encrypted data. -search: '`sysmon`EventCode=23 +search: '`sysmon` EventCode=23 TargetFilename IN ("*\.cmd", "*\.ini","*\.gif", "*\.jpg", "*\.jpeg", "*\.db", "*\.ps1", "*\.doc*", "*\.xls*", "*\.ppt*", "*\.bmp","*\.zip", "*\.rar", "*\.7z", "*\.chm", "*\.png", "*\.log", "*\.vbs", "*\.js") | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer user EventCode Image ProcessID |where count >=100 diff --git a/detections/endpoint/high_process_termination_frequency.yml b/detections/endpoint/high_process_termination_frequency.yml index 35b2cf647c..410a2b0983 100644 --- a/detections/endpoint/high_process_termination_frequency.yml +++ b/detections/endpoint/high_process_termination_frequency.yml @@ -8,9 +8,9 @@ datamodel: - Endpoint description: This analytics are designed to indentify a high frequency of process termination on a machine which is a common behavior of ransomware malware before encrypting files. This technique is designed to avoid an exception error while accessing (docs, images, database and etc..) in the infected machine for encryption. -search: '`sysmon`EventCode=5 +search: '`sysmon` EventCode=5 |bin _time span=3s - |stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime count by Computer EventCode | where count >= 15 + |stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime count by Computer EventCode ProcessID | where count >= 15 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `high_process_termination_frequency_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the Image (process full path of terminated process) from your @@ -36,4 +36,5 @@ tags: - Image - Computer - _time + - ProcessID security_domain: endpoint \ No newline at end of file diff --git a/detections/endpoint/process_deleting_its_process_file_path.yml b/detections/endpoint/process_deleting_its_process_file_path.yml index a6a3ba3ad6..12b6194ab8 100644 --- a/detections/endpoint/process_deleting_its_process_file_path.yml +++ b/detections/endpoint/process_deleting_its_process_file_path.yml @@ -9,10 +9,10 @@ datamodel: description: This detection is to identify a suspicious process that tries to delete the process file path related to its process. This technique is known to be defense evasion once a certain condition of malware is satisfied or not. Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect. -search: '`sysmon`EventCode=1 cmdline = "*/c del*" Image = "*\\cmd.exe" +search: '`sysmon` EventCode=1 cmdline = "*/c del*" Image = "*\\cmd.exe" |eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") | stats min(_time) as firstTime max(_time) as lastTime count by Computer user ParentImage ParentCommandLine Image cmdline EventCode ProcessID result - | where result = "contained" + | where result = "Found" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_deleting_its_process_file_path_filter`' From f95182fca688d354152ea7d55b075270623a01c4 Mon Sep 17 00:00:00 2001 From: root Date: Tue, 23 Mar 2021 11:59:50 +0000 Subject: [PATCH 57/62] Added detection testing service results inProcess Deleting Its Process File Path --- ...process_deleting_its_process_file_path.yml | 28 ++++++++++--------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/detections/endpoint/process_deleting_its_process_file_path.yml b/detections/endpoint/process_deleting_its_process_file_path.yml index 12b6194ab8..82643ed65a 100644 --- a/detections/endpoint/process_deleting_its_process_file_path.yml +++ b/detections/endpoint/process_deleting_its_process_file_path.yml @@ -6,16 +6,16 @@ author: Teoderick Contreras type: batch datamodel: - Endpoint -description: This detection is to identify a suspicious process that tries to delete the process file path related to its process. - This technique is known to be defense evasion once a certain condition of malware is satisfied or not. - Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect. -search: '`sysmon` EventCode=1 cmdline = "*/c del*" Image = "*\\cmd.exe" - |eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") - | stats min(_time) as firstTime max(_time) as lastTime count by Computer user ParentImage ParentCommandLine Image cmdline EventCode ProcessID result - | where result = "Found" - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `process_deleting_its_process_file_path_filter`' +description: This detection is to identify a suspicious process that tries to delete + the process file path related to its process. This technique is known to be defense + evasion once a certain condition of malware is satisfied or not. Clop ransomware + use this technique where it will try to delete its process file path using a .bat + command if the keyboard layout is not the layout it tries to infect. +search: '`sysmon` EventCode=1 cmdline = "*/c del*" Image = "*\\cmd.exe" |eval result + = if(like(process,"%".parent_process."%"), "Found", "Not Found") | stats min(_time) + as firstTime max(_time) as lastTime count by Computer user ParentImage ParentCommandLine + Image cmdline EventCode ProcessID result | where result = "Found" | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `process_deleting_its_process_file_path_filter`' how_to_implement: You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. @@ -27,7 +27,7 @@ references: - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html tags: analytic_story: - - Clop Ransomware + - Clop Ransomware kill_chain_phases: - Exploitation mitre_attack_id: @@ -47,5 +47,7 @@ tags: - ProcessID - result - _time - security_domain: endpoint - \ No newline at end of file + security_domain: endpoint + automated_detection_testing: passed + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log From 57e4a62a0156767ad4250a7f2993662bc8fce9a7 Mon Sep 17 00:00:00 2001 From: root Date: Tue, 23 Mar 2021 12:18:35 +0000 Subject: [PATCH 58/62] Added detection testing service results inHigh File Deletion Frequency --- .../endpoint/high_file_deletion_frequency.yml | 32 ++++++++++--------- 1 file changed, 17 insertions(+), 15 deletions(-) diff --git a/detections/endpoint/high_file_deletion_frequency.yml b/detections/endpoint/high_file_deletion_frequency.yml index 5b156371eb..556735275f 100644 --- a/detections/endpoint/high_file_deletion_frequency.yml +++ b/detections/endpoint/high_file_deletion_frequency.yml @@ -6,27 +6,26 @@ author: Teoderick Contreras type: batch datamodel: - Endpoint -description: This search looks for high frequency of file deletion relative to process name and process id. - These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions and sysmon treat the - original files to be deleted as soon it was replace as encrypted data. -search: '`sysmon` EventCode=23 - TargetFilename IN ("*\.cmd", "*\.ini","*\.gif", "*\.jpg", "*\.jpeg", "*\.db", "*\.ps1", "*\.doc*", "*\.xls*", "*\.ppt*", "*\.bmp","*\.zip", "*\.rar", "*\.7z", "*\.chm", "*\.png", "*\.log", "*\.vbs", "*\.js") - | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer user EventCode Image ProcessID - |where count >=100 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `high_file_deletion_frequency_filter`' +description: This search looks for high frequency of file deletion relative to process + name and process id. These events usually happen when the ransomware tries to encrypt + the files with the ransomware file extensions and sysmon treat the original files + to be deleted as soon it was replace as encrypted data. +search: '`sysmon` EventCode=23 TargetFilename IN ("*\.cmd", "*\.ini","*\.gif", "*\.jpg", + "*\.jpeg", "*\.db", "*\.ps1", "*\.doc*", "*\.xls*", "*\.ppt*", "*\.bmp","*\.zip", + "*\.rar", "*\.7z", "*\.chm", "*\.png", "*\.log", "*\.vbs", "*\.js") | stats values(TargetFilename) + as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer + user EventCode Image ProcessID |where count >=100 | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `high_file_deletion_frequency_filter`' how_to_implement: To successfully implement this search, you need to be ingesting - logs with the deleted target file name, process name and process id from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. + logs with the deleted target file name, process name and process id from your endpoints. + If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: user may delete bunch of pictures or files in a folder. references: - https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html tags: analytic_story: - - Clop Ransomware + - Clop Ransomware kill_chain_phases: - Exploitation mitre_attack_id: @@ -43,4 +42,7 @@ tags: - Image - ProcessID - _time - security_domain: endpoint \ No newline at end of file + security_domain: endpoint + automated_detection_testing: passed + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log From 526a5c7893ae7d45157aa01fecbaf1fbd722c467 Mon Sep 17 00:00:00 2001 From: root Date: Tue, 23 Mar 2021 12:41:38 +0000 Subject: [PATCH 59/62] Added detection testing service results inHigh Process Termination Frequency --- .../high_process_termination_frequency.yml | 28 +++++++++++-------- 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/detections/endpoint/high_process_termination_frequency.yml b/detections/endpoint/high_process_termination_frequency.yml index 410a2b0983..b2d5fcd97c 100644 --- a/detections/endpoint/high_process_termination_frequency.yml +++ b/detections/endpoint/high_process_termination_frequency.yml @@ -6,23 +6,24 @@ author: Teoderick Contreras type: batch datamodel: - Endpoint -description: This analytics are designed to indentify a high frequency of process termination on a machine which is a common behavior of ransomware malware before encrypting files. - This technique is designed to avoid an exception error while accessing (docs, images, database and etc..) in the infected machine for encryption. -search: '`sysmon` EventCode=5 - |bin _time span=3s - |stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime count by Computer EventCode ProcessID | where count >= 15 - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `high_process_termination_frequency_filter`' +description: This analytics are designed to indentify a high frequency of process + termination on a machine which is a common behavior of ransomware malware before + encrypting files. This technique is designed to avoid an exception error while accessing + (docs, images, database and etc..) in the infected machine for encryption. +search: '`sysmon` EventCode=5 |bin _time span=3s |stats values(Image) as proc_terminated + min(_time) as firstTime max(_time) as lastTime count by Computer EventCode ProcessID + | where count >= 15 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `high_process_termination_frequency_filter`' how_to_implement: To successfully implement this search, you need to be ingesting - logs with the Image (process full path of terminated process) from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. + logs with the Image (process full path of terminated process) from your endpoints. + If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: UPDATE_KNOWN_FALSE_POSITIVES -references: +references: - https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html tags: analytic_story: - - Clop Ransomware + - Clop Ransomware kill_chain_phases: - Exploitation mitre_attack_id: @@ -37,4 +38,7 @@ tags: - Computer - _time - ProcessID - security_domain: endpoint \ No newline at end of file + security_domain: endpoint + automated_detection_testing: passed + dataset: + - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log From 191e42f58a7f804438c984d3fdd90d1fea0a7057 Mon Sep 17 00:00:00 2001 From: tcontreras Date: Tue, 23 Mar 2021 14:13:33 +0100 Subject: [PATCH 60/62] clop_detection_mod --- detections/endpoint/clop_common_exec_parameter.yml | 7 +++++-- detections/endpoint/resize_shadowstorage_volume.yml | 5 ++++- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/detections/endpoint/clop_common_exec_parameter.yml b/detections/endpoint/clop_common_exec_parameter.yml index d24f1f3797..c8312f7c3e 100644 --- a/detections/endpoint/clop_common_exec_parameter.yml +++ b/detections/endpoint/clop_common_exec_parameter.yml @@ -17,8 +17,11 @@ search: '| tstats `security_content_summariesonly` values(Processes.process) as values(Processes.parent_process_name) as parent_process values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" by Processes.parent_process_name - Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `clop_common_exec_parameter_filter`' + Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `clop_common_exec_parameter_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the diff --git a/detections/endpoint/resize_shadowstorage_volume.yml b/detections/endpoint/resize_shadowstorage_volume.yml index 6a868489c2..a69ec49c5a 100644 --- a/detections/endpoint/resize_shadowstorage_volume.yml +++ b/detections/endpoint/resize_shadowstorage_volume.yml @@ -20,7 +20,10 @@ search: '| tstats `security_content_summariesonly` values(Processes.process) as = "wmic.exe" Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest - Processes.user | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` + Processes.user Processes.process_id Processes.process_guid + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + |`security_content_ctime(lastTime)` | `resize_shadowstorage_volume_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your From 9e240c9530acb6b7ce3b05c1b27a4fc9edc8f76f Mon Sep 17 00:00:00 2001 From: tcontreras Date: Tue, 23 Mar 2021 16:44:40 +0100 Subject: [PATCH 61/62] false_positive_field_mod --- detections/endpoint/high_process_termination_frequency.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detections/endpoint/high_process_termination_frequency.yml b/detections/endpoint/high_process_termination_frequency.yml index b2d5fcd97c..b142ddc128 100644 --- a/detections/endpoint/high_process_termination_frequency.yml +++ b/detections/endpoint/high_process_termination_frequency.yml @@ -17,7 +17,7 @@ search: '`sysmon` EventCode=5 |bin _time span=3s |stats values(Image) as proc_te how_to_implement: To successfully implement this search, you need to be ingesting logs with the Image (process full path of terminated process) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: UPDATE_KNOWN_FALSE_POSITIVES +known_false_positives: admin or user tool that can terminate multiple process. references: - https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html From 48120285ba6a4a3c63c3fb840f863dae4f354a92 Mon Sep 17 00:00:00 2001 From: tcontreras Date: Tue, 23 Mar 2021 17:00:55 +0100 Subject: [PATCH 62/62] clop_lookup_entry --- lookups/ransomware_extensions.csv | 4 +++- lookups/ransomware_notes.csv | 2 ++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/lookups/ransomware_extensions.csv b/lookups/ransomware_extensions.csv index 0f003c3d8f..9e8fe7eff6 100644 --- a/lookups/ransomware_extensions.csv +++ b/lookups/ransomware_extensions.csv @@ -285,4 +285,6 @@ Extensions,Name .wnry,WannaCry .wncryt,WannaCry .WNCRYT,WannaCry -.RYK,Ryuk \ No newline at end of file +.RYK,Ryuk +.Clop,Clop +.Cllp,Clop \ No newline at end of file diff --git a/lookups/ransomware_notes.csv b/lookups/ransomware_notes.csv index ead875e6e9..ecdfd2479d 100644 --- a/lookups/ransomware_notes.csv +++ b/lookups/ransomware_notes.csv @@ -57,3 +57,5 @@ HELP_DECRYPT_YOUR_FILES.HTML,True *-SORRY-FOR-FILES.html,True *-READ-FOR-HELLPP.html,True RyukReadMe.html,True +ClopReadMe.txt,True +README_README.txt,True \ No newline at end of file